--- OA_type: closed access _id: '20157' abstract: - lang: eng text: The focus of much of contemporary research ethics is on compliance with established protocols. However, large data-driven neuroscience research raises new ethical concerns that have no agreed-upon solution. Here we reflect on these challenges and propose better integration of public and patient involvement in this evolving landscape. acknowledgement: Funded provided by the European Union. Complementary funding was received by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (10131373and 10038599) and Ministry of Science and Technology of China (MOST) National Key Project of ‘Inter-governmental International Scientific and Technological Innovation Cooperation’ (2023YFE0199700). article_number: 1128-1130 article_processing_charge: No article_type: comment author: - first_name: Bernd full_name: Stahl, Bernd last_name: Stahl - first_name: George full_name: Ogoh, George last_name: Ogoh - first_name: Gunter full_name: Schumann, Gunter last_name: Schumann - first_name: Henrik full_name: Walter, Henrik last_name: Walter - first_name: Bernd full_name: Stahl, Bernd last_name: Stahl - first_name: Allan H. full_name: Young, Allan H. last_name: Young - first_name: Sylvane full_name: Desrivières, Sylvane last_name: Desrivières - first_name: Nicholas full_name: Clinton, Nicholas last_name: Clinton - first_name: Paul full_name: Thompson, Paul last_name: Thompson - first_name: Ameli full_name: Schwalber, Ameli last_name: Schwalber - first_name: Jingyu full_name: Liu, Jingyu last_name: Liu - first_name: Vince full_name: Calhoun, Vince last_name: Calhoun - first_name: Xiao full_name: Chang, Xiao last_name: Chang - first_name: Yunman full_name: Xia, Yunman last_name: Xia - first_name: Yanting full_name: Gong, Yanting last_name: Gong - first_name: Tianye full_name: Jia, Tianye last_name: Jia - first_name: Paul full_name: Renner, Paul last_name: Renner - first_name: Sören full_name: Hese, Sören last_name: Hese - first_name: Arantxa full_name: Giner, Arantxa last_name: Giner - first_name: Mavi full_name: Sanchez, Mavi last_name: Sanchez - first_name: Elena full_name: Alvarez, Elena last_name: Alvarez - first_name: Bernhard full_name: Spanlang, Bernhard last_name: Spanlang - first_name: Charlie full_name: Pearmund, Charlie last_name: Pearmund - first_name: Anastasios Polykarpos full_name: Athanasiadis, Anastasios Polykarpos last_name: Athanasiadis - first_name: Lisa full_name: Otten, Lisa last_name: Otten - first_name: Séverine full_name: Pitel, Séverine last_name: Pitel - first_name: Spase full_name: Petkoski, Spase last_name: Petkoski - first_name: Viktor full_name: Jirsa, Viktor last_name: Jirsa - first_name: Karen full_name: Schmitt, Karen last_name: Schmitt - first_name: Johannes full_name: Wilbertz, Johannes last_name: Wilbertz - first_name: Myrto full_name: Patraskaki, Myrto last_name: Patraskaki - first_name: Peter full_name: Sommer, Peter last_name: Sommer - first_name: Stefanie full_name: Heilmann-Heimbach, Stefanie last_name: Heilmann-Heimbach - first_name: Carina M. full_name: Mathey, Carina M. last_name: Mathey - first_name: Abigail full_name: Miller, Abigail last_name: Miller - first_name: Isabelle full_name: Claus, Isabelle last_name: Claus - first_name: Markus M. full_name: Nöthen, Markus M. last_name: Nöthen - first_name: Per full_name: Hoffmann, Per last_name: Hoffmann - first_name: Andreas J. full_name: Forstner, Andreas J. last_name: Forstner - first_name: Alvaro full_name: Pastor, Alvaro last_name: Pastor - first_name: Jaime full_name: Gallego, Jaime last_name: Gallego - first_name: Francisco Eiroa full_name: Orosa, Francisco Eiroa last_name: Orosa - first_name: Guillem Feixas full_name: Viapiana, Guillem Feixas last_name: Viapiana - first_name: Mel full_name: Slater, Mel last_name: Slater - first_name: Lena full_name: Marr, Lena id: 4406F586-F248-11E8-B48F-1D18A9856A87 last_name: Marr - first_name: Gaia full_name: Novarino, Gaia id: 3E57A680-F248-11E8-B48F-1D18A9856A87 last_name: Novarino orcid: 0000-0002-7673-7178 - first_name: Andre full_name: Marquand, Andre last_name: Marquand - first_name: Sarah Jane full_name: Böttger, Sarah Jane last_name: Böttger - first_name: Mira full_name: Tschorn, Mira last_name: Tschorn - first_name: Michael full_name: Rapp, Michael last_name: Rapp - first_name: Helga full_name: Ask, Helga last_name: Ask - first_name: Rikka full_name: Kjelkenes, Rikka last_name: Kjelkenes - first_name: Sara full_name: Fernandez, Sara last_name: Fernandez - first_name: Dennis full_name: Van Der Meer, Dennis last_name: Van Der Meer - first_name: Lars T. full_name: Westlye, Lars T. last_name: Westlye - first_name: Ole A. full_name: Andreassen, Ole A. last_name: Andreassen - first_name: Rieke full_name: Aden, Rieke last_name: Aden - first_name: Beke full_name: Seefried, Beke last_name: Seefried - first_name: Sebastian full_name: Siehl, Sebastian last_name: Siehl - first_name: Frauke full_name: Nees, Frauke last_name: Nees - first_name: Maja full_name: Neidhart, Maja last_name: Neidhart - first_name: Argyris full_name: Stringaris, Argyris last_name: Stringaris - first_name: Emanuel full_name: Schwarz, Emanuel last_name: Schwarz - first_name: Nathalie full_name: Holz, Nathalie last_name: Holz - first_name: Heike full_name: Tost, Heike last_name: Tost - first_name: Andreas full_name: Meyer-Lindenberg, Andreas last_name: Meyer-Lindenberg - first_name: Nina full_name: Christmann, Nina last_name: Christmann - first_name: Karina full_name: Jansone, Karina last_name: Jansone - first_name: Tobias full_name: Banaschewski, Tobias last_name: Banaschewski - first_name: Jamie full_name: Banks, Jamie last_name: Banks - first_name: Kerstin full_name: Schepanski, Kerstin last_name: Schepanski - first_name: Tatjana full_name: Schütz, Tatjana last_name: Schütz - first_name: Ulrike Helene full_name: Taron, Ulrike Helene last_name: Taron - first_name: Roland full_name: Eils, Roland last_name: Eils - first_name: Jean Charles full_name: Roy, Jean Charles last_name: Roy - first_name: Tristram A. full_name: Lett, Tristram A. last_name: Lett - first_name: Hedi full_name: Kebir, Hedi last_name: Kebir - first_name: Elli full_name: Polemiti, Elli last_name: Polemiti - first_name: Esther full_name: Hitchen, Esther last_name: Hitchen - first_name: Marcel full_name: Jentsch, Marcel last_name: Jentsch - first_name: Emin full_name: Serin, Emin last_name: Serin - first_name: Antoine full_name: Bernas, Antoine last_name: Bernas - first_name: Nilakshi full_name: Vaidya, Nilakshi last_name: Vaidya - first_name: Sven full_name: Twardziok, Sven last_name: Twardziok - first_name: Markus full_name: Ralser, Markus last_name: Ralser - first_name: Andreas full_name: Heinz, Andreas last_name: Heinz - first_name: Henrik full_name: Walter, Henrik last_name: Walter citation: ama: Stahl B, Ogoh G, Schumann G, et al. Rethinking ethics in interdisciplinary and big data-driven neuroscience projects. Nature Mental Health. 2024;2(10). doi:10.1038/s44220-024-00320-3 apa: Stahl, B., Ogoh, G., Schumann, G., Walter, H., Stahl, B., Young, A. H., … Walter, H. (2024). Rethinking ethics in interdisciplinary and big data-driven neuroscience projects. Nature Mental Health. Springer Nature. https://doi.org/10.1038/s44220-024-00320-3 chicago: Stahl, Bernd, George Ogoh, Gunter Schumann, Henrik Walter, Bernd Stahl, Allan H. Young, Sylvane Desrivières, et al. “Rethinking Ethics in Interdisciplinary and Big Data-Driven Neuroscience Projects.” Nature Mental Health. Springer Nature, 2024. https://doi.org/10.1038/s44220-024-00320-3. ieee: B. Stahl et al., “Rethinking ethics in interdisciplinary and big data-driven neuroscience projects,” Nature Mental Health, vol. 2, no. 10. Springer Nature, 2024. ista: Stahl B, Ogoh G, Schumann G, Walter H, Stahl B, Young AH, Desrivières S, Clinton N, Thompson P, Schwalber A, Liu J, Calhoun V, Chang X, Xia Y, Gong Y, Jia T, Renner P, Hese S, Giner A, Sanchez M, Alvarez E, Spanlang B, Pearmund C, Athanasiadis AP, Otten L, Pitel S, Petkoski S, Jirsa V, Schmitt K, Wilbertz J, Patraskaki M, Sommer P, Heilmann-Heimbach S, Mathey CM, Miller A, Claus I, Nöthen MM, Hoffmann P, Forstner AJ, Pastor A, Gallego J, Orosa FE, Viapiana GF, Slater M, Marr L, Novarino G, Marquand A, Böttger SJ, Tschorn M, Rapp M, Ask H, Kjelkenes R, Fernandez S, Van Der Meer D, Westlye LT, Andreassen OA, Aden R, Seefried B, Siehl S, Nees F, Neidhart M, Stringaris A, Schwarz E, Holz N, Tost H, Meyer-Lindenberg A, Christmann N, Jansone K, Banaschewski T, Banks J, Schepanski K, Schütz T, Taron UH, Eils R, Roy JC, Lett TA, Kebir H, Polemiti E, Hitchen E, Jentsch M, Serin E, Bernas A, Vaidya N, Twardziok S, Ralser M, Heinz A, Walter H. 2024. Rethinking ethics in interdisciplinary and big data-driven neuroscience projects. Nature Mental Health. 2(10), 1128–1130. mla: Stahl, Bernd, et al. “Rethinking Ethics in Interdisciplinary and Big Data-Driven Neuroscience Projects.” Nature Mental Health, vol. 2, no. 10, 1128–1130, Springer Nature, 2024, doi:10.1038/s44220-024-00320-3. short: B. Stahl, G. Ogoh, G. Schumann, H. Walter, B. Stahl, A.H. Young, S. Desrivières, N. Clinton, P. Thompson, A. Schwalber, J. Liu, V. Calhoun, X. Chang, Y. Xia, Y. Gong, T. Jia, P. Renner, S. Hese, A. Giner, M. Sanchez, E. Alvarez, B. Spanlang, C. Pearmund, A.P. Athanasiadis, L. Otten, S. Pitel, S. Petkoski, V. Jirsa, K. Schmitt, J. Wilbertz, M. Patraskaki, P. Sommer, S. Heilmann-Heimbach, C.M. Mathey, A. Miller, I. Claus, M.M. Nöthen, P. Hoffmann, A.J. Forstner, A. Pastor, J. Gallego, F.E. Orosa, G.F. Viapiana, M. Slater, L. Marr, G. Novarino, A. Marquand, S.J. Böttger, M. Tschorn, M. Rapp, H. Ask, R. Kjelkenes, S. Fernandez, D. Van Der Meer, L.T. Westlye, O.A. Andreassen, R. Aden, B. Seefried, S. Siehl, F. Nees, M. Neidhart, A. Stringaris, E. Schwarz, N. Holz, H. Tost, A. Meyer-Lindenberg, N. Christmann, K. Jansone, T. Banaschewski, J. Banks, K. Schepanski, T. Schütz, U.H. Taron, R. Eils, J.C. Roy, T.A. Lett, H. Kebir, E. Polemiti, E. Hitchen, M. Jentsch, E. Serin, A. Bernas, N. Vaidya, S. Twardziok, M. Ralser, A. Heinz, H. Walter, Nature Mental Health 2 (2024). date_created: 2025-08-10T22:01:30Z date_published: 2024-10-01T00:00:00Z date_updated: 2025-08-11T06:53:55Z day: '01' department: - _id: GaNo doi: 10.1038/s44220-024-00320-3 intvolume: ' 2' issue: '10' language: - iso: eng month: '10' oa_version: None publication: Nature Mental Health publication_identifier: eissn: - 2731-6076 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Rethinking ethics in interdisciplinary and big data-driven neuroscience projects type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2 year: '2024' ... --- OA_place: repository OA_type: green _id: '18755' abstract: - lang: eng text: "A universalthresholdizer (UT), constructed from a threshold fully homomorphic encryption by Boneh et. al , Crypto 2018, is a general framework for universally thresholdizing many cryptographic schemes. However, their framework is insufficient to construct strongly secure threshold schemes, such as threshold signatures and threshold public-key encryption, etc.\r\n\r\nIn this paper, we strengthen the security definition for a universal thresholdizer and propose a scheme which satisfies our stronger security notion. Our UT scheme is an improvement of Boneh et. al ’s construction at the level of threshold fully homomorphic encryption using a key homomorphic pseudorandom function. We apply our strongly secure UT scheme to construct strongly secure threshold signatures and threshold public-key encryption." acknowledgement: Ehsan Ebrahimi is supported by the Luxembourg National Research Fund under the Junior CORE project QSP (C22/IS/17272217/QSP/Ebrahimi). article_processing_charge: No author: - first_name: Ehsan full_name: Ebrahimi, Ehsan last_name: Ebrahimi - first_name: Anshu full_name: Yadav, Anshu id: dc8f1524-403e-11ee-bf07-9649ad996e21 last_name: Yadav citation: ama: 'Ebrahimi E, Yadav A. Strongly secure universal thresholdizer. In: 30th International Conference on the Theory and Application of Cryptology and Information Security. Vol 15486. Springer Nature; 2024:207-239. doi:10.1007/978-981-96-0891-1_7' apa: 'Ebrahimi, E., & Yadav, A. (2024). Strongly secure universal thresholdizer. In 30th International Conference on the Theory and Application of Cryptology and Information Security (Vol. 15486, pp. 207–239). Kolkata, India: Springer Nature. https://doi.org/10.1007/978-981-96-0891-1_7' chicago: Ebrahimi, Ehsan, and Anshu Yadav. “Strongly Secure Universal Thresholdizer.” In 30th International Conference on the Theory and Application of Cryptology and Information Security, 15486:207–39. Springer Nature, 2024. https://doi.org/10.1007/978-981-96-0891-1_7. ieee: E. Ebrahimi and A. Yadav, “Strongly secure universal thresholdizer,” in 30th International Conference on the Theory and Application of Cryptology and Information Security, Kolkata, India, 2024, vol. 15486, pp. 207–239. ista: 'Ebrahimi E, Yadav A. 2024. Strongly secure universal thresholdizer. 30th International Conference on the Theory and Application of Cryptology and Information Security. ASIACRYPT: Conference on the Theory and Application of Cryptology and Information Security vol. 15486, 207–239.' mla: Ebrahimi, Ehsan, and Anshu Yadav. “Strongly Secure Universal Thresholdizer.” 30th International Conference on the Theory and Application of Cryptology and Information Security, vol. 15486, Springer Nature, 2024, pp. 207–39, doi:10.1007/978-981-96-0891-1_7. short: E. Ebrahimi, A. Yadav, in:, 30th International Conference on the Theory and Application of Cryptology and Information Security, Springer Nature, 2024, pp. 207–239. conference: end_date: 2024-12-13 location: Kolkata, India name: 'ASIACRYPT: Conference on the Theory and Application of Cryptology and Information Security' start_date: 2024-12-09 date_created: 2025-01-05T23:01:56Z date_published: 2024-12-12T00:00:00Z date_updated: 2025-09-09T12:00:12Z day: '12' department: - _id: KrPi doi: 10.1007/978-981-96-0891-1_7 external_id: isi: - '001443889100007' intvolume: ' 15486' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://eprint.iacr.org/2024/2078 month: '12' oa: 1 oa_version: Preprint page: 207-239 publication: 30th International Conference on the Theory and Application of Cryptology and Information Security publication_identifier: eissn: - 1611-3349 isbn: - '9789819608904' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Strongly secure universal thresholdizer type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 15486 year: '2024' ... --- OA_place: repository OA_type: green _id: '18756' abstract: - lang: eng text: "The evasive LWE assumption, proposed by Wee [Eurocrypt’22 Wee] for constructing a lattice-based optimal broadcast encryption, has shown to be a powerful assumption, adopted by subsequent works to construct advanced primitives ranging from ABE variants to obfuscation for null circuits. However, a closer look reveals significant differences among the precise assumption statements involved in different works, leading to the fundamental question of how these assumptions compare to each other. In this work, we initiate a more systematic study on evasive LWE assumptions:\r\n(i) Based on the standard LWE assumption, we construct simple counterexamples against three private-coin evasive LWE variants, used in [Crypto’22 Tsabary, Asiacrypt’22 VWW, Crypto’23 ARYY] respectively, showing that these assumptions are unlikely to hold.\r\n\r\n(ii) Based on existing evasive LWE variants and our counterexamples, we propose and define three classes of plausible evasive LWE assumptions, suitably capturing all existing variants for which we are not aware of non-obfuscation-based counterexamples.\r\n\r\n(iii) We show that under our assumption formulations, the security proofs of [Asiacrypt’22 VWW] and [Crypto’23 ARYY] can be recovered, and we reason why the security proof of [Crypto’22 Tsabary] is also plausibly repairable using an appropriate evasive LWE assumption." acknowledgement: The authors thank the anonymous reviewers for insightful comments which very much improved this work, in particular, sharing with us the counterexamples against a prior version of Hiding Evasive LWE, and against public-coin Evasive LWE when the sampler inputs B. Chris Brzuska and Ivy K. Y. Woo are supported by Research Council of Finland grant 358950. We thank Russell W. F. Lai and Hoeteck Wee for helpful discussions. alternative_title: - LNCS article_processing_charge: No author: - first_name: Chris full_name: Brzuska, Chris last_name: Brzuska - first_name: Akin full_name: Ünal, Akin id: f6b56fb6-dc63-11ee-9dbf-f6780863a85a last_name: Ünal orcid: 0000-0002-8929-0221 - first_name: Ivy K.Y. full_name: Woo, Ivy K.Y. last_name: Woo citation: ama: 'Brzuska C, Ünal A, Woo IKY. Evasive LWE assumptions: Definitions, classes, and counterexamples. In: 30th International Conference on the Theory and Application of Cryptology and Information Security. Vol 15487. Springer Nature; 2024:418-449. doi:10.1007/978-981-96-0894-2_14' apa: 'Brzuska, C., Ünal, A., & Woo, I. K. Y. (2024). Evasive LWE assumptions: Definitions, classes, and counterexamples. In 30th International Conference on the Theory and Application of Cryptology and Information Security (Vol. 15487, pp. 418–449). Kolkata, India: Springer Nature. https://doi.org/10.1007/978-981-96-0894-2_14' chicago: 'Brzuska, Chris, Akin Ünal, and Ivy K.Y. Woo. “Evasive LWE Assumptions: Definitions, Classes, and Counterexamples.” In 30th International Conference on the Theory and Application of Cryptology and Information Security, 15487:418–49. Springer Nature, 2024. https://doi.org/10.1007/978-981-96-0894-2_14.' ieee: 'C. Brzuska, A. Ünal, and I. K. Y. Woo, “Evasive LWE assumptions: Definitions, classes, and counterexamples,” in 30th International Conference on the Theory and Application of Cryptology and Information Security, Kolkata, India, 2024, vol. 15487, pp. 418–449.' ista: 'Brzuska C, Ünal A, Woo IKY. 2024. Evasive LWE assumptions: Definitions, classes, and counterexamples. 30th International Conference on the Theory and Application of Cryptology and Information Security. ASIACRYPT: Conference on the Theory and Application of Cryptology and Information Security, LNCS, vol. 15487, 418–449.' mla: 'Brzuska, Chris, et al. “Evasive LWE Assumptions: Definitions, Classes, and Counterexamples.” 30th International Conference on the Theory and Application of Cryptology and Information Security, vol. 15487, Springer Nature, 2024, pp. 418–49, doi:10.1007/978-981-96-0894-2_14.' short: C. Brzuska, A. Ünal, I.K.Y. Woo, in:, 30th International Conference on the Theory and Application of Cryptology and Information Security, Springer Nature, 2024, pp. 418–449. conference: end_date: 2024-12-13 location: Kolkata, India name: 'ASIACRYPT: Conference on the Theory and Application of Cryptology and Information Security' start_date: 2024-12-09 date_created: 2025-01-05T23:01:56Z date_published: 2024-12-13T00:00:00Z date_updated: 2025-09-09T12:00:51Z day: '13' department: - _id: KrPi doi: 10.1007/978-981-96-0894-2_14 external_id: isi: - '001443890800014' intvolume: ' 15487' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://eprint.iacr.org/2024/2000 month: '12' oa: 1 oa_version: Preprint page: 418-449 publication: 30th International Conference on the Theory and Application of Cryptology and Information Security publication_identifier: eissn: - 1611-3349 isbn: - '9789819608935' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: 'Evasive LWE assumptions: Definitions, classes, and counterexamples' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 15487 year: '2024' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '18757' abstract: - lang: eng text: 'Segmentation is a critical data processing step in many applications of cryo-electron tomography. Downstream analyses, such as subtomogram averaging, are often based on segmentation results, and are thus critically dependent on the availability of open-source software for accurate as well as high-throughput tomogram segmentation. There is a need for more user-friendly, flexible, and comprehensive segmentation software that offers an insightful overview of all steps involved in preparing automated segmentations. Here, we present Ais: a dedicated tomogram segmentation package that is geared towards both high performance and accessibility, available on GitHub. In this report, we demonstrate two common processing steps that can be greatly accelerated with Ais: particle picking for subtomogram averaging, and generating many-feature segmentations of cellular architecture based on in situ tomography data. Featuring comprehensive annotation, segmentation, and rendering functionality, as well as an open repository for trained models at aiscryoet.org, we hope that Ais will help accelerate research and dissemination of data involving cryoET.' acknowledgement: 'We thank A Koster and M Barcena for helpful discussions and kindly sharing the coronaviral replication organelle datasets. We are also grateful to van den Hoek et al., 2022 and Wu et al., 2023, for uploading the data that we used for Figure 5 onto EMPIAR and EMDB, as well as to the authors of various other datasets uploaded to these databases that are not discussed in this manuscript but that were useful for testing the software. We also thank the reviewers, whose comments were very helpful in improving the manuscript and the software. Finally, we are grateful the early Ais users who provided us with feedback on the software and reported issues. This research was supported by the following grants to THS: European Research Council H202 Grant 759517; European Union’s Horizon Europe Program IMAGINE grant 101094250, and the Netherlands Organization for Scientific Research Grant VI.Vidi.193.014.' article_number: '98552' article_processing_charge: Yes article_type: original author: - first_name: Mart G.F. full_name: Last, Mart G.F. last_name: Last - first_name: Leoni full_name: Abendstein, Leoni id: 14f1f051-cd9d-11ef-9c94-8b942a882560 last_name: Abendstein orcid: 0000-0001-7634-5353 - first_name: Lenard M. full_name: Voortman, Lenard M. last_name: Voortman - first_name: Thomas H. full_name: Sharp, Thomas H. last_name: Sharp citation: ama: Last MGF, Abendstein L, Voortman LM, Sharp TH. Streamlining segmentation of cryo-electron tomography datasets with Ais. eLife. 2024;13. doi:10.7554/eLife.98552 apa: Last, M. G. F., Abendstein, L., Voortman, L. M., & Sharp, T. H. (2024). Streamlining segmentation of cryo-electron tomography datasets with Ais. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.98552 chicago: Last, Mart G.F., Leoni Abendstein, Lenard M. Voortman, and Thomas H. Sharp. “Streamlining Segmentation of Cryo-Electron Tomography Datasets with Ais.” ELife. eLife Sciences Publications, 2024. https://doi.org/10.7554/eLife.98552. ieee: M. G. F. Last, L. Abendstein, L. M. Voortman, and T. H. Sharp, “Streamlining segmentation of cryo-electron tomography datasets with Ais,” eLife, vol. 13. eLife Sciences Publications, 2024. ista: Last MGF, Abendstein L, Voortman LM, Sharp TH. 2024. Streamlining segmentation of cryo-electron tomography datasets with Ais. eLife. 13, 98552. mla: Last, Mart G. F., et al. “Streamlining Segmentation of Cryo-Electron Tomography Datasets with Ais.” ELife, vol. 13, 98552, eLife Sciences Publications, 2024, doi:10.7554/eLife.98552. short: M.G.F. Last, L. Abendstein, L.M. Voortman, T.H. Sharp, ELife 13 (2024). date_created: 2025-01-05T23:01:57Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-01-08T08:52:51Z day: '20' ddc: - '570' department: - _id: FlPr doi: 10.7554/eLife.98552 external_id: pmid: - '39704648' file: - access_level: open_access checksum: a4f0f906e4d5c1078208b317e78699d1 content_type: application/pdf creator: dernst date_created: 2025-01-08T08:51:45Z date_updated: 2025-01-08T08:51:45Z file_id: '18774' file_name: 2024_eLife_Last.pdf file_size: 7445664 relation: main_file success: 1 file_date_updated: 2025-01-08T08:51:45Z has_accepted_license: '1' intvolume: ' 13' language: - iso: eng month: '12' oa: 1 oa_version: Published Version pmid: 1 publication: eLife publication_identifier: eissn: - 2050-084X publication_status: published publisher: eLife Sciences Publications quality_controlled: '1' scopus_import: '1' status: public title: Streamlining segmentation of cryo-electron tomography datasets with Ais 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: 13 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18758' abstract: - lang: eng text: 'MaxCut is a classical NP-complete problem and a crucial building block in many combinatorial algorithms. The famous Edwards-Erdős bound states that any connected graph on n vertices with m edges contains a cut of size at least m/2+(n-1)/4. Crowston, Jones and Mnich [Algorithmica, 2015] showed that the MaxCut problem on simple connected graphs admits an FPT algorithm, where the parameter k is the difference between the desired cut size c and the lower bound given by the Edwards-Erdős bound. This was later improved by Etscheid and Mnich [Algorithmica, 2017] to run in parameterized linear time, i.e., f(k)⋅ O(m). We improve upon this result in two ways: Firstly, we extend the algorithm to work also for multigraphs (alternatively, graphs with positive integer weights). Secondly, we change the parameter; instead of the difference to the Edwards-Erdős bound, we use the difference to the Poljak-Turzík bound. The Poljak-Turzík bound states that any weighted graph G has a cut of size at least (w(G))/2+(w_MSF(G))/4, where w(G) denotes the total weight of G, and w_MSF(G) denotes the weight of its minimum spanning forest. In connected simple graphs the two bounds are equivalent, but for multigraphs the Poljak-Turzík bound can be larger and thus yield a smaller parameter k. Our algorithm also runs in parameterized linear time, i.e., f(k)⋅ O(m+n).' acknowledgement: "Kalina Petrova: Swiss National Science Foundation, grant no. CRSII5 173721. This project\r\nhas received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034413.\r\nSimon Weber: Swiss National Science Foundation under project no. 204320" alternative_title: - LIPIcs article_number: '2' article_processing_charge: Yes arxiv: 1 author: - first_name: Jonas full_name: Lill, Jonas last_name: Lill - first_name: Kalina H full_name: Petrova, Kalina H id: 554ff4e4-f325-11ee-b0c4-a10dbd523381 last_name: Petrova - first_name: Simon full_name: Weber, Simon last_name: Weber citation: ama: 'Lill J, Petrova KH, Weber S. Linear-time MaxCut in multigraphs parameterized above the Poljak-Turzík bound. In: 19th International Symposium on Parameterized and Exact Computation. Vol 321. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2024. doi:10.4230/LIPIcs.IPEC.2024.2' apa: 'Lill, J., Petrova, K. H., & Weber, S. (2024). Linear-time MaxCut in multigraphs parameterized above the Poljak-Turzík bound. In 19th International Symposium on Parameterized and Exact Computation (Vol. 321). Egham, United Kingdom: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.IPEC.2024.2' chicago: Lill, Jonas, Kalina H Petrova, and Simon Weber. “Linear-Time MaxCut in Multigraphs Parameterized above the Poljak-Turzík Bound.” In 19th International Symposium on Parameterized and Exact Computation, Vol. 321. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2024. https://doi.org/10.4230/LIPIcs.IPEC.2024.2. ieee: J. Lill, K. H. Petrova, and S. Weber, “Linear-time MaxCut in multigraphs parameterized above the Poljak-Turzík bound,” in 19th International Symposium on Parameterized and Exact Computation, Egham, United Kingdom, 2024, vol. 321. ista: 'Lill J, Petrova KH, Weber S. 2024. Linear-time MaxCut in multigraphs parameterized above the Poljak-Turzík bound. 19th International Symposium on Parameterized and Exact Computation. IPEC: Symposium on Parameterized and Exact Computation, LIPIcs, vol. 321, 2.' mla: Lill, Jonas, et al. “Linear-Time MaxCut in Multigraphs Parameterized above the Poljak-Turzík Bound.” 19th International Symposium on Parameterized and Exact Computation, vol. 321, 2, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2024, doi:10.4230/LIPIcs.IPEC.2024.2. short: J. Lill, K.H. Petrova, S. Weber, in:, 19th International Symposium on Parameterized and Exact Computation, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2024. conference: end_date: 2024-09-06 location: Egham, United Kingdom name: 'IPEC: Symposium on Parameterized and Exact Computation' start_date: 2024-09-04 corr_author: '1' date_created: 2025-01-05T23:01:57Z date_published: 2024-12-05T00:00:00Z date_updated: 2026-01-05T13:46:07Z day: '05' ddc: - '500' department: - _id: MaKw doi: 10.4230/LIPIcs.IPEC.2024.2 ec_funded: 1 external_id: arxiv: - '2407.01071' isi: - '001534851900002' file: - access_level: open_access checksum: a64b9a0e41f7b867d25cb155825ccd53 content_type: application/pdf creator: dernst date_created: 2025-01-08T09:14:59Z date_updated: 2025-01-08T09:14:59Z file_id: '18775' file_name: 2024_LIPIcs_Lill.pdf file_size: 927326 relation: main_file success: 1 file_date_updated: 2025-01-08T09:14:59Z has_accepted_license: '1' intvolume: ' 321' isi: 1 language: - iso: eng month: '12' oa: 1 oa_version: Published Version project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: 19th International Symposium on Parameterized and Exact Computation publication_identifier: isbn: - '9783959773539' issn: - 1868-8969 publication_status: published publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik quality_controlled: '1' related_material: record: - id: '19603' relation: later_version status: public scopus_import: '1' status: public title: Linear-time MaxCut in multigraphs parameterized above the Poljak-Turzík bound tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 321 year: '2024' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '18760' abstract: - lang: eng text: With the remarkable sensitivity and resolution of JWST in the infrared, measuring rest-optical kinematics of galaxies at z > 5 has become possible for the first time. This study pilots a new method for measuring galaxy dynamics for highly multiplexed, unbiased samples by combining FRESCO NIRCam grism spectroscopy and JADES medium-band imaging. Here we present one of the first JWST kinematic measurements for a galaxy at z > 5. We find a significant velocity gradient, which, if interpreted as rotation, yields Vrot = 305 ± 70 km s−1, and we hence refer to this galaxy as Twister-z5. With a rest-frame optical effective radius of re = 2.25 kpc, the high rotation velocity in this galaxy is not due to a compact size, as may be expected in the early Universe, but rather to a high total mass, (math formula). This is a factor of roughly 10× higher than the stellar mass within re. We also observe that the radial Hα equivalent width profile and the specific star formation rate map from resolved stellar population modeling are centrally depressed by a factor of ∼1.5 from the center to re. Combined with the morphology of the line-emitting gas in comparison to the continuum, this centrally suppressed star formation is consistent with a star-forming disk surrounding a bulge growing inside out. While large, rapidly rotating disks are common to z ∼ 2, the existence of one after only 1 Gyr of cosmic time, shown for the first time in ionized gas, adds to the growing evidence that some galaxies matured earlier than expected in the history of the Universe. acknowledgement: We thank the reviewer and editorial staff for their excellent feedback and effort—the manuscript is much stronger as a result. Support for this work was provided by NASA through grant JWST-GO-01895 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. H.Ü. gratefully acknowledges support by the Isaac Newton Trust and by the Kavli Foundation through a Newton-Kavli Junior Fellowship. This work has received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract No. MB22.00072, as well as from the Swiss National Science Foundation (SNSF) through project grant 200020_207349. The Cosmic Dawn Center (DAWN) is funded by the Danish National Research Foundation under grant No. 140. R.S. acknowledges an STFC Ernest Rutherford Fellowship (ST/S004831/1). R.P.N. acknowledges support for this work provided by NASA through the NASA Hubble Fellowship grant HST-HF2-51515.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. M.V.M. acknowledges support from the National Science Foundation via AAG grant 2205519 and the Wisconsin Alumni Research Foundation via grant MSN251397. R.M. also acknowledges funding from a research professorship from the Royal Society. A.J.B., A.J.C., and G.C.J. acknowledge funding from the "FirstGalaxies" Advanced Grant from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 789056). I.L. acknowledges support by the Australian Research Council through Future Fellowship FT220100798. D.J.E. is supported as a Simons Investigator and by a JWST/NIRCam contract to the University of Arizona, NAS5-02015. R.M., J.W., L.S., and W.B. acknowledge support by the Science and Technology Facilities Council (STFC), the ERC through advanced grant 695671 "QUENCH," and the UKRI Frontier Research grant RISEandFALL. B.E.R. acknowledges support from the NIRCam Science Team contract to the University of Arizona, NAS5-02015. The research of C.C.W. is supported by NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The HST and JWST image mosaics of the FRESCO fields are released at MAST as a High Level Science Product (P. Oesch & D. Magee 2023). article_number: L27 article_processing_charge: Yes article_type: letter_note arxiv: 1 author: - first_name: Erica full_name: Nelson, Erica last_name: Nelson - first_name: Gabriel full_name: Brammer, Gabriel last_name: Brammer - first_name: Clara full_name: Giménez-Arteaga, Clara last_name: Giménez-Arteaga - first_name: Pascal A. full_name: Oesch, Pascal A. last_name: Oesch - first_name: Rohan P. full_name: Naidu, Rohan P. last_name: Naidu - first_name: Hannah full_name: Übler, Hannah last_name: Übler - first_name: Jasleen full_name: Matharu, Jasleen last_name: Matharu - first_name: Alice E. full_name: Shapley, Alice E. last_name: Shapley - first_name: Katherine E. full_name: Whitaker, Katherine E. last_name: Whitaker - first_name: Emily full_name: Wisnioski, Emily last_name: Wisnioski - first_name: Natascha M. full_name: Förster Schreiber, Natascha M. last_name: Förster Schreiber - first_name: Renske full_name: Smit, Renske last_name: Smit - first_name: Pieter full_name: Van Dokkum, Pieter last_name: Van Dokkum - first_name: John full_name: Chisholm, John last_name: Chisholm - first_name: Ryan full_name: Endsley, Ryan last_name: Endsley - first_name: Abigail I. full_name: Hartley, Abigail I. last_name: Hartley - first_name: Justus full_name: Gibson, Justus last_name: Gibson - first_name: Emma full_name: Giovinazzo, Emma last_name: Giovinazzo - first_name: Garth full_name: Illingworth, Garth last_name: Illingworth - first_name: Ivo full_name: Labbe, Ivo last_name: Labbe - first_name: Michael V. full_name: Maseda, Michael V. last_name: Maseda - first_name: Jorryt J full_name: Matthee, Jorryt J id: 7439a258-f3c0-11ec-9501-9df22fe06720 last_name: Matthee orcid: 0000-0003-2871-127X - first_name: Alba full_name: Covelo Paz, Alba last_name: Covelo Paz - first_name: Sedona H. full_name: Price, Sedona H. last_name: Price - first_name: Naveen A. full_name: Reddy, Naveen A. last_name: Reddy - first_name: Irene full_name: Shivaei, Irene last_name: Shivaei - first_name: Andrea full_name: Weibel, Andrea last_name: Weibel - first_name: Stijn full_name: Wuyts, Stijn last_name: Wuyts - first_name: Mengyuan full_name: Xiao, Mengyuan last_name: Xiao - first_name: Stacey full_name: Alberts, Stacey last_name: Alberts - first_name: William M. full_name: Baker, William M. last_name: Baker - first_name: Andrew J. full_name: Bunker, Andrew J. last_name: Bunker - first_name: Alex J. full_name: Cameron, Alex J. last_name: Cameron - first_name: Stephane full_name: Charlot, Stephane last_name: Charlot - first_name: Daniel J. full_name: Eisenstein, Daniel J. last_name: Eisenstein - first_name: Anna full_name: De Graaff, Anna last_name: De Graaff - first_name: Zhiyuan full_name: Ji, Zhiyuan last_name: Ji - first_name: Benjamin D. full_name: Johnson, Benjamin D. last_name: Johnson - first_name: Gareth C. full_name: Jones, Gareth C. last_name: Jones - first_name: Roberto full_name: Maiolino, Roberto last_name: Maiolino - first_name: Brant full_name: Robertson, Brant last_name: Robertson - first_name: Lester full_name: Sandles, Lester last_name: Sandles - first_name: Katherine A. full_name: Suess, Katherine A. last_name: Suess - first_name: Sandro full_name: Tacchella, Sandro last_name: Tacchella - first_name: Christina C. full_name: Williams, Christina C. last_name: Williams - first_name: Joris full_name: Witstok, Joris last_name: Witstok citation: ama: 'Nelson E, Brammer G, Giménez-Arteaga C, et al. Ionized gas kinematics with FRESCO: An extended, massive, rapidly rotating galaxy at z = 5.4. Astrophysical Journal Letters. 2024;976(2). doi:10.3847/2041-8213/ad7b17' apa: 'Nelson, E., Brammer, G., Giménez-Arteaga, C., Oesch, P. A., Naidu, R. P., Übler, H., … Witstok, J. (2024). Ionized gas kinematics with FRESCO: An extended, massive, rapidly rotating galaxy at z = 5.4. Astrophysical Journal Letters. IOP Publishing. https://doi.org/10.3847/2041-8213/ad7b17' chicago: 'Nelson, Erica, Gabriel Brammer, Clara Giménez-Arteaga, Pascal A. Oesch, Rohan P. Naidu, Hannah Übler, Jasleen Matharu, et al. “Ionized Gas Kinematics with FRESCO: An Extended, Massive, Rapidly Rotating Galaxy at z = 5.4.” Astrophysical Journal Letters. IOP Publishing, 2024. https://doi.org/10.3847/2041-8213/ad7b17.' ieee: 'E. Nelson et al., “Ionized gas kinematics with FRESCO: An extended, massive, rapidly rotating galaxy at z = 5.4,” Astrophysical Journal Letters, vol. 976, no. 2. IOP Publishing, 2024.' ista: 'Nelson E, Brammer G, Giménez-Arteaga C, Oesch PA, Naidu RP, Übler H, Matharu J, Shapley AE, Whitaker KE, Wisnioski E, Förster Schreiber NM, Smit R, Van Dokkum P, Chisholm J, Endsley R, Hartley AI, Gibson J, Giovinazzo E, Illingworth G, Labbe I, Maseda MV, Matthee JJ, Covelo Paz A, Price SH, Reddy NA, Shivaei I, Weibel A, Wuyts S, Xiao M, Alberts S, Baker WM, Bunker AJ, Cameron AJ, Charlot S, Eisenstein DJ, De Graaff A, Ji Z, Johnson BD, Jones GC, Maiolino R, Robertson B, Sandles L, Suess KA, Tacchella S, Williams CC, Witstok J. 2024. Ionized gas kinematics with FRESCO: An extended, massive, rapidly rotating galaxy at z = 5.4. Astrophysical Journal Letters. 976(2), L27.' mla: 'Nelson, Erica, et al. “Ionized Gas Kinematics with FRESCO: An Extended, Massive, Rapidly Rotating Galaxy at z = 5.4.” Astrophysical Journal Letters, vol. 976, no. 2, L27, IOP Publishing, 2024, doi:10.3847/2041-8213/ad7b17.' short: E. Nelson, G. Brammer, C. Giménez-Arteaga, P.A. Oesch, R.P. Naidu, H. Übler, J. Matharu, A.E. Shapley, K.E. Whitaker, E. Wisnioski, N.M. Förster Schreiber, R. Smit, P. Van Dokkum, J. Chisholm, R. Endsley, A.I. Hartley, J. Gibson, E. Giovinazzo, G. Illingworth, I. Labbe, M.V. Maseda, J.J. Matthee, A. Covelo Paz, S.H. Price, N.A. Reddy, I. Shivaei, A. Weibel, S. Wuyts, M. Xiao, S. Alberts, W.M. Baker, A.J. Bunker, A.J. Cameron, S. Charlot, D.J. Eisenstein, A. De Graaff, Z. Ji, B.D. Johnson, G.C. Jones, R. Maiolino, B. Robertson, L. Sandles, K.A. Suess, S. Tacchella, C.C. Williams, J. Witstok, Astrophysical Journal Letters 976 (2024). date_created: 2025-01-05T23:01:58Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-09-09T11:58:02Z day: '01' ddc: - '520' department: - _id: JoMa doi: 10.3847/2041-8213/ad7b17 external_id: arxiv: - '2310.06887' isi: - '001364636000001' file: - access_level: open_access checksum: 5c7320196586b4340e55f215d8737185 content_type: application/pdf creator: dernst date_created: 2025-01-08T08:18:39Z date_updated: 2025-01-08T08:18:39Z file_id: '18771' file_name: 2024_AstrophysicalJour_Nelson.pdf file_size: 1822989 relation: main_file success: 1 file_date_updated: 2025-01-08T08:18:39Z has_accepted_license: '1' intvolume: ' 976' isi: 1 issue: '2' language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: Astrophysical Journal Letters publication_identifier: eissn: - 2041-8213 issn: - 2041-8205 publication_status: published publisher: IOP Publishing quality_controlled: '1' scopus_import: '1' status: public title: 'Ionized gas kinematics with FRESCO: An extended, massive, rapidly rotating galaxy at z = 5.4' 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: 976 year: '2024' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '18761' abstract: - lang: eng text: Termites, together with cockroaches, belong to the Blattodea. They possess an XX/XY sex determination system which has evolved from an XX/X0 system present in other Blattodean species, such as cockroaches and wood roaches. Little is currently known about the sex chromosomes of termites, their gene content, or their evolution. We here investigate the X chromosome of multiple termite species and compare them with the X chromosome of cockroaches using genomic and transcriptomic data. We find that the X chromosome of the termite Macrotermes natalensis is large and differentiated showing hall marks of sex chromosome evolution such as dosage compensation, while this does not seem to be the case in the other two termite species investigated here where sex chromosomes may be evolutionary younger. Furthermore, the X chromosome in M. natalensis is different from the X chromosome found in the cockroach Blattella germanica indicating that sex chromosome turn-over events may have happened during termite evolution. acknowledged_ssus: - _id: ScienComp acknowledgement: "urthermore, we thank all lab members and collaborators for feedback on the project. Specifically, Dino McMahon provided R. flavipes males and females, Judith Korb provided C. secundus males and females, gave feedback on the project and discussed questions on termite reproduction, Mireille Vasseur-Cognet provided M. natalensis males and females, Ariana Macon performed the lab work for sequencing and the Vicoso group gave critical feedback on the project. We furthermore thank the HPC group at IST Austria and Christian Meesters at JGU Mainz for their technical support.\r\nThis work was supported by a Österreichischer Wissenschaftsfonds (FWF) grant of the Meitner Programme to A.K.H. (project number M 2484), funding by the Deutsche Forschungsgemeinschaft (DFG) of the Research Training Group GenEvo (project number 407023052) to A.K.H., R.F., and A.D., and funding of the DFG within the Schwerpunktprogramm Gevol to A.K.H. and R.M. (project number 503256468)." article_number: evae265 article_processing_charge: Yes article_type: original author: - first_name: Roxanne full_name: Fraser, Roxanne last_name: Fraser - first_name: Ruth full_name: Moraa, Ruth last_name: Moraa - first_name: Annika full_name: Djolai, Annika last_name: Djolai - first_name: Nils full_name: Meisenheimer, Nils last_name: Meisenheimer - first_name: Sophie full_name: Laube, Sophie last_name: Laube - first_name: Beatriz full_name: Vicoso, Beatriz id: 49E1C5C6-F248-11E8-B48F-1D18A9856A87 last_name: Vicoso orcid: 0000-0002-4579-8306 - first_name: Ann K full_name: Huylmans, Ann K id: 4C0A3874-F248-11E8-B48F-1D18A9856A87 last_name: Huylmans orcid: 0000-0001-8871-4961 citation: ama: Fraser R, Moraa R, Djolai A, et al. Evidence for a novel X chromosome in termites. Genome Biology and Evolution. 2024;16(12). doi:10.1093/gbe/evae265 apa: Fraser, R., Moraa, R., Djolai, A., Meisenheimer, N., Laube, S., Vicoso, B., & Huylmans, A. K. (2024). Evidence for a novel X chromosome in termites. Genome Biology and Evolution. Oxford University Press. https://doi.org/10.1093/gbe/evae265 chicago: Fraser, Roxanne, Ruth Moraa, Annika Djolai, Nils Meisenheimer, Sophie Laube, Beatriz Vicoso, and Ann K Huylmans. “Evidence for a Novel X Chromosome in Termites.” Genome Biology and Evolution. Oxford University Press, 2024. https://doi.org/10.1093/gbe/evae265. ieee: R. Fraser et al., “Evidence for a novel X chromosome in termites,” Genome Biology and Evolution, vol. 16, no. 12. Oxford University Press, 2024. ista: Fraser R, Moraa R, Djolai A, Meisenheimer N, Laube S, Vicoso B, Huylmans AK. 2024. Evidence for a novel X chromosome in termites. Genome Biology and Evolution. 16(12), evae265. mla: Fraser, Roxanne, et al. “Evidence for a Novel X Chromosome in Termites.” Genome Biology and Evolution, vol. 16, no. 12, evae265, Oxford University Press, 2024, doi:10.1093/gbe/evae265. short: R. Fraser, R. Moraa, A. Djolai, N. Meisenheimer, S. Laube, B. Vicoso, A.K. Huylmans, Genome Biology and Evolution 16 (2024). corr_author: '1' date_created: 2025-01-05T23:01:58Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-09-09T11:58:41Z day: '01' ddc: - '570' department: - _id: BeVi doi: 10.1093/gbe/evae265 external_id: isi: - '001380841100001' pmid: - '39658246' file: - access_level: open_access checksum: 9cf8fd14580dd694dd810ccca808ad0e content_type: application/pdf creator: dernst date_created: 2025-01-08T08:28:07Z date_updated: 2025-01-08T08:28:07Z file_id: '18772' file_name: 2024_GBE_Fraser.pdf file_size: 795106 relation: main_file success: 1 file_date_updated: 2025-01-08T08:28:07Z has_accepted_license: '1' intvolume: ' 16' isi: 1 issue: '12' language: - iso: eng month: '12' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 26641CAC-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: M02484 name: Sex Determination in Termites publication: Genome Biology and Evolution publication_identifier: eissn: - 1759-6653 publication_status: published publisher: Oxford University Press quality_controlled: '1' scopus_import: '1' status: public title: Evidence for a novel X chromosome in termites tmp: image: /images/cc_by_nc.png legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) short: CC BY-NC (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 16 year: '2024' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '18762' abstract: - lang: eng text: Consider the random variable $\mathrm{Tr}( f_1(W)A_1\dots f_k(W)A_k)$ where $W$ is an $N\times N$ Hermitian Wigner matrix, $k\in\mathbb{N}$, and choose (possibly $N$-dependent) regular functions $f_1,\dots, f_k$ as well as bounded deterministic matrices $A_1,\dots,A_k$. We give a functional central limit theorem showing that the fluctuations around the expectation are Gaussian. Moreover, we determine the limiting covariance structure and give explicit error bounds in terms of the scaling of $f_1,\dots,f_k$ and the number of traceless matrices among $A_1,\dots,A_k$, thus extending the results of [Cipolloni, Erdős, Schröder 2023] to products of arbitrary length $k\geq2$. As an application, we consider the fluctuation of $\mathrm{Tr}(\mathrm{e}^{\mathrm{i} tW}A_1\mathrm{e}^{-\mathrm{i} tW}A_2)$ around its thermal value $\mathrm{Tr}(A_1)\mathrm{Tr}(A_2)$ when $t$ is large and give an explicit formula for the variance. acknowledgement: "I am very grateful to László Erdős for suggesting the topic and many valuable discussions during my work on the project. I would also like to thank the two anonymous referees for their careful reading of the manuscript and detailed feedback.\r\nPartially supported by ERC Advanced Grants “RMTBeyond” No. 101020331 and “LDRaM” No. 884584." article_number: '191' article_processing_charge: Yes article_type: original arxiv: 1 author: - first_name: Jana full_name: Reker, Jana id: e796e4f9-dc8d-11ea-abe3-97e26a0323e9 last_name: Reker citation: ama: Reker J. Multi-point functional central limit theorem for Wigner matrices. Electronic Journal of Probability. 2024;29. doi:10.1214/24-EJP1247 apa: Reker, J. (2024). Multi-point functional central limit theorem for Wigner matrices. Electronic Journal of Probability. Institute of Mathematical Statistics. https://doi.org/10.1214/24-EJP1247 chicago: Reker, Jana. “Multi-Point Functional Central Limit Theorem for Wigner Matrices.” Electronic Journal of Probability. Institute of Mathematical Statistics, 2024. https://doi.org/10.1214/24-EJP1247. ieee: J. Reker, “Multi-point functional central limit theorem for Wigner matrices,” Electronic Journal of Probability, vol. 29. Institute of Mathematical Statistics, 2024. ista: Reker J. 2024. Multi-point functional central limit theorem for Wigner matrices. Electronic Journal of Probability. 29, 191. mla: Reker, Jana. “Multi-Point Functional Central Limit Theorem for Wigner Matrices.” Electronic Journal of Probability, vol. 29, 191, Institute of Mathematical Statistics, 2024, doi:10.1214/24-EJP1247. short: J. Reker, Electronic Journal of Probability 29 (2024). corr_author: '1' date_created: 2025-01-05T23:01:58Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-09-09T11:59:15Z day: '20' ddc: - '510' department: - _id: LaEr doi: 10.1214/24-EJP1247 ec_funded: 1 external_id: arxiv: - '2307.11028' isi: - '001381599200001' file: - access_level: open_access checksum: 67178feaa8630a332599d3037a3fe70e content_type: application/pdf creator: dernst date_created: 2025-01-08T08:44:03Z date_updated: 2025-01-08T08:44:03Z file_id: '18773' file_name: 2024_ElectrJournProbability_Reker.pdf file_size: 812428 relation: main_file success: 1 file_date_updated: 2025-01-08T08:44:03Z has_accepted_license: '1' intvolume: ' 29' isi: 1 language: - iso: eng month: '12' oa: 1 oa_version: Published Version project: - _id: 62796744-2b32-11ec-9570-940b20777f1d call_identifier: H2020 grant_number: '101020331' name: Random matrices beyond Wigner-Dyson-Mehta publication: Electronic Journal of Probability publication_identifier: eissn: - 1083-6489 publication_status: published publisher: Institute of Mathematical Statistics quality_controlled: '1' related_material: record: - id: '17173' relation: earlier_version status: public scopus_import: '1' status: public title: Multi-point functional central limit theorem for Wigner matrices tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 29 year: '2024' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '18779' abstract: - lang: eng text: Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background, and noise in any type of image. The method, originally described for geoinformatics, does not require a predefined intensity threshold or teaching algorithm for image segmentation and allows quantitative comparison of samples obtained in different conditions. It utilizes relative intensity as well as spatial information of neighboring elements to select spatially contiguous groups of pixels. We demonstrate that Moran’s method outperforms threshold-based method in both artificially generated as well as in natural images especially when background noise is substantial. This superior performance can be attributed to the exclusion of false positive pixels resulting from isolated, high intensity pixels in high noise conditions. To test the method’s power in real situation, we used high power confocal images of the somatosensory thalamus immunostained for Kv4.2 and Kv4.3 (A-type) voltage-gated potassium channels in mice. Moran’s method identified high-intensity Kv4.2 and Kv4.3 ion channel clusters in the thalamic neuropil. Spatial distribution of these clusters displayed strong correlation with large sensory axon terminals of subcortical origin. The unique association of the special presynaptic terminals and a postsynaptic voltage-gated ion channel cluster was confirmed with electron microscopy. These data demonstrate that Moran’s method is a rapid, simple image segmentation method optimal for variable and high noise conditions. acknowledgement: "This research was supported by the Wellcome Trust (ZN, LA). In addition, LA was supported by an ERC Advanced Grant (FRONTHAL, 742595) and the European Union project RRF-2.3.1-\r\n21-2022-00004 within the framework of the Artificial Intelligence National Laboratory and Lendület_2023_90. ZN is the recipient of a Hungarian Academy of Sciences Momentum Grant (Lendület, LP2012-29) and an ERC Advanced Grant (293681). We thank the Light Microscopy Center at Institute of Experimental Medicine for kindly providing microscopy support. Authors would like to express their deepest gratitude to Prof Luc Anselin (Center for Spatial Data Science, University of Chicago) and Dr Szabolcs Káli (Instiute of Experimental Medicine, Budapest) for the valuable discussion about analysis of spatial association, and to Krisztina Faddi for the excellent technical assistance. " article_number: '89361' article_processing_charge: Yes article_type: original author: - first_name: Csaba full_name: Dávid, Csaba last_name: Dávid - first_name: Kristóf full_name: Giber, Kristóf last_name: Giber - first_name: Margit Katalin full_name: Szigeti, Margit Katalin id: 44F4BDC0-F248-11E8-B48F-1D18A9856A87 last_name: Szigeti orcid: 0000-0001-9500-8758 - first_name: Mihály full_name: Köllő, Mihály last_name: Köllő - first_name: Zoltan full_name: Nusser, Zoltan last_name: Nusser - first_name: Laszlo full_name: Acsady, Laszlo last_name: Acsady citation: ama: Dávid C, Giber K, Szigeti MK, Köllő M, Nusser Z, Acsady L. A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus. eLife. 2024;12. doi:10.7554/elife.89361 apa: Dávid, C., Giber, K., Szigeti, M. K., Köllő, M., Nusser, Z., & Acsady, L. (2024). A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus. ELife. eLife Sciences Publications. https://doi.org/10.7554/elife.89361 chicago: Dávid, Csaba, Kristóf Giber, Margit Katalin Szigeti, Mihály Köllő, Zoltan Nusser, and Laszlo Acsady. “A Novel Image Segmentation Method Based on Spatial Autocorrelation Identifies A-Type Potassium Channel Clusters in the Thalamus.” ELife. eLife Sciences Publications, 2024. https://doi.org/10.7554/elife.89361. ieee: C. Dávid, K. Giber, M. K. Szigeti, M. Köllő, Z. Nusser, and L. Acsady, “A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus,” eLife, vol. 12. eLife Sciences Publications, 2024. ista: Dávid C, Giber K, Szigeti MK, Köllő M, Nusser Z, Acsady L. 2024. A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus. eLife. 12, 89361. mla: Dávid, Csaba, et al. “A Novel Image Segmentation Method Based on Spatial Autocorrelation Identifies A-Type Potassium Channel Clusters in the Thalamus.” ELife, vol. 12, 89361, eLife Sciences Publications, 2024, doi:10.7554/elife.89361. short: C. Dávid, K. Giber, M.K. Szigeti, M. Köllő, Z. Nusser, L. Acsady, ELife 12 (2024). date_created: 2025-01-08T13:25:45Z date_published: 2024-12-10T00:00:00Z date_updated: 2025-01-08T13:37:04Z day: '10' ddc: - '570' department: - _id: GaNo doi: 10.7554/elife.89361 file: - access_level: open_access checksum: 1d64265f62a3bf14550b4f5c684f1782 content_type: application/pdf creator: dernst date_created: 2025-01-08T13:33:05Z date_updated: 2025-01-08T13:33:05Z file_id: '18780' file_name: 2024_eLife_David.pdf file_size: 9992462 relation: main_file success: 1 file_date_updated: 2025-01-08T13:33:05Z has_accepted_license: '1' intvolume: ' 12' language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: eLife publication_identifier: issn: - 2050-084X publication_status: published publisher: eLife Sciences Publications quality_controlled: '1' scopus_import: '1' status: public title: A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus 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: 12 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18847' abstract: - lang: eng text: "Machine Learning and AI have the potential to transform data-driven\r\nscientific discovery, enabling accurate predictions for several scientific\r\nphenomena. As many scientific questions are inherently causal, this paper looks\r\nat the causal inference task of treatment effect estimation, where the outcome\r\nof interest is recorded in high-dimensional observations in a Randomized\r\nControlled Trial (RCT). Despite being the simplest possible causal setting and\r\na perfect fit for deep learning, we theoretically find that many common choices\r\nin the literature may lead to biased estimates. To test the practical impact of\r\nthese considerations, we recorded ISTAnt, the first real-world benchmark for\r\ncausal inference downstream tasks on high-dimensional observations as an RCT\r\nstudying how garden ants (Lasius neglectus) respond to microparticles applied\r\nonto their colony members by hygienic grooming. Comparing 6 480 models\r\nfine-tuned from state-of-the-art visual backbones, we find that the sampling\r\nand modeling choices significantly affect the accuracy of the causal estimate,\r\nand that classification accuracy is not a proxy thereof. We further validated\r\nthe analysis, repeating it on a synthetically generated visual data set\r\ncontrolling the causal model. Our results suggest that future benchmarks should\r\ncarefully consider real downstream scientific questions, especially causal\r\nones. Further, we highlight guidelines for representation learning methods to\r\nhelp answer causal questions in the sciences." acknowledgement: We thank Piersilvio De Bartolomeis, and the full Causal Learning and Artificial Intelligence (CLAI) group at ISTA for the extremely helpful discussions. Riccardo Cadei was supported by a Google Research Scholar Award and a Google Initiated Gift to Francesco Locatello. We thank the Social Immunity team at ISTA particularly Michaela Hönigsberger and Wilfrid Jean Louis, for supporting the ecological experiment and Farnaz Beikzadeh Abbasi, Luisa Fiebig and Martin Estermann for annotating ant behavior in ISTAnt. article_processing_charge: No arxiv: 1 author: - first_name: Riccardo full_name: Cadei, Riccardo id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b last_name: Cadei - first_name: Lukas full_name: Lindorfer, Lukas id: 85f0e6d3-06b3-11ec-8982-8c5049fa4455 last_name: Lindorfer - first_name: Sylvia full_name: Cremer, Sylvia id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87 last_name: Cremer orcid: 0000-0002-2193-3868 - first_name: Cordelia full_name: Schmid, Cordelia last_name: Schmid - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Cadei R, Lindorfer L, Cremer S, Schmid C, Locatello F. Smoke and mirrors in causal downstream tasks. In: ICML 2024 Workshop AI4Science. Vol 38. Curran Associates; 2024.' apa: Cadei, R., Lindorfer, L., Cremer, S., Schmid, C., & Locatello, F. (2024). Smoke and mirrors in causal downstream tasks. In ICML 2024 Workshop AI4Science (Vol. 38). Curran Associates. chicago: Cadei, Riccardo, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, and Francesco Locatello. “Smoke and Mirrors in Causal Downstream Tasks.” In ICML 2024 Workshop AI4Science, Vol. 38. Curran Associates, 2024. ieee: R. Cadei, L. Lindorfer, S. Cremer, C. Schmid, and F. Locatello, “Smoke and mirrors in causal downstream tasks,” in ICML 2024 Workshop AI4Science, 2024, vol. 38. ista: 'Cadei R, Lindorfer L, Cremer S, Schmid C, Locatello F. 2024. Smoke and mirrors in causal downstream tasks. ICML 2024 Workshop AI4Science. ICML: International Conference on Machine Learning vol. 38.' mla: Cadei, Riccardo, et al. “Smoke and Mirrors in Causal Downstream Tasks.” ICML 2024 Workshop AI4Science, vol. 38, Curran Associates, 2024. short: R. Cadei, L. Lindorfer, S. Cremer, C. Schmid, F. Locatello, in:, ICML 2024 Workshop AI4Science, Curran Associates, 2024. conference: end_date: 2024-07-26 name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-26 corr_author: '1' date_created: 2025-01-14T07:27:26Z date_published: 2024-09-25T00:00:00Z date_updated: 2025-07-10T11:51:50Z day: '25' ddc: - '000' - '570' department: - _id: SyCr - _id: FrLo - _id: GradSch external_id: arxiv: - '2405.17151' file: - access_level: open_access checksum: beedf05388bbdb7ddda81ec3d5ec7026 content_type: application/pdf creator: dernst date_created: 2025-01-27T11:42:24Z date_updated: 2025-01-27T11:42:24Z file_id: '18896' file_name: 2024_ICML_Cadei.pdf file_size: 4453014 relation: main_file success: 1 file_date_updated: 2025-01-27T11:42:24Z has_accepted_license: '1' intvolume: ' 38' language: - iso: eng month: '09' oa: 1 oa_version: Published Version publication: ICML 2024 Workshop AI4Science publication_status: published publisher: Curran Associates quality_controlled: '1' related_material: link: - relation: software url: https://github.com/CausalLearningAI/ISTAnt record: - id: '18895' relation: research_data status: public - id: '19509' relation: is_continued_by status: for_moderation scopus_import: '1' status: public title: Smoke and mirrors in causal downstream tasks tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 38 year: '2024' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '18856' abstract: - lang: eng text: This research is aimed to solve the tweet/user geolocation prediction task and provide a flexible methodology for the geo-tagging of textual big data. The suggested approach implements neural networks for natural language processing (NLP) to estimate the location as coordinate pairs (longitude, latitude) and two-dimensional Gaussian Mixture Models (GMMs). The scope of proposed models has been finetuned on a Twitter dataset using pretrained Bidirectional Encoder Representations from Transformers (BERT) as base models. Performance metrics show a median error of fewer than 30 km on a worldwide-level, and fewer than 15 km on the US-level datasets for the models trained and evaluated on text features of tweets' content and metadata context. Our source code and data are available at https://github.com/K4TEL/geo-twitter.git. acknowledgement: The authors acknowledge the Institute of Science and Technology (ISTA) for their material support and for granting access to the Twitter database archive, which was essential for the research. article_processing_charge: Yes article_type: original author: - first_name: Kateryna full_name: Lutsai, Kateryna last_name: Lutsai - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Lutsai K, Lampert C. Predicting the geolocation of tweets using transformer models on customized data. Journal of Spatial Information Science. 2024;(29):69-99. doi:10.5311/JOSIS.2024.29.295 apa: Lutsai, K., & Lampert, C. (2024). Predicting the geolocation of tweets using transformer models on customized data. Journal of Spatial Information Science. University of Maine. https://doi.org/10.5311/JOSIS.2024.29.295 chicago: Lutsai, Kateryna, and Christoph Lampert. “Predicting the Geolocation of Tweets Using Transformer Models on Customized Data.” Journal of Spatial Information Science. University of Maine, 2024. https://doi.org/10.5311/JOSIS.2024.29.295. ieee: K. Lutsai and C. Lampert, “Predicting the geolocation of tweets using transformer models on customized data,” Journal of Spatial Information Science, no. 29. University of Maine, pp. 69–99, 2024. ista: Lutsai K, Lampert C. 2024. Predicting the geolocation of tweets using transformer models on customized data. Journal of Spatial Information Science. (29), 69–99. mla: Lutsai, Kateryna, and Christoph Lampert. “Predicting the Geolocation of Tweets Using Transformer Models on Customized Data.” Journal of Spatial Information Science, no. 29, University of Maine, 2024, pp. 69–99, doi:10.5311/JOSIS.2024.29.295. short: K. Lutsai, C. Lampert, Journal of Spatial Information Science (2024) 69–99. corr_author: '1' date_created: 2025-01-19T23:01:53Z date_published: 2024-12-26T00:00:00Z date_updated: 2025-06-05T13:47:12Z day: '26' ddc: - '500' department: - _id: ChLa doi: 10.5311/JOSIS.2024.29.295 file: - access_level: open_access checksum: b82413f00398ffb5168e8e747571a98d content_type: application/pdf creator: dernst date_created: 2025-01-20T08:41:10Z date_updated: 2025-01-20T08:41:10Z file_id: '18857' file_name: 2024_JourSpatialInfoScience_Lutsai.pdf file_size: 7250655 relation: main_file success: 1 file_date_updated: 2025-01-20T08:41:10Z has_accepted_license: '1' issue: '29' language: - iso: eng license: https://creativecommons.org/licenses/by/3.0/ month: '12' oa: 1 oa_version: Published Version page: 69-99 publication: Journal of Spatial Information Science publication_identifier: eissn: - 1948-660X publication_status: published publisher: University of Maine quality_controlled: '1' related_material: link: - relation: software url: https://github.com/K4TEL/geo-twitter.git scopus_import: '1' status: public title: Predicting the geolocation of tweets using transformer models on customized data tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode name: Creative Commons Attribution 3.0 Unported (CC BY 3.0) short: CC BY (3.0) type: journal_article user_id: 68b8ca59-c5b3-11ee-8790-cd641c68093d year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18875' abstract: - lang: eng text: Current state-of-the-art methods for differentially private model training are based on matrix factorization techniques. However, these methods suffer from high computational overhead because they require numerically solving a demanding optimization problem to determine an approximately optimal factorization prior to the actual model training. In this work, we present a new matrix factorization approach, BSR, which overcomes this computational bottleneck. By exploiting properties of the standard matrix square root, BSR allows to efficiently handle also large-scale problems. For the key scenario of stochastic gradient descent with momentum and weight decay, we even derive analytical expressions for BSR that render the computational overhead negligible. We prove bounds on the approximation quality that hold both in the centralized and in the federated learning setting. Our numerical experiments demonstrate that models trained using BSR perform on par with the best existing methods, while completely avoiding their computational overhead. alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Nikita full_name: Kalinin, Nikita id: 4b14526e-14d2-11ed-ba64-c14c9553d137 last_name: Kalinin - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Kalinin N, Lampert C. Banded square root matrix factorization for differentially private model training. In: 38th Annual Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Kalinin, N., & Lampert, C. (2024). Banded square root matrix factorization for differentially private model training. In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: Kalinin, Nikita, and Christoph Lampert. “Banded Square Root Matrix Factorization for Differentially Private Model Training.” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024. ieee: N. Kalinin and C. Lampert, “Banded square root matrix factorization for differentially private model training,” in 38th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37. ista: 'Kalinin N, Lampert C. 2024. Banded square root matrix factorization for differentially private model training. 38th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: Kalinin, Nikita, and Christoph Lampert. “Banded Square Root Matrix Factorization for Differentially Private Model Training.” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024. short: N. Kalinin, C. Lampert, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-16 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-16 corr_author: '1' date_created: 2025-01-24T17:58:16Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-05-14T11:34:20Z day: '01' ddc: - '000' department: - _id: GradSch - _id: ChLa external_id: arxiv: - '2405.13763' file: - access_level: open_access checksum: a216cab8eddc1fe7840aede0e2c0d41e content_type: application/pdf creator: dernst date_created: 2025-01-27T09:52:15Z date_updated: 2025-01-27T09:52:15Z file_id: '18888' file_name: 2024_NeurIPS_Nikita.pdf file_size: 1144656 relation: main_file success: 1 file_date_updated: 2025-01-27T09:52:15Z has_accepted_license: '1' intvolume: ' 37' language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: 38th Annual Conference on Neural Information Processing Systems publication_identifier: eissn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Banded square root matrix factorization for differentially private model training tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '18890' abstract: - lang: eng text: Deep Neural Collapse (DNC) refers to the surprisingly rigid structure of the data representations in the final layers of Deep Neural Networks (DNNs). Though the phenomenon has been measured in a variety of settings, its emergence is typically explained via data-agnostic approaches, such as the unconstrained features model. In this work, we introduce a data-dependent setting where DNC forms due to feature learning through the average gradient outer product (AGOP). The AGOP is defined with respect to a learned predictor and is equal to the uncentered covariance matrix of its input-output gradients averaged over the training dataset. The Deep Recursive Feature Machine (Deep RFM) is a method that constructs a neural network by iteratively mapping the data with the AGOP and applying an untrained random feature map. We demonstrate empirically that DNC occurs in Deep RFM across standard settings as a consequence of the projection with the AGOP matrix computed at each layer. Further, we theoretically explain DNC in Deep RFM in an asymptotic setting and as a result of kernel learning. We then provide evidence that this mechanism holds for neural networks more generally. In particular, we show that the right singular vectors and values of the weights can be responsible for the majority of within-class variability collapse for DNNs trained in the feature learning regime. As observed in recent work, this singular structure is highly correlated with that of the AGOP. acknowledgement: 'We acknowledge support from the National Science Foundation (NSF) and the Simons Foundation for the Collaboration on the Theoretical Foundations of Deep Learning through awards DMS-2031883 and #814639 as well as the TILOS institute (NSF CCF-2112665). This work used the programs (1) XSEDE (Extreme science and engineering discovery environment) which is supported by NSF grant numbers ACI-1548562, and (2) ACCESS (Advanced cyberinfrastructure coordination ecosystem: services & support) which is supported by NSF grants numbers #2138259, #2138286, #2138307, #2137603, and #2138296. Specifically, we used the resources from SDSC Expanse GPU compute nodes, and NCSA Delta system, via allocations TG-CIS220009. Marco Mondelli is supported by the 2019 Lopez-Loreta prize. We also acknowledge useful feedback from anonymous reviewers. ' alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Daniel full_name: Beaglehole, Daniel last_name: Beaglehole - first_name: Peter full_name: Súkeník, Peter id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c last_name: Súkeník - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 - first_name: Mikhail full_name: Belkin, Mikhail last_name: Belkin citation: ama: 'Beaglehole D, Súkeník P, Mondelli M, Belkin M. Average gradient outer product as a mechanism for deep neural collapse. In: 38th Annual Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Beaglehole, D., Súkeník, P., Mondelli, M., & Belkin, M. (2024). Average gradient outer product as a mechanism for deep neural collapse. In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: Beaglehole, Daniel, Peter Súkeník, Marco Mondelli, and Mikhail Belkin. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024. ieee: D. Beaglehole, P. Súkeník, M. Mondelli, and M. Belkin, “Average gradient outer product as a mechanism for deep neural collapse,” in 38th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37. ista: 'Beaglehole D, Súkeník P, Mondelli M, Belkin M. 2024. Average gradient outer product as a mechanism for deep neural collapse. 38th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: Beaglehole, Daniel, et al. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024. short: D. Beaglehole, P. Súkeník, M. Mondelli, M. Belkin, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-16 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-16 corr_author: '1' date_created: 2025-01-27T11:11:40Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-05-14T11:29:45Z day: '01' department: - _id: GradSch - _id: MaMo external_id: arxiv: - '2402.13728' intvolume: ' 37' language: - iso: eng main_file_link: - open_access: '1' url: https://openreview.net/forum?id=lJ1jdl2K9k month: '12' oa: 1 oa_version: Preprint project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: 38th Annual Conference on Neural Information Processing Systems publication_identifier: eissn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Average gradient outer product as a mechanism for deep neural collapse type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18891' abstract: - lang: eng text: "Deep neural networks (DNNs) exhibit a surprising structure in their final layer\r\nknown as neural collapse (NC), and a growing body of works has currently investigated the propagation of neural collapse to earlier layers of DNNs – a phenomenon\r\ncalled deep neural collapse (DNC). However, existing theoretical results are restricted to special cases: linear models, only two layers or binary classification.\r\nIn contrast, we focus on non-linear models of arbitrary depth in multi-class classification and reveal a surprising qualitative shift. As soon as we go beyond two\r\nlayers or two classes, DNC stops being optimal for the deep unconstrained features\r\nmodel (DUFM) – the standard theoretical framework for the analysis of collapse.\r\nThe main culprit is a low-rank bias of multi-layer regularization schemes: this bias\r\nleads to optimal solutions of even lower rank than the neural collapse. We support\r\nour theoretical findings with experiments on both DUFM and real data, which show\r\nthe emergence of the low-rank structure in the solution found by gradient descent." acknowledged_ssus: - _id: ScienComp acknowledgement: Marco Mondelli is partially supported by the 2019 Lopez-Loreta prize. This research was supported by the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp). alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Peter full_name: Súkeník, Peter id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c last_name: Súkeník - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: ama: 'Súkeník P, Lampert C, Mondelli M. Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In: 38th Annual Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Súkeník, P., Lampert, C., & Mondelli, M. (2024). Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: 'Súkeník, Peter, Christoph Lampert, and Marco Mondelli. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.' ieee: 'P. Súkeník, C. Lampert, and M. Mondelli, “Neural collapse versus low-rank bias: Is deep neural collapse really optimal?,” in 38th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.' ista: 'Súkeník P, Lampert C, Mondelli M. 2024. Neural collapse versus low-rank bias: Is deep neural collapse really optimal? 38th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: 'Súkeník, Peter, et al. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.' short: P. Súkeník, C. Lampert, M. Mondelli, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-16 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-16 corr_author: '1' date_created: 2025-01-27T11:15:18Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-06-04T07:19:21Z day: '01' ddc: - '000' department: - _id: GradSch - _id: MaMo - _id: ChLa external_id: arxiv: - '2405.14468' file: - access_level: open_access checksum: b7b79f1ea3ac1e9e11b3d91faaeb0780 content_type: application/pdf creator: dernst date_created: 2025-02-04T08:11:25Z date_updated: 2025-02-04T08:11:25Z file_id: '18989' file_name: 2024_NeurIPS_Sukenik.pdf file_size: 1784118 relation: main_file success: 1 file_date_updated: 2025-02-04T08:11:25Z has_accepted_license: '1' intvolume: ' 37' language: - iso: eng month: '12' oa: 1 oa_version: Published Version project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: 38th Annual Conference on Neural Information Processing Systems publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' status: public title: 'Neural collapse versus low-rank bias: Is deep neural collapse really optimal?' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: gold _id: '18895' abstract: - lang: eng text: 'ISTAnt is a new ecological dataset for social immunity and represents the first real-world benchmark for causal inference downstream tasks on high-dimensional observations. It analyzes grooming behavior in the ant Lasius neglectus in groups of three worker ants. The workers for the experiment were obtained from their laboratory stock colony, which had been collected from the field in 2022 in the Botanical Garden Jena, Germany. Ant collection and all experimental work were performed in compliance with international, national and institutional regulations and ethical guidelines. For the experiment, the body surface of one of the three ants was treated with a suspension of either of two microparticle types (diameter ~5 µm) to induce grooming by the two nestmates, which were individually color-coded by application of a dot of blue or orange paint, respectively. The three ants were housed in small plastic containers (diameter 28mm, height 30mm) with moistened, plastered ground and the interior walls covered with PTFE (polytetrafluoroethane) to hamper climbing by the ants. Filming occurred in a temperature- and humidity-controlled room at 23°C within a custom-made filming box with controlled lighting and ventilation conditions. We set up nine ant groups at a time (always containing both treatments) and placed them randomly on positions 1-9 marked on the floor in a 3x3 grid, about 3mm from each other. The experiment was performed on two consecutive days. Videos were acquired using a USB camera (FLIR blackfly S BFS-U3-120S4C, Teledyne FLIR) with a high-performance lens (HP Series 25mm Focal Length, Edmund optics 86-572) in OBS studio 29.0.0 \citep{bailey2017obs} at a framerate of 30 FPS and a resolution of 2500x2500 pixels. From each original video (105x105 mm), we generated nine individual videos .mkv (each ~32x32 mm, 770x770 pixels) by determining exact coordinates per container from one frame in GIMP 2.10.36 and cropping of the videos with FFmpeg 6.1.1. Annotation was performed over two consecutive days by three observers who had not been involved in the experimental setup or recording and were unaware of the treatment assignments to ensure bias-free behavioral annotation. They annotated the behavior of the ants during video observations, using custom-made software that saves the start and end frames of behaviors marked in a .csv file (see ''annotations'' folder). In one of the videos, one of the nestmates'' legs got inadvertently stuck to its body surface during the color-coding, interfering with its behavior, so the video was discarded. This left 44 videos from 5 independent setups (n=24 of treatment 1 and n=20 of treatment 2) of 10 minutes each for a total of 792 000 annotated frames (see ''video'' folder). For each video, we provide the following information: the number of the set to which it belongs (1-5); the number of the position within the set reflecting the position of the ant group under the camera (1-9), for which we also provide ‘coordinates’ in the 3x3 grid (taking values -1/0/1 for both X and Y axis); treatment (1 or 2); the hour of the day when the recording was started (in 24h CEST); experimental day (A or B); the top left coordinate of the cropping square from the original video (CropX/CropY); the person annotating the video (given as A, B, C); the date of annotation (1: first day, 2: second day) and in which order the videos were annotated by each person, both reflecting a possible training effect of the person (see ''experiments_settings.csv'' file).' article_processing_charge: No author: - first_name: Riccardo full_name: Cadei, Riccardo id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b last_name: Cadei - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Sylvia M full_name: Cremer, Sylvia M id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87 last_name: Cremer orcid: 0000-0002-2193-3868 - first_name: Lukas full_name: Lindorfer, Lukas id: 85f0e6d3-06b3-11ec-8982-8c5049fa4455 last_name: Lindorfer - first_name: Cordelia full_name: Schmid, Cordelia last_name: Schmid citation: ama: Cadei R, Locatello F, Cremer S, Lindorfer L, Schmid C. ISTAnt. 2024. doi:10.6084/M9.FIGSHARE.26484934.V2 apa: Cadei, R., Locatello, F., Cremer, S., Lindorfer, L., & Schmid, C. (2024). ISTAnt. Institute of Science and Technology Austria. https://doi.org/10.6084/M9.FIGSHARE.26484934.V2 chicago: Cadei, Riccardo, Francesco Locatello, Sylvia Cremer, Lukas Lindorfer, and Cordelia Schmid. “ISTAnt.” Institute of Science and Technology Austria, 2024. https://doi.org/10.6084/M9.FIGSHARE.26484934.V2. ieee: R. Cadei, F. Locatello, S. Cremer, L. Lindorfer, and C. Schmid, “ISTAnt.” Institute of Science and Technology Austria, 2024. ista: Cadei R, Locatello F, Cremer S, Lindorfer L, Schmid C. 2024. ISTAnt, Institute of Science and Technology Austria, 10.6084/M9.FIGSHARE.26484934.V2. mla: Cadei, Riccardo, et al. ISTAnt. Institute of Science and Technology Austria, 2024, doi:10.6084/M9.FIGSHARE.26484934.V2. short: R. Cadei, F. Locatello, S. Cremer, L. Lindorfer, C. Schmid, (2024). corr_author: '1' date_created: 2025-01-27T11:45:43Z date_published: 2024-10-23T00:00:00Z date_updated: 2025-01-27T11:58:38Z day: '23' ddc: - '570' department: - _id: SyCr - _id: FrLo - _id: GradSch doi: 10.6084/M9.FIGSHARE.26484934.V2 main_file_link: - open_access: '1' url: https://10.6084/M9.FIGSHARE.26484934.V2 month: '10' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria related_material: record: - id: '18847' relation: used_in_publication status: public status: public title: ISTAnt type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18897' abstract: - lang: eng text: 'Score-based generative models (SGMs) are powerful tools to sample from complex data distributions. Their underlying idea is to (i) run a forward process for time T1 by adding noise to the data, (ii) estimate its score function, and (iii) use such estimate to run a reverse process. As the reverse process is initialized with the stationary distribution of the forward one, the existing analysis paradigm requires T1→∞. This is however problematic: from a theoretical viewpoint, for a given precision of the score approximation, the convergence guarantee fails as T1 diverges; from a practical viewpoint, a large T1 increases computational costs and leads to error propagation. This paper addresses the issue by considering a version of the popular predictor-corrector scheme: after running the forward process, we first estimate the final distribution via an inexact Langevin dynamics and then revert the process. Our key technical contribution is to provide convergence guarantees which require to run the forward process only for a fixed finite time T1. Our bounds exhibit a mild logarithmic dependence on the input dimension and the subgaussian norm of the target distribution, have minimal assumptions on the data, and require only to control the L2 loss on the score approximation, which is the quantity minimized in practice.' acknowledgement: "Francesco Pedrotti and Jan Maas acknowledge support by the Austrian Science Fund (FWF) project 10.55776/F65. Marco Mondelli acknowledges support by the 2019 Lopez-Loreta prize.\r\n" alternative_title: - TMLR article_processing_charge: No arxiv: 1 author: - first_name: Francesco full_name: Pedrotti, Francesco id: d3ac8ac6-dc8d-11ea-abe3-e2a9628c4c3c last_name: Pedrotti - first_name: Jan full_name: Maas, Jan id: 4C5696CE-F248-11E8-B48F-1D18A9856A87 last_name: Maas orcid: 0000-0002-0845-1338 - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: ama: 'Pedrotti F, Maas J, Mondelli M. Improved convergence of score-based diffusion models via prediction-correction. In: Transactions on Machine Learning Research. ; 2024.' apa: Pedrotti, F., Maas, J., & Mondelli, M. (2024). Improved convergence of score-based diffusion models via prediction-correction. In Transactions on Machine Learning Research. chicago: Pedrotti, Francesco, Jan Maas, and Marco Mondelli. “Improved Convergence of Score-Based Diffusion Models via Prediction-Correction.” In Transactions on Machine Learning Research, 2024. ieee: F. Pedrotti, J. Maas, and M. Mondelli, “Improved convergence of score-based diffusion models via prediction-correction,” in Transactions on Machine Learning Research, 2024. ista: Pedrotti F, Maas J, Mondelli M. 2024. Improved convergence of score-based diffusion models via prediction-correction. Transactions on Machine Learning Research. , TMLR, . mla: Pedrotti, Francesco, et al. “Improved Convergence of Score-Based Diffusion Models via Prediction-Correction.” Transactions on Machine Learning Research, 2024. short: F. Pedrotti, J. Maas, M. Mondelli, in:, Transactions on Machine Learning Research, 2024. corr_author: '1' date_created: 2025-01-27T12:18:05Z date_published: 2024-06-01T00:00:00Z date_updated: 2025-04-15T08:31:35Z day: '01' ddc: - '000' department: - _id: JaMa - _id: MaMo external_id: arxiv: - '2305.14164' file: - access_level: open_access checksum: 76a1fd5afd8ee6f7ae0e5912d7dbf6b4 content_type: application/pdf creator: dernst date_created: 2025-01-27T12:19:44Z date_updated: 2025-01-27T12:19:44Z file_id: '18898' file_name: 2024_TMLR_Pedrotti.pdf file_size: 780315 relation: main_file success: 1 file_date_updated: 2025-01-27T12:19:44Z has_accepted_license: '1' language: - iso: eng month: '06' oa: 1 oa_version: Published Version project: - _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2 grant_number: F6504 name: Taming Complexity in Partial Differential Systems - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: Transactions on Machine Learning Research publication_identifier: issn: - 2835-8856 publication_status: published quality_controlled: '1' related_material: record: - id: '17350' relation: earlier_version status: public scopus_import: '1' status: public title: Improved convergence of score-based diffusion models via prediction-correction tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- _id: '18899' abstract: - lang: eng text: "The flourishing theory of classical optimal transport concerns mass transportation at minimal cost. This book introduces the reader to optimal transport on quantum structures, i.e., optimal transportation between quantum states and related non-commutative concepts of mass transportation. It contains lecture notes on\r\n\r\nclassical optimal transport and Wasserstein gradient flows\r\ndynamics and quantum optimal transport\r\nquantum couplings and many-body problems\r\nquantum channels and qubits\r\n\r\nThese notes are based on lectures given by the authors at the \"Optimal Transport on Quantum Structures\" School held at the Erdös Center in Budapest in the fall of 2022. The lecture notes are complemented by two survey chapters presenting the state of the art in different research areas of non-commutative optimal transport." alternative_title: - Bolyai Society Mathematical Studies article_processing_charge: No citation: ama: 'Maas J, Rademacher SAE, Titkos T, Virosztek D, eds. Optimal Transport on Quantum Structures. Vol 29. Cham: Springer Nature; 2024. doi:10.1007/978-3-031-50466-2' apa: 'Maas, J., Rademacher, S. A. E., Titkos, T., & Virosztek, D. (Eds.). (2024). Optimal Transport on Quantum Structures (Vol. 29). Cham: Springer Nature. https://doi.org/10.1007/978-3-031-50466-2' chicago: 'Maas, Jan, Simone Anna Elvira Rademacher, Tamás Titkos, and Daniel Virosztek, eds. Optimal Transport on Quantum Structures. Vol. 29. BSMS. Cham: Springer Nature, 2024. https://doi.org/10.1007/978-3-031-50466-2.' ieee: 'J. Maas, S. A. E. Rademacher, T. Titkos, and D. Virosztek, Eds., Optimal Transport on Quantum Structures, vol. 29. Cham: Springer Nature, 2024.' ista: 'Maas J, Rademacher SAE, Titkos T, Virosztek D eds. 2024. Optimal Transport on Quantum Structures, Cham: Springer Nature,p.' mla: Maas, Jan, et al., editors. Optimal Transport on Quantum Structures. Vol. 29, Springer Nature, 2024, doi:10.1007/978-3-031-50466-2. short: J. Maas, S.A.E. Rademacher, T. Titkos, D. Virosztek, eds., Optimal Transport on Quantum Structures, Springer Nature, Cham, 2024. date_created: 2025-01-27T12:26:03Z date_published: 2024-09-19T00:00:00Z date_updated: 2025-02-17T12:22:18Z day: '19' department: - _id: JaMa doi: 10.1007/978-3-031-50466-2 editor: - first_name: Jan full_name: Maas, Jan id: 4C5696CE-F248-11E8-B48F-1D18A9856A87 last_name: Maas orcid: 0000-0002-0845-1338 - first_name: Simone Anna Elvira full_name: Rademacher, Simone Anna Elvira id: 856966FE-A408-11E9-977E-802DE6697425 last_name: Rademacher orcid: 0000-0001-5059-4466 - first_name: Tamás full_name: Titkos, Tamás last_name: Titkos - first_name: Daniel full_name: Virosztek, Daniel id: 48DB45DA-F248-11E8-B48F-1D18A9856A87 last_name: Virosztek orcid: 0000-0003-1109-5511 intvolume: ' 29' language: - iso: eng month: '09' oa_version: None place: Cham publication_identifier: eisbn: - '9783031504662' eissn: - 2947-9460 isbn: - '9783031504655' issn: - 1217-4696 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' series_title: BSMS status: public title: Optimal Transport on Quantum Structures type: book_editor user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 29 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '18900' abstract: - lang: eng text: We prove that certain closable derivations on the GNS Hilbert space associated with a non-tracial weight on a von Neumann algebra give rise to GNS-symmetric semigroups of contractive completely positive maps on the von Neumann algebra. acknowledgement: 'The author was funded by the Austrian Science Fund under the Esprit Programme [ESP 156]. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. ' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Melchior full_name: Wirth, Melchior id: 88644358-0A0E-11EA-8FA5-49A33DDC885E last_name: Wirth orcid: 0000-0002-0519-4241 citation: ama: Wirth M. Modular completely Dirichlet forms as squares of derivations. International Mathematics Research Notices. 2024;2024(14):10597-10614. doi:10.1093/imrn/rnae092 apa: Wirth, M. (2024). Modular completely Dirichlet forms as squares of derivations. International Mathematics Research Notices. Oxford University Press. https://doi.org/10.1093/imrn/rnae092 chicago: Wirth, Melchior. “Modular Completely Dirichlet Forms as Squares of Derivations.” International Mathematics Research Notices. Oxford University Press, 2024. https://doi.org/10.1093/imrn/rnae092. ieee: M. Wirth, “Modular completely Dirichlet forms as squares of derivations,” International Mathematics Research Notices, vol. 2024, no. 14. Oxford University Press, pp. 10597–10614, 2024. ista: Wirth M. 2024. Modular completely Dirichlet forms as squares of derivations. International Mathematics Research Notices. 2024(14), 10597–10614. mla: Wirth, Melchior. “Modular Completely Dirichlet Forms as Squares of Derivations.” International Mathematics Research Notices, vol. 2024, no. 14, Oxford University Press, 2024, pp. 10597–614, doi:10.1093/imrn/rnae092. short: M. Wirth, International Mathematics Research Notices 2024 (2024) 10597–10614. corr_author: '1' date_created: 2025-01-27T12:36:10Z date_published: 2024-07-01T00:00:00Z date_updated: 2025-09-09T12:02:46Z day: '01' ddc: - '510' department: - _id: JaMa doi: 10.1093/imrn/rnae092 external_id: isi: - '001222279400001' file: - access_level: open_access checksum: 3e1f80d58ada0c60a58f35df8080967e content_type: application/pdf creator: dernst date_created: 2025-01-27T12:38:10Z date_updated: 2025-01-27T12:38:10Z file_id: '18901' file_name: 2024_IMRN_Wirth.pdf file_size: 689984 relation: main_file success: 1 file_date_updated: 2025-01-27T12:38:10Z has_accepted_license: '1' intvolume: ' 2024' isi: 1 issue: '14' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 10597-10614 project: - _id: 34c6ea2d-11ca-11ed-8bc3-c04f3c502833 grant_number: ESP156_N name: Gradient flow techniques for quantum Markov semigroups publication: International Mathematics Research Notices publication_identifier: eissn: - 1687-0247 issn: - 1073-7928 publication_status: published publisher: Oxford University Press quality_controlled: '1' scopus_import: '1' status: public title: Modular completely Dirichlet forms as squares of derivations 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: publisher OA_type: hybrid _id: '18904' abstract: - lang: eng text: The Planetary Transits and Oscillations of stars mission (PLATO) will allow us to measure surface rotation and monitor photometric activity of tens of thousands of main sequence solar-type and subgiant stars. This paper is the first of a series dedicated to the preparation of the analysis of stellar surface rotation and photospheric activity with the near-future PLATO data. We describe in this work the strategy that will be implemented in the PLATO pipeline to measure stellar surface rotation, photometric activity, and long-term modulations. The algorithms are applied on both noise-free and noisy simulations of solar-type stars, which include activity cycles, latitudinal differential rotation, and spot evolution. PLATO simulated systematics are included in the noisy light curves. We show that surface rotation periods can be recovered with confidence for most of the stars with only six months of observations and that the recovery rate of the analysis significantly improves as additional observations are collected. This means that the first PLATO data release will already provide a substantial set of measurements for this quantity, with a significant refinement on their quality as the instrument obtains longer light curves. Measuring the Schwabe-like magnetic activity cycle during the mission will require that the same field be observed over a significant timescale (more than four years). Nevertheless, PLATO will provide a vast and robust sample of solar-type stars with constraints on the activity-cycle length. Such a sample is lacking from previous missions dedicated to space photometry. acknowledgement: 'This work presents results from the European Space Agency (ESA) space mission PLATO. The PLATO payload, the PLATO Ground Segment and PLATO data processing are joint developments of ESA and the PLATO Mission Consortium (PMC). Funding for the PMC is provided at national levels, in particular by countries participating in the PLATO Multilateral Agreement (Austria, Belgium, Czech Republic, Denmark, France, Germany, Italy, Netherlands, Portugal, Spain, Sweden, Switzerland, Norway, and United Kingdom) and institutions from Brazil. Members of the PLATO Consortium can be found at https://platomission.com. The ESA PLATO mission website is https://www.cosmos.esa.int/plato. The authors thank the teams working for PLATO for all their work. They acknowledge the critical reading and the constructive comments from the anonymous referee that significantly allowed improving the original version of this paper. They finally thank R. Samadi for helpful advice and suggestions concerning the PSLS abilities. S.N.B, A.F.L, S.Me, I.P and E.C acknowledge support from PLATO ASI-INAF agreement no. 2022-28-HH.0 “PLATO Fase D”. S.N.B, L.A, A.S.B, Q.N, and A.S acknowledge financial support by ERC Whole Sun Synergy grant #810218. S.N.B, R.A.G, L.A, A.S.B, Q.N., D.B.P, E.P, and A.S acknowledge the support from PLATO CNES grant. R.A.G, D.B.P, and E.P acknowledge the support from SoHO/GOLF CNES grant. A.S.B, Q.N, and A.S acknowledge the support from INSU/PNST grant and Solar Orbiter CNES grant. A.S acknowledges funding from from the European Union’s Horizon-2020 research and innovation program (grant agreement no. 776403 ExoplANETS-A) and the Programme National de Planétologie (PNP). A.R.G.S acknowledges the support from the FCT through national funds and FEDER through COMPETE2020 (UIDB/04434/2020, UIDP/04434/2020, 2022.03993.PTDC) and the support from the FCT through the work contract No. 2020.02480.CEECIND/CP1631/CT0001. S.Ma acknowledges support from the Spanish Ministry of Science and Innovation (MICINN) with the Ramón y Cajal fellowship no. RYC-2015-17697 and through AEI under the Severo Ochoa Centres of Excellence Programme 2020–2023 (CEX2019-000920-S). S.Ma acknowledges support from the Spanish Ministry of Science and Innovation (MICINN) with the grant no. PID2019-107187GB-I00. M.J.G., K.B., R.M.O, J.P, O.R., C.R. acknowledge support from CNES. The computations were performed with the IRFU/CEA Saclay server facilities, funded by ERC Synergy grant WholeSun No.810218, the P2IO Labex emergence project FlarePredict, and CNES PLATO funds. Software:star-privateer (this work), pyspot (Aigrain et al. 2015), PSLS (Samadi et al. 2019), numpy (Harris et al. 2020), matplotlib (Hunter 2007), scipy (Virtanen et al. 2020), astropy (Astropy Collaboration 2022), pandas (Wes McKinney 2010; The pandas development team 2020), scikit-learn (Pedregosa et al. 2011).' article_number: A229 article_processing_charge: No article_type: original arxiv: 1 author: - first_name: S. N. full_name: Breton, S. N. last_name: Breton - first_name: A. F. full_name: Lanza, A. F. last_name: Lanza - first_name: S. full_name: Messina, S. last_name: Messina - first_name: I. full_name: Pagano, I. last_name: Pagano - first_name: Lisa Annabelle full_name: Bugnet, Lisa Annabelle id: d9edb345-f866-11ec-9b37-d119b5234501 last_name: Bugnet orcid: 0000-0003-0142-4000 - first_name: E. full_name: Corsaro, E. last_name: Corsaro - first_name: R. A. full_name: García, R. A. last_name: García - first_name: S. full_name: Mathur, S. last_name: Mathur - first_name: A. R. G. full_name: Santos, A. R. G. last_name: Santos - first_name: S. full_name: Aigrain, S. last_name: Aigrain - first_name: L. full_name: Amard, L. last_name: Amard - first_name: A. S. full_name: Brun, A. S. last_name: Brun - first_name: L. full_name: Degott, L. last_name: Degott - first_name: Q. full_name: Noraz, Q. last_name: Noraz - first_name: D. B. full_name: Palakkatharappil, D. B. last_name: Palakkatharappil - first_name: E. full_name: Panetier, E. last_name: Panetier - first_name: A. full_name: Strugarek, A. last_name: Strugarek - first_name: K. full_name: Belkacem, K. last_name: Belkacem - first_name: M.-J full_name: Goupil, M.-J last_name: Goupil - first_name: R. M. full_name: Ouazzani, R. M. last_name: Ouazzani - first_name: J. full_name: Philidet, J. last_name: Philidet - first_name: C. full_name: Renié, C. last_name: Renié - first_name: O. full_name: Roth, O. last_name: Roth citation: ama: Breton SN, Lanza AF, Messina S, et al. Measuring stellar surface rotation and activity with the PLATO mission. I. Strategy and application to simulated light curves. Astronomy and Astrophysics. 2024;689. doi:10.1051/0004-6361/202449893 apa: Breton, S. N., Lanza, A. F., Messina, S., Pagano, I., Bugnet, L. A., Corsaro, E., … Roth, O. (2024). Measuring stellar surface rotation and activity with the PLATO mission. I. Strategy and application to simulated light curves. Astronomy and Astrophysics. EDP Sciences. https://doi.org/10.1051/0004-6361/202449893 chicago: Breton, S. N., A. F. Lanza, S. Messina, I. Pagano, Lisa Annabelle Bugnet, E. Corsaro, R. A. García, et al. “Measuring Stellar Surface Rotation and Activity with the PLATO Mission. I. Strategy and Application to Simulated Light Curves.” Astronomy and Astrophysics. EDP Sciences, 2024. https://doi.org/10.1051/0004-6361/202449893. ieee: S. N. Breton et al., “Measuring stellar surface rotation and activity with the PLATO mission. I. Strategy and application to simulated light curves,” Astronomy and Astrophysics, vol. 689. EDP Sciences, 2024. ista: Breton SN, Lanza AF, Messina S, Pagano I, Bugnet LA, Corsaro E, García RA, Mathur S, Santos ARG, Aigrain S, Amard L, Brun AS, Degott L, Noraz Q, Palakkatharappil DB, Panetier E, Strugarek A, Belkacem K, Goupil M-J, Ouazzani RM, Philidet J, Renié C, Roth O. 2024. Measuring stellar surface rotation and activity with the PLATO mission. I. Strategy and application to simulated light curves. Astronomy and Astrophysics. 689, A229. mla: Breton, S. N., et al. “Measuring Stellar Surface Rotation and Activity with the PLATO Mission. I. Strategy and Application to Simulated Light Curves.” Astronomy and Astrophysics, vol. 689, A229, EDP Sciences, 2024, doi:10.1051/0004-6361/202449893. short: S.N. Breton, A.F. Lanza, S. Messina, I. Pagano, L.A. Bugnet, E. Corsaro, R.A. García, S. Mathur, A.R.G. Santos, S. Aigrain, L. Amard, A.S. Brun, L. Degott, Q. Noraz, D.B. Palakkatharappil, E. Panetier, A. Strugarek, K. Belkacem, M.-J. Goupil, R.M. Ouazzani, J. Philidet, C. Renié, O. Roth, Astronomy and Astrophysics 689 (2024). date_created: 2025-01-27T13:12:44Z date_published: 2024-09-01T00:00:00Z date_updated: 2025-09-09T12:04:24Z day: '01' ddc: - '520' department: - _id: LiBu doi: 10.1051/0004-6361/202449893 external_id: arxiv: - '2407.03709' isi: - '001366206400007' file: - access_level: open_access checksum: 5c871ba7370a507ed6ea9fb2304d8263 content_type: application/pdf creator: dernst date_created: 2025-01-27T13:18:41Z date_updated: 2025-01-27T13:18:41Z file_id: '18905' file_name: 2024_AstronomyAstrophysics_Breton.pdf file_size: 6212007 relation: main_file success: 1 file_date_updated: 2025-01-27T13:18:41Z has_accepted_license: '1' intvolume: ' 689' isi: 1 language: - iso: eng month: '09' oa: 1 oa_version: Published Version publication: Astronomy and Astrophysics publication_identifier: eissn: - 1432-0746 issn: - 0004-6361 publication_status: published publisher: EDP Sciences quality_controlled: '1' scopus_import: '1' status: public title: Measuring stellar surface rotation and activity with the PLATO mission. I. Strategy and application to simulated light curves 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: 689 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '18906' abstract: - lang: eng text: "Expander decompositions of graphs have significantly advanced the understanding of many classical graph problems and led to numerous fundamental theoretical results. However, their adoption in practice has been hindered due to their inherent intricacies and large hidden factors in their asymptotic running times. Here, we introduce the first practically efficient algorithm for computing expander decompositions and their hierarchies and demonstrate its effectiveness and utility by incorporating it as the core component in a novel solver for the normalized cut graph clustering objective.\r\nOur extensive experiments on a variety of large graphs show that our expander-based algorithm outperforms state-of-the-art solvers for normalized cut with respect to solution quality by a large margin on a variety of graph classes such as citation, e-mail, and social networks or web graphs while remaining competitive in running time." acknowledgement: "Monika Henzinger: This project has received funding from the European Research\r\nCouncil (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.\r\nHarald Räcke, Robin Münk: This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 498605858 and 470029389." article_processing_charge: Yes (in subscription journal) author: - first_name: Kathrin full_name: Hanauer, Kathrin last_name: Hanauer - 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: Robin full_name: Münk, Robin last_name: Münk - first_name: Harald full_name: Räcke, Harald last_name: Räcke - first_name: Maximilian full_name: Vötsch, Maximilian last_name: Vötsch citation: ama: 'Hanauer K, Henzinger M, Münk R, Räcke H, Vötsch M. Expander hierarchies for normalized cuts on graphs. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM; 2024:1016-1027. doi:10.1145/3637528.3671978' apa: 'Hanauer, K., Henzinger, M., Münk, R., Räcke, H., & Vötsch, M. (2024). Expander hierarchies for normalized cuts on graphs. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1016–1027). Barcelona, Spain: ACM. https://doi.org/10.1145/3637528.3671978' chicago: Hanauer, Kathrin, Monika Henzinger, Robin Münk, Harald Räcke, and Maximilian Vötsch. “Expander Hierarchies for Normalized Cuts on Graphs.” In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1016–27. ACM, 2024. https://doi.org/10.1145/3637528.3671978. ieee: K. Hanauer, M. Henzinger, R. Münk, H. Räcke, and M. Vötsch, “Expander hierarchies for normalized cuts on graphs,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 2024, pp. 1016–1027. ista: 'Hanauer K, Henzinger M, Münk R, Räcke H, Vötsch M. 2024. Expander hierarchies for normalized cuts on graphs. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD: Knowledge Discovery and Data Mining, 1016–1027.' mla: Hanauer, Kathrin, et al. “Expander Hierarchies for Normalized Cuts on Graphs.” Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, 2024, pp. 1016–27, doi:10.1145/3637528.3671978. short: K. Hanauer, M. Henzinger, R. Münk, H. Räcke, M. Vötsch, in:, Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, 2024, pp. 1016–1027. conference: end_date: 2024-08-29 location: Barcelona, Spain name: 'KDD: Knowledge Discovery and Data Mining' start_date: 2024-08-05 date_created: 2025-01-27T13:20:26Z date_published: 2024-09-01T00:00:00Z date_updated: 2025-09-09T12:04:56Z day: '01' ddc: - '000' department: - _id: MoHe doi: 10.1145/3637528.3671978 ec_funded: 1 external_id: isi: - '001324524201013' file: - access_level: open_access checksum: 1265d5cf6aa5f94157631651723c4a2b content_type: application/pdf creator: dernst date_created: 2025-01-27T13:25:23Z date_updated: 2025-01-27T13:25:23Z file_id: '18907' file_name: 2024_ACMKDD_Hanauer.pdf file_size: 1450331 relation: main_file success: 1 file_date_updated: 2025-01-27T13:25:23Z has_accepted_license: '1' isi: 1 language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: 1016-1027 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 publication: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining publication_identifier: isbn: - '9798400704901' publication_status: published publisher: ACM quality_controlled: '1' scopus_import: '1' status: public title: Expander hierarchies for normalized cuts on graphs tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ...