--- OA_place: publisher OA_type: gold _id: '18949' abstract: - lang: eng text: 'Speciation research—the scientific field focused on understanding the origin and diversity of species—has a long and complex history. While relevant to one another, the specific goals and activities of speciation researchers are highly diverse, and scattered across a collection of different perspectives. Thus, our understanding of speciation will benefit from efforts to bridge scientific findings and the diverse people who do the work. In this paper, we outline two ways of integrating speciation research: (i) scientific integration, through the bringing together of ideas, data, and approaches; and (ii) social integration, by creating ways for a diversity of researchers to participate in the scientific process. We then discuss five challenges to integration: (i) the multidisciplinary nature of speciation research, (ii) the complex language of speciation; (iii) a bias toward certain study systems; (iv) the challenges of working across scales; and (v) inconsistent measures and reporting standards. We provide practical steps that individuals and groups can take to help overcome these challenges, and argue that integration is a team effort in which we all have a role to play.' acknowledgement: "We thank the staff of the Tvärminne Zoological Station (University of Helsinki) for their hospitality during the workshop. We are also grateful to everyone who applied to attend the workshop.\r\nFunding for the workshop was provided by the European Society for Evolutionary Biology through the Special Topic Network (STN) funding scheme." article_number: kzae001 article_processing_charge: Yes article_type: original author: - first_name: Sean full_name: Stankowski, Sean id: 43161670-5719-11EA-8025-FABC3DDC885E last_name: Stankowski - first_name: Asher D full_name: Cutter, Asher D last_name: Cutter - first_name: Ina full_name: Satokangas, Ina last_name: Satokangas - first_name: Brian A full_name: Lerch, Brian A last_name: Lerch - first_name: Jonathan full_name: Rolland, Jonathan last_name: Rolland - first_name: Carole M full_name: Smadja, Carole M last_name: Smadja - first_name: J Carolina full_name: Segami Marzal, J Carolina last_name: Segami Marzal - first_name: Christopher R full_name: Cooney, Christopher R last_name: Cooney - first_name: Philine G D full_name: Feulner, Philine G D last_name: Feulner - first_name: Fabricius Maia Chaves Bicalho full_name: Domingos, Fabricius Maia Chaves Bicalho last_name: Domingos - first_name: Henry L full_name: North, Henry L last_name: North - first_name: Ryo full_name: Yamaguchi, Ryo last_name: Yamaguchi - first_name: Roger K full_name: Butlin, Roger K last_name: Butlin - first_name: Jochen B W full_name: Wolf, Jochen B W last_name: Wolf - first_name: Jenn full_name: Coughlan, Jenn last_name: Coughlan - first_name: Patrick full_name: Heidbreder, Patrick last_name: Heidbreder - first_name: Rebeca full_name: Hernández-Gutiérrez, Rebeca last_name: Hernández-Gutiérrez - first_name: Karen B full_name: Barnard-Kubow, Karen B last_name: Barnard-Kubow - first_name: David full_name: Peede, David last_name: Peede - first_name: Loïs full_name: Rancilhac, Loïs last_name: Rancilhac - first_name: Rodrigo Brincalepe full_name: Salvador, Rodrigo Brincalepe last_name: Salvador - first_name: Ken A full_name: Thompson, Ken A last_name: Thompson - first_name: Elizabeth A full_name: Stacy, Elizabeth A last_name: Stacy - first_name: Leonie C full_name: Moyle, Leonie C last_name: Moyle - first_name: Martin D full_name: Garlovsky, Martin D last_name: Garlovsky - first_name: Arif full_name: Maulana, Arif last_name: Maulana - first_name: Annina full_name: Kantelinen, Annina last_name: Kantelinen - first_name: N Ivalú full_name: Cacho, N Ivalú last_name: Cacho - first_name: Hilde full_name: Schneemann, Hilde last_name: Schneemann - first_name: Marisol full_name: Domínguez, Marisol last_name: Domínguez - first_name: Erik B full_name: Dopman, Erik B last_name: Dopman - first_name: Konrad full_name: Lohse, Konrad last_name: Lohse - first_name: Sina J full_name: Rometsch, Sina J last_name: Rometsch - first_name: Aaron A full_name: Comeault, Aaron A last_name: Comeault - first_name: Richard M full_name: Merrill, Richard M last_name: Merrill - first_name: Elizabeth S C full_name: Scordato, Elizabeth S C last_name: Scordato - first_name: Sonal full_name: Singhal, Sonal last_name: Singhal - first_name: Varpu full_name: Pärssinen, Varpu last_name: Pärssinen - first_name: Alycia C R full_name: Lackey, Alycia C R last_name: Lackey - first_name: Sanghamitra full_name: Kumar, Sanghamitra last_name: Kumar - first_name: Joana I full_name: Meier, Joana I last_name: Meier - first_name: Nicholas H full_name: Barton, Nicholas H id: 4880FE40-F248-11E8-B48F-1D18A9856A87 last_name: Barton orcid: 0000-0002-8548-5240 - first_name: Christelle full_name: Fraisse, Christelle id: 32DF5794-F248-11E8-B48F-1D18A9856A87 last_name: Fraisse orcid: 0000-0001-8441-5075 - first_name: Mark full_name: Ravinet, Mark last_name: Ravinet - first_name: Jonna full_name: Kulmuni, Jonna last_name: Kulmuni citation: ama: Stankowski S, Cutter AD, Satokangas I, et al. Toward the integration of speciation research. Evolutionary Journal of the Linnean Society. 2024;3(1). doi:10.1093/evolinnean/kzae001 apa: Stankowski, S., Cutter, A. D., Satokangas, I., Lerch, B. A., Rolland, J., Smadja, C. M., … Kulmuni, J. (2024). Toward the integration of speciation research. Evolutionary Journal of the Linnean Society. Oxford University Press. https://doi.org/10.1093/evolinnean/kzae001 chicago: Stankowski, Sean, Asher D Cutter, Ina Satokangas, Brian A Lerch, Jonathan Rolland, Carole M Smadja, J Carolina Segami Marzal, et al. “Toward the Integration of Speciation Research.” Evolutionary Journal of the Linnean Society. Oxford University Press, 2024. https://doi.org/10.1093/evolinnean/kzae001. ieee: S. Stankowski et al., “Toward the integration of speciation research,” Evolutionary Journal of the Linnean Society, vol. 3, no. 1. Oxford University Press, 2024. ista: Stankowski S, Cutter AD, Satokangas I, Lerch BA, Rolland J, Smadja CM, Segami Marzal JC, Cooney CR, Feulner PGD, Domingos FMCB, North HL, Yamaguchi R, Butlin RK, Wolf JBW, Coughlan J, Heidbreder P, Hernández-Gutiérrez R, Barnard-Kubow KB, Peede D, Rancilhac L, Salvador RB, Thompson KA, Stacy EA, Moyle LC, Garlovsky MD, Maulana A, Kantelinen A, Cacho NI, Schneemann H, Domínguez M, Dopman EB, Lohse K, Rometsch SJ, Comeault AA, Merrill RM, Scordato ESC, Singhal S, Pärssinen V, Lackey ACR, Kumar S, Meier JI, Barton NH, Fraisse C, Ravinet M, Kulmuni J. 2024. Toward the integration of speciation research. Evolutionary Journal of the Linnean Society. 3(1), kzae001. mla: Stankowski, Sean, et al. “Toward the Integration of Speciation Research.” Evolutionary Journal of the Linnean Society, vol. 3, no. 1, kzae001, Oxford University Press, 2024, doi:10.1093/evolinnean/kzae001. short: S. Stankowski, A.D. Cutter, I. Satokangas, B.A. Lerch, J. Rolland, C.M. Smadja, J.C. Segami Marzal, C.R. Cooney, P.G.D. Feulner, F.M.C.B. Domingos, H.L. North, R. Yamaguchi, R.K. Butlin, J.B.W. Wolf, J. Coughlan, P. Heidbreder, R. Hernández-Gutiérrez, K.B. Barnard-Kubow, D. Peede, L. Rancilhac, R.B. Salvador, K.A. Thompson, E.A. Stacy, L.C. Moyle, M.D. Garlovsky, A. Maulana, A. Kantelinen, N.I. Cacho, H. Schneemann, M. Domínguez, E.B. Dopman, K. Lohse, S.J. Rometsch, A.A. Comeault, R.M. Merrill, E.S.C. Scordato, S. Singhal, V. Pärssinen, A.C.R. Lackey, S. Kumar, J.I. Meier, N.H. Barton, C. Fraisse, M. Ravinet, J. Kulmuni, Evolutionary Journal of the Linnean Society 3 (2024). corr_author: '1' date_created: 2025-01-29T10:38:17Z date_published: 2024-02-16T00:00:00Z date_updated: 2025-01-29T10:55:54Z day: '16' ddc: - '570' department: - _id: NiBa doi: 10.1093/evolinnean/kzae001 file: - access_level: open_access checksum: db08120a92527acaef476bd93f2b87f9 content_type: application/pdf creator: dernst date_created: 2025-01-29T10:52:40Z date_updated: 2025-01-29T10:52:40Z file_id: '18950' file_name: 2024_EvolJourLinneanSoc_Stankowski.pdf file_size: 3935454 relation: main_file success: 1 file_date_updated: 2025-01-29T10:52:40Z has_accepted_license: '1' intvolume: ' 3' issue: '1' language: - iso: eng month: '02' oa: 1 oa_version: Published Version publication: Evolutionary Journal of the Linnean Society publication_identifier: issn: - 2752-938X publication_status: published publisher: Oxford University Press quality_controlled: '1' scopus_import: '1' status: public title: Toward the integration of speciation research 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: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 3 year: '2024' ... --- OA_place: repository OA_type: green _id: '18955' abstract: - lang: eng text: We give a simple proof that assuming the Exponential Time Hypothesis (ETH), determining the winner of a Rabin game cannot be done in time 2o(k log k) · nO(1), where k is the number of pairs of vertex subsets involved in the winning condition and n is the vertex count of the game graph. While this result follows from the lower bounds provided by Calude et al [SIAM J. Comp. 2022], our reduction is considerably simpler and arguably provides more insight into the complexity of the problem. In fact, the analogous lower bounds discussed by Calude et al, for solving Muller games and multidimensional parity games, follow as simple corollaries of our approach. Our reduction also highlights the usefulness of a certain pivot problem — Permutation SAT — which may be of independent interest. acknowledgement: This work is a part of projects CUTACOMBS (Ma. Pilipczuk), BOBR (Mi. Pilipczuk), and VAMOS (K. S. Thejaswini) that have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, grant agreements No 714704, 948057, and 101020093, respectively. Ma. Pilipczuk is also partially supported by Polish National Science Centre SONATA BIS-12 grant number 2022/46/E/ST6/00143. article_processing_charge: No arxiv: 1 author: - first_name: Antonio full_name: Casares, Antonio last_name: Casares - first_name: Marcin full_name: Pilipczuk, Marcin last_name: Pilipczuk - first_name: Michał full_name: Pilipczuk, Michał last_name: Pilipczuk - first_name: Uéverton S. full_name: Souza, Uéverton S. last_name: Souza - first_name: K. S. full_name: Thejaswini, K. S. id: 3807fb92-fdc1-11ee-bb4a-b4d8a431c753 last_name: Thejaswini citation: ama: 'Casares A, Pilipczuk M, Pilipczuk M, Souza US, Thejaswini KS. Simple and tight complexity lower bounds for solving Rabin games. In: 2024 Symposium on Simplicity in Algorithms. Society for Industrial and Applied Mathematics; 2024:160-167. doi:10.1137/1.9781611977936.16' apa: 'Casares, A., Pilipczuk, M., Pilipczuk, M., Souza, U. S., & Thejaswini, K. S. (2024). Simple and tight complexity lower bounds for solving Rabin games. In 2024 Symposium on Simplicity in Algorithms (pp. 160–167). Alexandria, VA, United States: Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977936.16' chicago: Casares, Antonio, Marcin Pilipczuk, Michał Pilipczuk, Uéverton S. Souza, and K. S. Thejaswini. “Simple and Tight Complexity Lower Bounds for Solving Rabin Games.” In 2024 Symposium on Simplicity in Algorithms, 160–67. Society for Industrial and Applied Mathematics, 2024. https://doi.org/10.1137/1.9781611977936.16. ieee: A. Casares, M. Pilipczuk, M. Pilipczuk, U. S. Souza, and K. S. Thejaswini, “Simple and tight complexity lower bounds for solving Rabin games,” in 2024 Symposium on Simplicity in Algorithms, Alexandria, VA, United States, 2024, pp. 160–167. ista: 'Casares A, Pilipczuk M, Pilipczuk M, Souza US, Thejaswini KS. 2024. Simple and tight complexity lower bounds for solving Rabin games. 2024 Symposium on Simplicity in Algorithms. SOSA: Symposium on Simplicity in Algorithms, 160–167.' mla: Casares, Antonio, et al. “Simple and Tight Complexity Lower Bounds for Solving Rabin Games.” 2024 Symposium on Simplicity in Algorithms, Society for Industrial and Applied Mathematics, 2024, pp. 160–67, doi:10.1137/1.9781611977936.16. short: A. Casares, M. Pilipczuk, M. Pilipczuk, U.S. Souza, K.S. Thejaswini, in:, 2024 Symposium on Simplicity in Algorithms, Society for Industrial and Applied Mathematics, 2024, pp. 160–167. conference: end_date: 2024-01-10 location: Alexandria, VA, United States name: 'SOSA: Symposium on Simplicity in Algorithms' start_date: 2024-01-08 date_created: 2025-01-29T11:55:50Z date_published: 2024-01-01T00:00:00Z date_updated: 2025-04-14T07:55:54Z day: '01' department: - _id: ToHe doi: 10.1137/1.9781611977936.16 ec_funded: 1 external_id: arxiv: - '2310.20433' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2310.20433 month: '01' oa: 1 oa_version: Preprint page: 160-167 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: 2024 Symposium on Simplicity in Algorithms publication_identifier: isbn: - '9781611977936' publication_status: published publisher: Society for Industrial and Applied Mathematics quality_controlled: '1' scopus_import: '1' status: public title: Simple and tight complexity lower bounds for solving Rabin games type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository OA_type: green _id: '18956' abstract: - lang: eng text: 'Group Activity Recognition (GAR) aims to detect the activity performed by multiple actors in a scene. Prior works model the spatio-temporal features based on the RGB, optical flow or keypoint data types. On the contrary, our hypothesis is that by only using the RGB data without temporality, the performance can be maintained with a negligible loss in accuracy. To that end, we propose a novel GAR technique for volleyball videos, DECOMPL, which consists of two complementary branches. In the visual branch, it extracts the features using attention pooling. In the coordinate branch, it considers the configuration of the players and extracts the spatial information from the box coordinates. Moreover, we analyzed the Volleyball dataset that the recent literature is mostly based on, and systematically reannotated it to emphasize the group concept. Experimental results demonstrated the effectiveness of the proposed model DECOMPL, which delivered the best/second best GAR performance with the reannotations/original annotations among the comparable state-of-the-art methods. Code and new annotations are available at GitHub: https://github.com/berkerdemirel/decompl' article_processing_charge: No arxiv: 1 author: - first_name: Berker full_name: Demirel, Berker id: 8b4bc47f-3200-11ee-973b-8f0e7be21a9f last_name: Demirel - first_name: Huseyin full_name: Ozkan, Huseyin last_name: Ozkan citation: ama: 'Demirel B, Ozkan H. Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image. In: 2024 IEEE International Conference on Image Processing. IEEE; 2024:977-983. doi:10.1109/icip51287.2024.10647499' apa: 'Demirel, B., & Ozkan, H. (2024). Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image. In 2024 IEEE International Conference on Image Processing (pp. 977–983). Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/icip51287.2024.10647499' chicago: 'Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image.” In 2024 IEEE International Conference on Image Processing, 977–83. IEEE, 2024. https://doi.org/10.1109/icip51287.2024.10647499.' ieee: 'B. Demirel and H. Ozkan, “Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image,” in 2024 IEEE International Conference on Image Processing, Abu Dhabi, United Arab Emirates, 2024, pp. 977–983.' ista: 'Demirel B, Ozkan H. 2024. Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image. 2024 IEEE International Conference on Image Processing. ICIP: International Conference on Image Processing, 977–983.' mla: 'Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image.” 2024 IEEE International Conference on Image Processing, IEEE, 2024, pp. 977–83, doi:10.1109/icip51287.2024.10647499.' short: B. Demirel, H. Ozkan, in:, 2024 IEEE International Conference on Image Processing, IEEE, 2024, pp. 977–983. conference: end_date: 2024-10-30 location: Abu Dhabi, United Arab Emirates name: 'ICIP: International Conference on Image Processing' start_date: 2024-10-27 corr_author: '1' date_created: 2025-01-29T12:22:24Z date_published: 2024-11-01T00:00:00Z date_updated: 2025-09-09T12:13:12Z day: '01' department: - _id: FrLo doi: 10.1109/icip51287.2024.10647499 external_id: arxiv: - '2303.06439' isi: - '001442947000143' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2303.06439 month: '11' oa: 1 oa_version: Preprint page: 977-983 publication: 2024 IEEE International Conference on Image Processing publication_identifier: eisbn: - '9798350349399' eissn: - 2381-8549 publication_status: published publisher: IEEE quality_controlled: '1' related_material: link: - relation: software url: https://github.com/berkerdemirel/decompl status: public title: 'Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: repository OA_type: hybrid _id: '18957' abstract: - lang: eng text: Sui Lutris is the first smart-contract platform to sustainably achieve sub-second finality. It achieves this significant decrease by employing consensusless agreement not only for simple payments but for a large variety of transactions. Unlike prior work, Sui Lutris neither compromises expressiveness nor throughput and can run perpetually without restarts. Sui Lutris achieves this by safely integrating consensuless agreement with a high-throughput consensus protocol that is invoked out of the critical finality path but ensures that when a transaction is at risk of inconsistent concurrent accesses, its settlement is delayed until the total ordering is resolved. Building such a hybrid architecture is especially delicate during reconfiguration events, where the system needs to preserve the safety of the consensusless path without compromising the long-term liveness of potentially misconfigured clients. We thus develop a novel reconfiguration protocol, the first to provably show the safe and efficient reconfiguration of a consensusless blockchain. Sui Lutris is currently running in production and underpins the Sui smart-contract platform. Combined with the use of Objects instead of accounts it enables the safe execution of smart contracts that expose objects as a first-class resource. In our experiments Sui Lutris achieves latency lower than 0.5 seconds for throughput up to 5,000 certificates per second (150k ops/s with transaction blocks), compared to the state-of-the-art real-world consensus latencies of 3 seconds. Furthermore, it gracefully handles validators crash-recovery and does not suffer visible performance degradation during reconfiguration. acknowledgement: 'This work is funded by MystenLabs. We thank the Mysten Labs Engineering teams for valuable feedback broadly, and specifically Dmitry Perelman and Todd Fiala for managing the implementation effort. A number of folks contributed to specific aspects of the implementation of Sui Lutris (amongst many other contributions to the overall blockchain): Francois Garillot, Laura Makdah, Mingwei Tian, Andrew Schran, Sadhan Sood and William Smith implemented and optimized aspects of both Sui Lutris and Narwhal / Bullshark consensus; Alonso de Gortari oversaw the cryptoeconomics of the blockchain, and Emma Zhong, Ade Adepoju, Tim Zakia and Dario Russi designed and implemented staking and gas mechanisms. Adam Welc designed several Move tools and provided great feedback on the manuscript. We also extend our thanks to Patrick Kuo, Ge Gao, Chris Li, and Arun Koshy for their work on the Sui Lutris SDK, clients, and RPC layer; Kostas Chalkias, Jonas Lindstrøm, and Joy Wang built cryptographic components.' article_processing_charge: No arxiv: 1 author: - first_name: Sam full_name: Blackshear, Sam last_name: Blackshear - first_name: Andrey full_name: Chursin, Andrey last_name: Chursin - first_name: George full_name: Danezis, George last_name: Danezis - first_name: Anastasios full_name: Kichidis, Anastasios last_name: Kichidis - first_name: Eleftherios full_name: Kokoris Kogias, Eleftherios id: f5983044-d7ef-11ea-ac6d-fd1430a26d30 last_name: Kokoris Kogias - first_name: Xun full_name: Li, Xun last_name: Li - first_name: Mark full_name: Logan, Mark last_name: Logan - first_name: Ashok full_name: Menon, Ashok last_name: Menon - first_name: Todd full_name: Nowacki, Todd last_name: Nowacki - first_name: Alberto full_name: Sonnino, Alberto last_name: Sonnino - first_name: Brandon full_name: Williams, Brandon last_name: Williams - first_name: Lu full_name: Zhang, Lu last_name: Zhang citation: ama: 'Blackshear S, Chursin A, Danezis G, et al. Sui Lutris: A blockchain combining broadcast and consensus. In: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security. ACM; 2024:2606-2620. doi:10.1145/3658644.3670286' apa: 'Blackshear, S., Chursin, A., Danezis, G., Kichidis, A., Kokoris Kogias, E., Li, X., … Zhang, L. (2024). Sui Lutris: A blockchain combining broadcast and consensus. In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security (pp. 2606–2620). Salt Lake City, UT, United States: ACM. https://doi.org/10.1145/3658644.3670286' chicago: 'Blackshear, Sam, Andrey Chursin, George Danezis, Anastasios Kichidis, Eleftherios Kokoris Kogias, Xun Li, Mark Logan, et al. “Sui Lutris: A Blockchain Combining Broadcast and Consensus.” In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2606–20. ACM, 2024. https://doi.org/10.1145/3658644.3670286.' ieee: 'S. Blackshear et al., “Sui Lutris: A blockchain combining broadcast and consensus,” in Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, Salt Lake City, UT, United States, 2024, pp. 2606–2620.' ista: 'Blackshear S, Chursin A, Danezis G, Kichidis A, Kokoris Kogias E, Li X, Logan M, Menon A, Nowacki T, Sonnino A, Williams B, Zhang L. 2024. Sui Lutris: A blockchain combining broadcast and consensus. Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security. CCS: Conference on Computer and Communications Security, 2606–2620.' mla: 'Blackshear, Sam, et al. “Sui Lutris: A Blockchain Combining Broadcast and Consensus.” Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, ACM, 2024, pp. 2606–20, doi:10.1145/3658644.3670286.' short: S. Blackshear, A. Chursin, G. Danezis, A. Kichidis, E. Kokoris Kogias, X. Li, M. Logan, A. Menon, T. Nowacki, A. Sonnino, B. Williams, L. Zhang, in:, Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, ACM, 2024, pp. 2606–2620. conference: end_date: 2024-10-18 location: Salt Lake City, UT, United States name: 'CCS: Conference on Computer and Communications Security' start_date: 2024-10-14 date_created: 2025-01-29T12:42:21Z date_published: 2024-12-09T00:00:00Z date_updated: 2025-09-09T12:13:55Z day: '09' department: - _id: ElKo doi: 10.1145/3658644.3670286 external_id: arxiv: - '2310.18042' isi: - '001436367300178' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2310.18042 month: '12' oa: 1 oa_version: Preprint page: 2606-2620 publication: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security publication_identifier: isbn: - '9798400706363' publication_status: published publisher: ACM quality_controlled: '1' status: public title: 'Sui Lutris: A blockchain combining broadcast and consensus' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- _id: '18958' abstract: - lang: eng text: This workshop brought together experts on the analysis of quantum many-body problems and quantum statistical mechanics, with the goal of discussing the state-of-the-art of the field, recent developments as well as challenges for the future. The main topics of discussion concerned the equilibrium and dynamical behavior of (bosonic or fermionic) quantum gases, quantum spin systems, as well as quantum field theory models like the Nelson or Fröhlich model. acknowledgement: The MFO and the workshop organizers would like to thank the National Science Foundation for supporting the participation of junior researchers in the workshop by the grant DMS-2230648, “US Junior Oberwolfach Fellows”. article_processing_charge: No article_type: original author: - first_name: Christian full_name: Hainzl, Christian last_name: Hainzl - first_name: Benjamin full_name: Schlein, Benjamin last_name: Schlein - first_name: Robert full_name: Seiringer, Robert id: 4AFD0470-F248-11E8-B48F-1D18A9856A87 last_name: Seiringer orcid: 0000-0002-6781-0521 - first_name: Simone full_name: Warzel, Simone last_name: Warzel citation: ama: Hainzl C, Schlein B, Seiringer R, Warzel S. Many-body quantum systems. Oberwolfach Reports. 2024;20(3):2247-2302. doi:10.4171/owr/2023/39 apa: Hainzl, C., Schlein, B., Seiringer, R., & Warzel, S. (2024). Many-body quantum systems. Oberwolfach Reports. EMS Press. https://doi.org/10.4171/owr/2023/39 chicago: Hainzl, Christian, Benjamin Schlein, Robert Seiringer, and Simone Warzel. “Many-Body Quantum Systems.” Oberwolfach Reports. EMS Press, 2024. https://doi.org/10.4171/owr/2023/39. ieee: C. Hainzl, B. Schlein, R. Seiringer, and S. Warzel, “Many-body quantum systems,” Oberwolfach Reports, vol. 20, no. 3. EMS Press, pp. 2247–2302, 2024. ista: Hainzl C, Schlein B, Seiringer R, Warzel S. 2024. Many-body quantum systems. Oberwolfach Reports. 20(3), 2247–2302. mla: Hainzl, Christian, et al. “Many-Body Quantum Systems.” Oberwolfach Reports, vol. 20, no. 3, EMS Press, 2024, pp. 2247–302, doi:10.4171/owr/2023/39. short: C. Hainzl, B. Schlein, R. Seiringer, S. Warzel, Oberwolfach Reports 20 (2024) 2247–2302. date_created: 2025-01-29T13:09:28Z date_published: 2024-04-18T00:00:00Z date_updated: 2025-01-29T13:19:06Z day: '18' department: - _id: RoSe doi: 10.4171/owr/2023/39 intvolume: ' 20' issue: '3' language: - iso: eng month: '04' oa_version: None page: 2247-2302 publication: Oberwolfach Reports publication_identifier: eissn: - 1660-8941 issn: - 1660-8933 publication_status: published publisher: EMS Press quality_controlled: '1' status: public title: Many-body quantum systems type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 20 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18961' abstract: - lang: eng text: "Automated contact tracing (ACT) emerged as a promising measure to curb the spread of Covid-19. Users enable ACT on their smartphones to automatically record contacts with other users. If a user tests positive for the disease, they report their diagnosis to alert their contacts.\r\nDesigning effective ACT protocols is challenging since they need to be efficient and secure while also ensuring users' privacy. As ACT protocols necessarily leak some information by design, defining privacy is difficult. For example, a user cannot deny having met another user. Ideally, however, the user can plausibly deny everything else, in particular, when they met. We call this privacy property contact-time deniability.\r\nWhile some early works discussed contact-time deniability informally, it has received little attention since then. We investigate deniability from a rigorous, theoretical point of view and arrive at the following impossibility result:\r\nA decentralized protocol with unidirectional communication cannot be contact-time deniable and replay-secure. This holds even if malicious users treat smartphones as black-boxes.\r\n Unidirectional protocols are usually very efficient and many proposals are unidirectional, e.g., the widely-deployed Google-Apple Exposure Notifications. So the impossibility result considerably constrains the design space of efficient, secure, and private ACT protocols. However, it can also be used as a guide; we discuss several possibilities to achieve contact-time deniability in practice." acknowledgement: "We thank Raluca-Georgia Diugan for her initial contributions and support afterward.\r\nThis research was funded in whole or in part by the Austrian Science Fund (FWF) 10.55776/F85." article_processing_charge: No article_type: original author: - first_name: Christoph Ullrich full_name: Günther, Christoph Ullrich id: ec98511c-eb8e-11eb-b029-edd25d7271a1 last_name: Günther - first_name: Krzysztof Z full_name: Pietrzak, Krzysztof Z id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87 last_name: Pietrzak orcid: 0000-0002-9139-1654 citation: ama: 'Günther CU, Pietrzak KZ. Deniability in automated contact tracing: Impossibilities and possibilities. Proceedings on Privacy Enhancing Technologies. 2024;2024(4):636-648. doi:10.56553/popets-2024-0134' apa: 'Günther, C. U., & Pietrzak, K. Z. (2024). Deniability in automated contact tracing: Impossibilities and possibilities. Proceedings on Privacy Enhancing Technologies. Bristol, UK/Virtual: Privacy Enhancing Technologies Symposium Advisory Board. https://doi.org/10.56553/popets-2024-0134' chicago: 'Günther, Christoph Ullrich, and Krzysztof Z Pietrzak. “Deniability in Automated Contact Tracing: Impossibilities and Possibilities.” Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Advisory Board, 2024. https://doi.org/10.56553/popets-2024-0134.' ieee: 'C. U. Günther and K. Z. Pietrzak, “Deniability in automated contact tracing: Impossibilities and possibilities,” Proceedings on Privacy Enhancing Technologies, vol. 2024, no. 4. Privacy Enhancing Technologies Symposium Advisory Board, pp. 636–648, 2024.' ista: 'Günther CU, Pietrzak KZ. 2024. Deniability in automated contact tracing: Impossibilities and possibilities. Proceedings on Privacy Enhancing Technologies. 2024(4), 636–648.' mla: 'Günther, Christoph Ullrich, and Krzysztof Z. Pietrzak. “Deniability in Automated Contact Tracing: Impossibilities and Possibilities.” Proceedings on Privacy Enhancing Technologies, vol. 2024, no. 4, Privacy Enhancing Technologies Symposium Advisory Board, 2024, pp. 636–48, doi:10.56553/popets-2024-0134.' short: C.U. Günther, K.Z. Pietrzak, Proceedings on Privacy Enhancing Technologies 2024 (2024) 636–648. conference: end_date: 2024-07-20 location: Bristol, UK/Virtual name: 'PETs: Privacy Enhancing Technologies Symposium ' start_date: 2024-07-15 corr_author: '1' date_created: 2025-01-29T13:39:34Z date_published: 2024-07-01T00:00:00Z date_updated: 2025-04-15T08:16:04Z day: '01' ddc: - '000' department: - _id: KrPi - _id: GradSch doi: 10.56553/popets-2024-0134 file: - access_level: open_access checksum: 348ed6adcf6ad2f925227bde1758cae6 content_type: application/pdf creator: dernst date_created: 2025-01-29T13:44:47Z date_updated: 2025-01-29T13:44:47Z file_id: '18962' file_name: 2024_ProcPrivacyEnhTech_Guenther.pdf file_size: 611567 relation: main_file success: 1 file_date_updated: 2025-01-29T13:44:47Z has_accepted_license: '1' intvolume: ' 2024' issue: '4' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 636-648 project: - _id: 34a34d57-11ca-11ed-8bc3-a2688a8724e1 grant_number: F8509 name: Security and Privacy by Design for Complex Systems publication: Proceedings on Privacy Enhancing Technologies publication_identifier: issn: - 2299-0984 publication_status: published publisher: Privacy Enhancing Technologies Symposium Advisory Board quality_controlled: '1' status: public title: 'Deniability in automated contact tracing: Impossibilities and possibilities' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2024 year: '2024' ... --- OA_place: repository OA_type: green _id: '18964' abstract: - lang: eng text: Object-centric learning (OCL) extracts the representation of objects with slots, offering an exceptional blend of flexibility and interpretability for abstracting low-level perceptual features. A widely adopted method within OCL is slot attention, which utilizes attention mechanisms to iteratively refine slot representations. However, a major draw-back of most object-centric models, including slot attention, is their reliance on predefining the number of slots. This not only necessitates prior knowledge of the dataset but also overlooks the inherent variability in the number of objects present in each instance. To overcome this fundamental limitation, we present a novel complexity-aware object auto-encoder framework. Within this framework, we introduce an adaptive slot attention (AdaSlot) mecha-nism that dynamically determines the optimal number of slots based on the content of the data. This is achieved by proposing a discrete slot sampling module that is responsible for selecting an appropriate number of slots from a candidate list. Furthermore, we introduce a masked slot decoder that suppresses unselected slots during the decoding process. Our framework, tested extensively on object discovery tasks with various datasets, shows performance matching or exceeding top fixed-slot models. Moreover, our analysis substantiates that our method exhibits the capability to dynamically adapt the slot number according to each instance's complexity, offering the potential for further exploration in slot attention research. Project will be available at https://kfan21.github.io/AdaSlot/ acknowledgement: Yanwei Fu is the corresponding authour. Yanwei Fu is with School of Data Science, Fudan University, Shanghai Key Lab of Intelligent Information Processing, Fudan University, and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China. article_processing_charge: No arxiv: 1 author: - first_name: Ke full_name: Fan, Ke last_name: Fan - first_name: Zechen full_name: Bai, Zechen last_name: Bai - first_name: Tianjun full_name: Xiao, Tianjun last_name: Xiao - first_name: Tong full_name: He, Tong last_name: He - first_name: Max full_name: Horn, Max last_name: Horn - first_name: Yanwei full_name: Fu, Yanwei last_name: Fu - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Zheng full_name: Zhang, Zheng last_name: Zhang citation: ama: 'Fan K, Bai Z, Xiao T, et al. Adaptive slot attention: Object discovery with dynamic slot number. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2024. doi:10.1109/cvpr52733.2024.02176' apa: 'Fan, K., Bai, Z., Xiao, T., He, T., Horn, M., Fu, Y., … Zhang, Z. (2024). Adaptive slot attention: Object discovery with dynamic slot number. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, United States: IEEE. https://doi.org/10.1109/cvpr52733.2024.02176' chicago: 'Fan, Ke, Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco Locatello, and Zheng Zhang. “Adaptive Slot Attention: Object Discovery with Dynamic Slot Number.” In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2024. https://doi.org/10.1109/cvpr52733.2024.02176.' ieee: 'K. Fan et al., “Adaptive slot attention: Object discovery with dynamic slot number,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, United States, 2024.' ista: 'Fan K, Bai Z, Xiao T, He T, Horn M, Fu Y, Locatello F, Zhang Z. 2024. Adaptive slot attention: Object discovery with dynamic slot number. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition.' mla: 'Fan, Ke, et al. “Adaptive Slot Attention: Object Discovery with Dynamic Slot Number.” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2024, doi:10.1109/cvpr52733.2024.02176.' short: K. Fan, Z. Bai, T. Xiao, T. He, M. Horn, Y. Fu, F. Locatello, Z. Zhang, in:, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2024. conference: end_date: 2024-06-22 location: Seattle, WA, United States name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2024-06-16 date_created: 2025-01-29T14:27:39Z date_published: 2024-06-15T00:00:00Z date_updated: 2025-09-09T12:15:17Z day: '15' department: - _id: FrLo doi: 10.1109/cvpr52733.2024.02176 external_id: arxiv: - '2406.09196' isi: - '001342515506043' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2406.09196 month: '06' oa: 1 oa_version: Preprint publication: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition publication_identifier: eisbn: - '9798350353006' publication_status: published publisher: IEEE quality_controlled: '1' related_material: link: - relation: software url: https://kfan21.github.io/AdaSlot/ status: public title: 'Adaptive slot attention: Object discovery with dynamic slot number' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '18967' abstract: - lang: eng text: "Background: We identified small molecule tricyclic pyrone compound CP2 as a mild mitochondrial complex I (MCI) inhibitor that induces neuroprotection in multiple mouse models of AD. One of the major concerns while targeting mitochondria is the production of reactive oxygen species (ROS). CP2 consists of two diastereoisomers, D1 and D2, with distinct activity and toxicity profiles. This study was designed to understand how structure of D1 and D2 affects their binding to MCI and the consequential impact on ROS production.\r\n\r\nMethod: The X-ray crystallography and cryo-electron microscopy (cryo-EM) at global resolution of 3.25-3.27Å were employed to identify the molecular structure of D1 and D2 and the D1 binding to the isolated ovine MCI. The assessment of the MCI inhibition and the extent of ROS generation were done in isolated MCI and human neuroblastoma MC65 cells using flow cytometry, a Seahorse extracellular flux analyzer, and the kinetic studies.\r\n\r\nResult: In the closed conformation of MCI, D1 selectively binds to the deep Quinone-site (Qd) but not to the shallow Q-site (Qs), sharing the same binding pocket as rotenone. In the open MCI state, D1 exclusively binds to the Qs in contrast to rotenone, which binds Qd and Qs in both closed and open states. At the same concentrations, D1 inhibits respiration to a greater extent compared to D2 (5:1 ratio) and produces higher level of ROS.\r\n\r\nConclusion:Cryo-EM unambiguously identified binding of D1 to both the Qd and Qs sites, contingent upon the conformational state of MCI. In contrast to rotenone, D1 binds Qd only in the closed conformation during catalytic cycle, leading to mild inhibition. Superimposing X-ray crystallography data of D1 and D2 onto cryo-EM data suggests that the orientation of the methyl group in D2 induces a flatter conformation, resulting in lower binding affinity to MCI, which correlates with lower inhibition and toxicity compared to D1. At physiologically relevant concentrations, CP2 (D1:D2 = 1:1) demonstrates low MCI inhibition yielding negligible ROS levels. This observation provides new insight into the absence of toxicity associated with CP2 treatment in vivo, further highlighting feasibility for the development of safe and efficacious MCI inhibitors." article_number: e085971 article_processing_charge: Yes (in subscription journal) author: - first_name: Olga full_name: Petrova, Olga id: 5D8C9660-5D49-11EA-8188-567B3DDC885E last_name: Petrova - first_name: Sergey A full_name: Trushin, Sergey A last_name: Trushin - first_name: Thi Kim Oanh full_name: Nguyen, Thi Kim Oanh last_name: Nguyen - first_name: Mark full_name: Ostroot, Mark last_name: Ostroot - first_name: Matthew full_name: Schellenberg, Matthew last_name: Schellenberg - first_name: Graham full_name: Johnson, Graham last_name: Johnson - first_name: Eugenia full_name: Trushina, Eugenia last_name: Trushina - first_name: Leonid A full_name: Sazanov, Leonid A id: 338D39FE-F248-11E8-B48F-1D18A9856A87 last_name: Sazanov orcid: 0000-0002-0977-7989 citation: ama: Petrova O, Trushin SA, Nguyen TKO, et al. Structure‐activity Relationship Study of Neuroprotective Complex I Inhibitor CP2. Vol 20. Wiley; 2024. doi:10.1002/alz.085971 apa: Petrova, O., Trushin, S. A., Nguyen, T. K. O., Ostroot, M., Schellenberg, M., Johnson, G., … Sazanov, L. A. (2024). Structure‐activity relationship study of neuroprotective complex I inhibitor CP2. Alzheimer’s & Dementia (Vol. 20). Wiley. https://doi.org/10.1002/alz.085971 chicago: Petrova, Olga, Sergey A Trushin, Thi Kim Oanh Nguyen, Mark Ostroot, Matthew Schellenberg, Graham Johnson, Eugenia Trushina, and Leonid A Sazanov. Structure‐activity Relationship Study of Neuroprotective Complex I Inhibitor CP2. Alzheimer’s & Dementia. Vol. 20. Wiley, 2024. https://doi.org/10.1002/alz.085971. ieee: O. Petrova et al., Structure‐activity relationship study of neuroprotective complex I inhibitor CP2, vol. 20, no. S6. Wiley, 2024. ista: Petrova O, Trushin SA, Nguyen TKO, Ostroot M, Schellenberg M, Johnson G, Trushina E, Sazanov LA. 2024. Structure‐activity relationship study of neuroprotective complex I inhibitor CP2, Wiley,p. mla: Petrova, Olga, et al. “Structure‐activity Relationship Study of Neuroprotective Complex I Inhibitor CP2.” Alzheimer’s & Dementia, vol. 20, no. S6, e085971, Wiley, 2024, doi:10.1002/alz.085971. short: O. Petrova, S.A. Trushin, T.K.O. Nguyen, M. Ostroot, M. Schellenberg, G. Johnson, E. Trushina, L.A. Sazanov, Structure‐activity Relationship Study of Neuroprotective Complex I Inhibitor CP2, Wiley, 2024. date_created: 2025-01-29T15:21:40Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-01-29T15:29:22Z day: '01' ddc: - '570' department: - _id: LeSa doi: 10.1002/alz.085971 file: - access_level: open_access checksum: e914bd5f3a701659ab79d497a122811f content_type: application/pdf creator: dernst date_created: 2025-01-29T15:24:50Z date_updated: 2025-01-29T15:24:50Z file_id: '18968' file_name: 2024_AlzheimerDementia_Petrova.pdf file_size: 70870 relation: main_file success: 1 file_date_updated: 2025-01-29T15:24:50Z has_accepted_license: '1' intvolume: ' 20' issue: S6 language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: Alzheimer's & Dementia publication_identifier: eissn: - 1552-5279 issn: - 1552-5260 publication_status: published publisher: Wiley quality_controlled: '1' status: public title: Structure‐activity relationship study of neuroprotective complex I inhibitor CP2 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: other_academic_publication user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 20 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '18970' abstract: - lang: eng text: Given a smooth projective curve C, nonabelian Hodge theory gives a diffeomorphism between two different moduli spaces associated to C. The first is the moduli space of Higgs bundles on C of rank n, which is equipped with the structure of an algebraic completely integrable Hamiltonian system. The second is the character variety of representations of the fundamental group of C into GL(n). In 2012, de Cataldo, Hausel, and Migliorini [1] proposed the P=W conjecture which identifies the perverse filtration on the cohomology of the Higgs moduli space with the weight filtration on the cohomology of the character variety. Recently, in 2022, two independent proofs of the P=W Conjecture appeared, in work of Maulik &Shen [2] and Hausel, Mellit, Minets &Schiffmann [6]. The aim of the Arbeitsgemeinschaft was to understand the P=W Conjecture and these two recent proofs. acknowledgement: "The MFO and the workshop organizers would like to thank the\r\nNational Science Foundation for supporting the participation of junior researchers\r\nby the grant DMS-2230648, “US Junior Oberwolfach Fellows”. Moreover, the\r\nMFO and the workshop organizers would like to thank the Oberwolfach Foundation for supporting the participation of junior researchers in the Arbeitsgemeinschaft." article_processing_charge: No article_type: original author: - first_name: Tamás full_name: Hausel, Tamás id: 4A0666D8-F248-11E8-B48F-1D18A9856A87 last_name: Hausel orcid: 0000-0002-9582-2634 - first_name: Davesh full_name: Maulik, Davesh last_name: Maulik - first_name: Anton full_name: Mellit, Anton last_name: Mellit - first_name: Olivier full_name: Schiffmann, Olivier last_name: Schiffmann - first_name: Junliang full_name: Shen, Junliang last_name: Shen citation: ama: 'Hausel T, Maulik D, Mellit A, Schiffmann O, Shen J. Arbeitsgemeinschaft: Geometry and representation theory around the P=W conjecture. Oberwolfach Reports. 2024;21(2):949-1004. doi:10.4171/owr/2024/16' apa: 'Hausel, T., Maulik, D., Mellit, A., Schiffmann, O., & Shen, J. (2024). Arbeitsgemeinschaft: Geometry and representation theory around the P=W conjecture. Oberwolfach Reports. EMS Press. https://doi.org/10.4171/owr/2024/16' chicago: 'Hausel, Tamás, Davesh Maulik, Anton Mellit, Olivier Schiffmann, and Junliang Shen. “Arbeitsgemeinschaft: Geometry and Representation Theory around the P=W Conjecture.” Oberwolfach Reports. EMS Press, 2024. https://doi.org/10.4171/owr/2024/16.' ieee: 'T. Hausel, D. Maulik, A. Mellit, O. Schiffmann, and J. Shen, “Arbeitsgemeinschaft: Geometry and representation theory around the P=W conjecture,” Oberwolfach Reports, vol. 21, no. 2. EMS Press, pp. 949–1004, 2024.' ista: 'Hausel T, Maulik D, Mellit A, Schiffmann O, Shen J. 2024. Arbeitsgemeinschaft: Geometry and representation theory around the P=W conjecture. Oberwolfach Reports. 21(2), 949–1004.' mla: 'Hausel, Tamás, et al. “Arbeitsgemeinschaft: Geometry and Representation Theory around the P=W Conjecture.” Oberwolfach Reports, vol. 21, no. 2, EMS Press, 2024, pp. 949–1004, doi:10.4171/owr/2024/16.' short: T. Hausel, D. Maulik, A. Mellit, O. Schiffmann, J. Shen, Oberwolfach Reports 21 (2024) 949–1004. date_created: 2025-01-29T15:34:22Z date_published: 2024-05-05T00:00:00Z date_updated: 2025-01-29T15:39:55Z day: '05' ddc: - '500' department: - _id: TaHa doi: 10.4171/owr/2024/16 has_accepted_license: '1' intvolume: ' 21' issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.4171/owr/2024/16 month: '05' oa: 1 oa_version: Published Version page: 949-1004 publication: Oberwolfach Reports publication_identifier: eissn: - 1660-8941 issn: - 1660-8933 publication_status: published publisher: EMS Press quality_controlled: '1' status: public title: 'Arbeitsgemeinschaft: Geometry and representation theory around the P=W conjecture' tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 21 year: '2024' ... --- OA_place: repository OA_type: green _id: '18971' abstract: - lang: eng text: 'Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases. Addressing this challenge typically involves methods relying on prior knowledge and group annotation to remove spurious correlations, which may not be readily available in many applications. In this paper, we establish a novel connection between unsupervised object-centric learning and mitigation of spurious correlations. Instead of directly inferring subgroups with varying correlations with labels, our approach focuses on discovering concepts: discrete ideas that are shared across input samples. Leveraging existing object-centric representation learning, we introduce CoBalT: a concept balancing technique that effectively mitigates spurious correlations without requiring human labeling of subgroups. Evaluation across the benchmark datasets for sub-population shifts demonstrate superior or competitive performance compared state-of-the-art baselines, without the need for group annotation. Code is available at https://github.com/rarefin/CoBalT' acknowledgement: "We acknowledge the support of the Canada CIFAR AI Chair Program and IVADO. We thank Mila and Compute Canada for providing computational resources.\r\n" alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Rifat full_name: Arefin, Rifat last_name: Arefin - first_name: Yan full_name: Zhang, Yan last_name: Zhang - first_name: Aristide full_name: Baratin, Aristide last_name: Baratin - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Irina full_name: Rish, Irina last_name: Rish - first_name: Dianbo full_name: Liu, Dianbo last_name: Liu - first_name: Kenji full_name: Kawaguchi, Kenji last_name: Kawaguchi citation: ama: 'Arefin R, Zhang Y, Baratin A, et al. Unsupervised concept discovery mitigates spurious correlations. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:1672-1688.' apa: 'Arefin, R., Zhang, Y., Baratin, A., Locatello, F., Rish, I., Liu, D., & Kawaguchi, K. (2024). Unsupervised concept discovery mitigates spurious correlations. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 1672–1688). Vienna, Austria: ML Research Press.' chicago: Arefin, Rifat, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, and Kenji Kawaguchi. “Unsupervised Concept Discovery Mitigates Spurious Correlations.” In Proceedings of the 41st International Conference on Machine Learning, 235:1672–88. ML Research Press, 2024. ieee: R. Arefin et al., “Unsupervised concept discovery mitigates spurious correlations,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 1672–1688. ista: 'Arefin R, Zhang Y, Baratin A, Locatello F, Rish I, Liu D, Kawaguchi K. 2024. Unsupervised concept discovery mitigates spurious correlations. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 1672–1688.' mla: Arefin, Rifat, et al. “Unsupervised Concept Discovery Mitigates Spurious Correlations.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 1672–88. short: R. Arefin, Y. Zhang, A. Baratin, F. Locatello, I. Rish, D. Liu, K. Kawaguchi, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 1672–1688. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 date_created: 2025-01-30T07:21:57Z date_published: 2024-07-30T00:00:00Z date_updated: 2025-01-30T07:23:10Z day: '30' department: - _id: FrLo external_id: arxiv: - '2402.13368' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2402.13368 month: '07' oa: 1 oa_version: Preprint page: 1672-1688 publication: Proceedings of the 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: link: - relation: software url: https://github.com/rarefin/CoBalT scopus_import: '1' status: public title: Unsupervised concept discovery mitigates spurious correlations type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 235 year: '2024' ... --- OA_place: repository OA_type: green _id: '18972' abstract: - lang: eng text: 'Deep learning models are known to overfit and memorize spurious features in the training dataset. While numerous empirical studies have aimed at understanding this phenomenon, a rigorous theoretical framework to quantify it is still missing. In this paper, we consider spurious features that are uncorrelated with the learning task, and we provide a precise characterization of how they are memorized via two separate terms: (i) the stability of the model with respect to individual training samples, and (ii) the feature alignment between the spurious pattern and the full sample. While the first term is well established in learning theory and it is connected to the generalization error in classical work, the second one is, to the best of our knowledge, novel. Our key technical result gives a precise characterization of the feature alignment for the two prototypical settings of random features (RF) and neural tangent kernel (NTK) regression. We prove that the memorization of spurious features weakens as the generalization capability increases and, through the analysis of the feature alignment, we unveil the role of the model and of its activation function. Numerical experiments show the predictive power of our theory on standard datasets (MNIST, CIFAR-10).' acknowledgement: "The authors were partially supported by the 2019 LopezLoreta prize, and they would like to thank (in alphabetical order) Grigorios Chrysos, Simone Maria Giancola, Mahyar\r\nJafari Nodeh, Christoph Lampert, Marco Miani, GuanWen Qiu, and Peter Sukenık for helpful discussions." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Simone full_name: Bombari, Simone id: ca726dda-de17-11ea-bc14-f9da834f63aa last_name: Bombari - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: ama: 'Bombari S, Mondelli M. How spurious features are memorized: Precise analysis for random and NTK features. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:4267-4299.' apa: 'Bombari, S., & Mondelli, M. (2024). How spurious features are memorized: Precise analysis for random and NTK features. In 41st International Conference on Machine Learning (Vol. 235, pp. 4267–4299). Vienna, Austria: ML Research Press.' chicago: 'Bombari, Simone, and Marco Mondelli. “How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features.” In 41st International Conference on Machine Learning, 235:4267–99. ML Research Press, 2024.' ieee: 'S. Bombari and M. Mondelli, “How spurious features are memorized: Precise analysis for random and NTK features,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 4267–4299.' ista: 'Bombari S, Mondelli M. 2024. How spurious features are memorized: Precise analysis for random and NTK features. 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 4267–4299.' mla: 'Bombari, Simone, and Marco Mondelli. “How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 4267–99.' short: S. Bombari, M. Mondelli, in:, 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 4267–4299. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' date_created: 2025-01-30T07:29:47Z date_published: 2024-07-30T00:00:00Z date_updated: 2025-04-15T07:50:12Z day: '30' department: - _id: MaMo external_id: arxiv: - '2305.12100' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2305.12100 month: '07' oa: 1 oa_version: Preprint page: 4267-4299 project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'How spurious features are memorized: Precise analysis for random and NTK features' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 235 year: '2024' ... --- OA_place: repository OA_type: green _id: '18973' abstract: - lang: eng text: 'Understanding the reasons behind the exceptional success of transformers requires a better analysis of why attention layers are suitable for NLP tasks. In particular, such tasks require predictive models to capture contextual meaning which often depends on one or few words, even if the sentence is long. Our work studies this key property, dubbed word sensitivity (WS), in the prototypical setting of random features. We show that attention layers enjoy high WS, namely, there exists a vector in the space of embeddings that largely perturbs the random attention features map. The argument critically exploits the role of the softmax in the attention layer, highlighting its benefit compared to other activations (e.g., ReLU). In contrast, the WS of standard random features is of order 1/n−−√, n being the number of words in the textual sample, and thus it decays with the length of the context. We then translate these results on the word sensitivity into generalization bounds: due to their low WS, random features provably cannot learn to distinguish between two sentences that differ only in a single word; in contrast, due to their high WS, random attention features have higher generalization capabilities. We validate our theoretical results with experimental evidence over the BERT-Base word embeddings of the imdb review dataset.' acknowledgement: The authors were partially supported by the 2019 LopezLoreta prize, and they would like to thank Mohammad Hossein Amani, Lorenzo Beretta, and Clement Rebuffel for helpful discussions. alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Simone full_name: Bombari, Simone id: ca726dda-de17-11ea-bc14-f9da834f63aa last_name: Bombari - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: ama: 'Bombari S, Mondelli M. Towards understanding the word sensitivity of attention layers: A study via random features. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:4300-4328.' apa: 'Bombari, S., & Mondelli, M. (2024). Towards understanding the word sensitivity of attention layers: A study via random features. In 41st International Conference on Machine Learning (Vol. 235, pp. 4300–4328). Vienna, Austria: ML Research Press.' chicago: 'Bombari, Simone, and Marco Mondelli. “Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features.” In 41st International Conference on Machine Learning, 235:4300–4328. ML Research Press, 2024.' ieee: 'S. Bombari and M. Mondelli, “Towards understanding the word sensitivity of attention layers: A study via random features,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 4300–4328.' ista: 'Bombari S, Mondelli M. 2024. Towards understanding the word sensitivity of attention layers: A study via random features. 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 4300–4328.' mla: 'Bombari, Simone, and Marco Mondelli. “Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 4300–28.' short: S. Bombari, M. Mondelli, in:, 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 4300–4328. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' date_created: 2025-01-30T07:35:49Z date_published: 2024-07-30T00:00:00Z date_updated: 2025-04-15T07:50:12Z day: '30' department: - _id: MaMo external_id: arxiv: - '2402.02969' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2402.02969 month: '07' oa: 1 oa_version: Preprint page: 4300-4328 project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'Towards understanding the word sensitivity of attention layers: A study via random features' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 235 year: '2024' ... --- OA_place: publisher OA_type: green _id: '18974' abstract: - lang: eng text: Reinforcement Learning (RL) from temporal logical specifications is a fundamental problem in sequential decision making. One of the basic and core such specification is the reachability specification that requires a target set to be eventually visited. Despite strong empirical results for RL from such specifications, the theoretical guarantees are bleak, including the impossibility of Probably Approximately Correct (PAC) guarantee for reachability specifications. Given the impossibility result, in this work we consider the problem of RL from reachability specifications along with the information of expected conditional distance (ECD). We present (a) lower bound results which establish the necessity of ECD information for PAC guarantees and (b) an algorithm that establishes PAC-guarantees given the ECD information. To the best of our knowledge, this is the first RL from reachability specifications that does not make any assumptions on the underlying environment to learn policies. alternative_title: - PMLR article_processing_charge: No author: - first_name: Jakub full_name: Svoboda, Jakub id: 130759D2-D7DD-11E9-87D2-DE0DE6697425 last_name: Svoboda orcid: 0000-0002-1419-3267 - first_name: Suguman full_name: Bansal, Suguman last_name: Bansal - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X citation: ama: 'Svoboda J, Bansal S, Chatterjee K. Reinforcement learning from reachability specifications: PAC guarantees with expected conditional distance. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:47331-47344.' apa: 'Svoboda, J., Bansal, S., & Chatterjee, K. (2024). Reinforcement learning from reachability specifications: PAC guarantees with expected conditional distance. In 41st International Conference on Machine Learning (Vol. 235, pp. 47331–47344). Vienna, Austria: ML Research Press.' chicago: 'Svoboda, Jakub, Suguman Bansal, and Krishnendu Chatterjee. “Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance.” In 41st International Conference on Machine Learning, 235:47331–44. ML Research Press, 2024.' ieee: 'J. Svoboda, S. Bansal, and K. Chatterjee, “Reinforcement learning from reachability specifications: PAC guarantees with expected conditional distance,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 47331–47344.' ista: 'Svoboda J, Bansal S, Chatterjee K. 2024. Reinforcement learning from reachability specifications: PAC guarantees with expected conditional distance. 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 47331–47344.' mla: 'Svoboda, Jakub, et al. “Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 47331–44.' short: J. Svoboda, S. Bansal, K. Chatterjee, in:, 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 47331–47344. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' date_created: 2025-01-30T07:45:22Z date_published: 2024-07-29T00:00:00Z date_updated: 2025-01-30T07:46:16Z day: '29' department: - _id: KrCh intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://openreview.net/forum?id=mXUDDL4r1Q month: '07' oa: 1 oa_version: Preprint page: 47331-47344 publication: 41st International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'Reinforcement learning from reachability specifications: PAC guarantees with expected conditional distance' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 235 year: '2024' ... --- OA_place: repository OA_type: green _id: '18975' abstract: - lang: eng text: Leveraging second-order information about the loss at the scale of deep networks is one of the main lines of approach for improving the performance of current optimizers for deep learning. Yet, existing approaches for accurate full-matrix preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate Curvature (M-FAC) suffer from massive storage costs when applied even to small-scale models, as they must store a sliding window of gradients, whose memory requirements are multiplicative in the model dimension. In this paper, we address this issue via a novel and efficient error-feedback technique that can be applied to compress preconditioners by up to two orders of magnitude in practice, without loss of convergence. Specifically, our approach compresses the gradient information via sparsification or low-rank compression before it is fed into the preconditioner, feeding the compression error back into future iterations. Extensive experiments on deep neural networks show that this approach can compress full-matrix preconditioners to up to 99% sparsity without accuracy loss, effectively removing the memory overhead of fullmatrix preconditioners such as GGT and M-FAC. acknowledged_ssus: - _id: CampIT acknowledgement: The authors thank Adrian Vladu, Razvan Pascanu, Alexandra Peste, Mher Safaryan for their valuable feedback, the IT department from Institute of Science and Technology Austria for the hardware support and Weights and Biases for the infrastructure to track all our experiments. alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Ionut-Vlad full_name: Modoranu, Ionut-Vlad id: 449f7a18-f128-11eb-9611-9b430c0c6333 last_name: Modoranu - first_name: Aleksei full_name: Kalinov, Aleksei id: 44b7120e-eb97-11eb-a6c2-e1557aa81d02 last_name: Kalinov orcid: 0000-0003-2189-3904 - first_name: Eldar full_name: Kurtic, Eldar id: 47beb3a5-07b5-11eb-9b87-b108ec578218 last_name: Kurtic - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:35910-35933.' apa: 'Modoranu, I.-V., Kalinov, A., Kurtic, E., Frantar, E., & Alistarh, D.-A. (2024). Error feedback can accurately compress preconditioners. In 41st International Conference on Machine Learning (Vol. 235, pp. 35910–35933). Vienna, Austria: ML Research Press.' chicago: Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.” In 41st International Conference on Machine Learning, 235:35910–33. ML Research Press, 2024. ieee: I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, and D.-A. Alistarh, “Error feedback can accurately compress preconditioners,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 35910–35933. ista: 'Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. 2024. Error feedback can accurately compress preconditioners. 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 35910–35933.' mla: Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 35910–33. short: I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, D.-A. Alistarh, in:, 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 35910–35933. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' date_created: 2025-01-30T07:53:22Z date_published: 2024-07-30T00:00:00Z date_updated: 2025-01-30T07:54:16Z day: '30' department: - _id: DaAl external_id: arxiv: - '2306.06098' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2306.06098 month: '07' oa: 1 oa_version: Preprint page: 35910-35933 publication: 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Error feedback can accurately compress preconditioners type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 235 year: '2024' ... --- OA_place: repository OA_type: green _id: '18976' abstract: - lang: eng text: We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous setting, where each worker has its own computation and communication speeds, as well as data distribution. In these algorithms, workers compute possibly stale and stochastic gradients associated with their local data at some iteration back in history and then return those gradients to the server without synchronizing with other workers. We present a unified convergence theory for non-convex smooth functions in the heterogeneous regime. The proposed analysis provides convergence for pure asynchronous SGD and its various modifications. Moreover, our theory explains what affects the convergence rate and what can be done to improve the performance of asynchronous algorithms. In particular, we introduce a novel asynchronous method based on worker shuffling. As a by-product of our analysis, we also demonstrate convergence guarantees for gradient-type algorithms such as SGD with random reshuffling and shuffle-once mini-batch SGD. The derived rates match the best-known results for those algorithms, highlighting the tightness of our approach. Finally, our numerical evaluations support theoretical findings and show the good practical performance of our method. acknowledgement: "The authors thank all anonymous reviewers for their valuable comments and suggestions on how to improve the manuscript. This work was done when Rustem Islamov was a Master’s student at Institut Polytechnique de Paris (IP Paris) and an intern at Institute of Science and Technology Austria (ISTA). The research of Rustem Islamov was supported by ISTA internship\r\nprogram. Mher Safaryan has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101034413." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Rustem full_name: Islamov, Rustem last_name: Islamov - first_name: Mher full_name: Safaryan, Mher id: dd546b39-0804-11ed-9c55-ef075c39778d last_name: Safaryan - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.' apa: 'Islamov, R., Safaryan, M., & Alistarh, D.-A. (2024). AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 649–657). Valencia, Spain: ML Research Press.' chicago: 'Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 238:649–57. ML Research Press, 2024.' ieee: 'R. Islamov, M. Safaryan, and D.-A. Alistarh, “AsGrad: A sharp unified analysis of asynchronous-SGD algorithms,” in Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 649–657.' ista: 'Islamov R, Safaryan M, Alistarh D-A. 2024. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 238, 649–657.' mla: 'Islamov, Rustem, et al. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 649–57.' short: R. Islamov, M. Safaryan, D.-A. Alistarh, in:, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 649–657. conference: end_date: 2024-05-04 location: Valencia, Spain name: 'AISTATS: Conference on Artificial Intelligence and Statistics' start_date: 2024-05-02 corr_author: '1' date_created: 2025-01-30T08:15:49Z date_published: 2024-05-15T00:00:00Z date_updated: 2025-04-14T07:54:52Z day: '15' department: - _id: DaAl ec_funded: 1 external_id: arxiv: - '2310.20452' intvolume: ' 238' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2310.20452 month: '05' oa: 1 oa_version: Preprint page: 649-657 project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'AsGrad: A sharp unified analysis of asynchronous-SGD algorithms' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 238 year: '2024' ... --- OA_place: repository OA_type: green _id: '18977' abstract: - lang: eng text: "Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. Quantizing models to 3-4 bits per parameter can lead to moderate to high accuracy losses, especially for smaller models (1-10B parameters), which are suitable for edge deployment. To address this accuracy issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique that enables for the first time \\emph{near-lossless} compression of LLMs across model scales while reaching similar compression levels to previous methods. SpQR works by identifying and isolating \\emph{outlier weights}, which cause particularly large quantization errors, and storing them in higher precision while compressing all other weights to 3-4 bits, and achieves relative accuracy losses of less than \r\n in perplexity for highly-accurate LLaMA and Falcon LLMs. This makes it possible to run a 33B parameter LLM on a single 24 GB consumer GPU without performance degradation at 15% speedup, thus making powerful LLMs available to consumers without any downsides. SpQR comes with efficient algorithms for both encoding weights into its format, as well as decoding them efficiently at runtime. Specifically, we provide an efficient GPU inference algorithm for SpQR, which yields faster inference than 16-bit baselines at similar accuracy while enabling memory compression gains of more than 4x." acknowledgement: "Denis Kuznedelev acknowledges the support from the Russian Ministry of Science and Higher\r\nEducation, grant No. 075-10-2021-068. Ruslan Svirschevski and Vage Egiazarian and Denis\r\nKuznedelev were supported by the grant for research centers in the field of AI provided by the\r\nAnalytical Center for the Government of the Russian Federation (ACRF) in accordance with the\r\nagreement on the provision of subsidies (identifier of the agreement 000000D730321P5Q0002) and the agreement with HSE University No. 70-2021-00139." article_processing_charge: No arxiv: 1 author: - first_name: Tim full_name: Dettmers, Tim last_name: Dettmers - first_name: Ruslan A. full_name: Svirschevski, Ruslan A. last_name: Svirschevski - first_name: Vage full_name: Egiazarian, Vage last_name: Egiazarian - first_name: Denis full_name: Kuznedelev, Denis last_name: Kuznedelev - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Saleh full_name: Ashkboos, Saleh last_name: Ashkboos - first_name: Alexander full_name: Borzunov, Alexander last_name: Borzunov - first_name: Torsten full_name: Hoefler, Torsten last_name: Hoefler - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Dettmers T, Svirschevski RA, Egiazarian V, et al. SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In: 12th International Conference on Learning Representations. OpenReview; 2024.' apa: 'Dettmers, T., Svirschevski, R. A., Egiazarian, V., Kuznedelev, D., Frantar, E., Ashkboos, S., … Alistarh, D.-A. (2024). SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In 12th International Conference on Learning Representations. Vienna, Austria: OpenReview.' chicago: 'Dettmers, Tim, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan-Adrian Alistarh. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” In 12th International Conference on Learning Representations. OpenReview, 2024.' ieee: 'T. Dettmers et al., “SpQR: A sparse-quantized representation for near-lossless LLM weight compression,” in 12th International Conference on Learning Representations, Vienna, Austria, 2024.' ista: 'Dettmers T, Svirschevski RA, Egiazarian V, Kuznedelev D, Frantar E, Ashkboos S, Borzunov A, Hoefler T, Alistarh D-A. 2024. SpQR: A sparse-quantized representation for near-lossless LLM weight compression. 12th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: 'Dettmers, Tim, et al. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” 12th International Conference on Learning Representations, OpenReview, 2024.' short: T. Dettmers, R.A. Svirschevski, V. Egiazarian, D. Kuznedelev, E. Frantar, S. Ashkboos, A. Borzunov, T. Hoefler, D.-A. Alistarh, in:, 12th International Conference on Learning Representations, OpenReview, 2024. conference: end_date: 2024-05-11 location: Vienna, Austria name: 'ICLR: International Conference on Learning Representations' start_date: 2024-05-07 date_created: 2025-01-30T08:26:59Z date_published: 2024-05-15T00:00:00Z date_updated: 2025-01-30T08:27:47Z day: '15' department: - _id: DaAl external_id: arxiv: - '2306.03078' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2306.03078 month: '05' oa: 1 oa_version: Preprint publication: 12th International Conference on Learning Representations publication_status: published publisher: OpenReview quality_controlled: '1' scopus_import: '1' status: public title: 'SpQR: A sparse-quantized representation for near-lossless LLM weight compression' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository _id: '18981' abstract: - lang: eng text: We establish several results combining discrete Morse theory and microlocal sheaf theory in the setting of finite posets and simplicial complexes. Our primary tool is a computationally tractable description of the bounded derived category of sheaves on a poset with the Alexandrov topology. We prove that each bounded complex of sheaves on a finite poset admits a unique (up to isomorphism of complexes) minimal injective resolution, and we provide algorithms for computing minimal injective resolution of an injective complex, as well as several useful functors between derived categories of sheaves. For the constant sheaf on a simplicial complex, we give asymptotically tight bounds on the complexity of computing the minimal injective resolution using those algorithms. Our main result is a novel definition of the discrete microsupport of a bounded complex of sheaves on a finite poset. We detail several foundational properties of the discrete microsupport, as well as a microlocal generalization of the discrete homological Morse theorem and Morse inequalities. acknowledgement: "This project has received funding from the European Research Council (ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme, grant no. 788183, from the Wittgenstein Prize,\r\nAustrian Science Fund (FWF), grant no. Z 342-N31, and from the DFG Collaborative Research Center TRR\r\n109, ‘Discretization in Geometry and Dynamics’, Austrian Science Fund (FWF), grant no. I 02979-N35." article_processing_charge: No arxiv: 1 author: - first_name: Adam full_name: Brown, Adam last_name: Brown - first_name: Ondrej full_name: Draganov, Ondrej id: 2B23F01E-F248-11E8-B48F-1D18A9856A87 last_name: Draganov orcid: 0000-0003-0464-3823 citation: ama: Brown A, Draganov O. Discrete microlocal Morse theory. arXiv. doi:10.48550/arXiv.2209.14993 apa: Brown, A., & Draganov, O. (n.d.). Discrete microlocal Morse theory. arXiv. https://doi.org/10.48550/arXiv.2209.14993 chicago: Brown, Adam, and Ondrej Draganov. “Discrete Microlocal Morse Theory.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2209.14993. ieee: A. Brown and O. Draganov, “Discrete microlocal Morse theory,” arXiv. . ista: Brown A, Draganov O. Discrete microlocal Morse theory. arXiv, 10.48550/arXiv.2209.14993. mla: Brown, Adam, and Ondrej Draganov. “Discrete Microlocal Morse Theory.” ArXiv, doi:10.48550/arXiv.2209.14993. short: A. Brown, O. Draganov, ArXiv (n.d.). corr_author: '1' date_created: 2025-01-31T17:03:04Z date_published: 2024-06-09T00:00:00Z date_updated: 2026-04-07T11:47:29Z day: '09' department: - _id: HeEd doi: 10.48550/arXiv.2209.14993 ec_funded: 1 external_id: arxiv: - '2209.14993' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2209.14993 month: '06' oa: 1 oa_version: Preprint project: - _id: 266A2E9E-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '788183' name: Alpha Shape Theory Extended - _id: 268116B8-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z00342 name: Mathematics, Computer Science - _id: 2561EBF4-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: I02979-N35 name: Persistence and stability of geometric complexes publication: arXiv publication_status: draft related_material: record: - id: '20323' relation: later_version status: public - id: '18979' relation: dissertation_contains status: public status: public title: Discrete microlocal Morse theory tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository OA_type: green _id: '18996' abstract: - lang: eng text: 'We consider the linear causal representation learning setting where we observe a linear mixing of d unknown latent factors, which follow a linear structural causal model. Recent work has shown that it is possible to recover the latent factors as well as the underlying structural causal model over them, up to permutation and scaling, provided that we have at least d environments, each of which corresponds to perfect interventions on a single latent node (factor). After this powerful result, a key open problem faced by the community has been to relax these conditions: allow for coarser than perfect single-node interventions, and allow for fewer than d of them, since the number of latent factors d could be very large. In this work, we consider precisely such a setting, where we allow a smaller than d number of environments, and also allow for very coarse interventions that can very coarsely \textit{change the entire causal graph over the latent factors}. On the flip side, we relax what we wish to extract to simply the \textit{list of nodes that have shifted between one or more environments}. We provide a surprising identifiability result that it is indeed possible, under some very mild standard assumptions, to identify the set of shifted nodes. Our identifiability proof moreover is a constructive one: we explicitly provide necessary and sufficient conditions for a node to be a shifted node, and show that we can check these conditions given observed data. Our algorithm lends itself very naturally to the sample setting where instead of just interventional distributions, we are provided datasets of samples from each of these distributions. We corroborate our results on both synthetic experiments as well as an interesting psychometric dataset. The code can be found at https://github.com/TianyuCodings/iLCS.' alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Tianyu full_name: Chen, Tianyu last_name: Chen - first_name: Kevin full_name: Bello, Kevin last_name: Bello - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bryon full_name: Aragam, Bryon last_name: Aragam - first_name: Pradeep Kumar full_name: Ravikumar, Pradeep Kumar last_name: Ravikumar citation: ama: 'Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. Identifying general mechanism shifts in linear causal representations. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Chen, T., Bello, K., Locatello, F., Aragam, B., & Ravikumar, P. K. (2024). Identifying general mechanism shifts in linear causal representations. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: Chen, Tianyu, Kevin Bello, Francesco Locatello, Bryon Aragam, and Pradeep Kumar Ravikumar. “Identifying General Mechanism Shifts in Linear Causal Representations.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024. ieee: T. Chen, K. Bello, F. Locatello, B. Aragam, and P. K. Ravikumar, “Identifying general mechanism shifts in linear causal representations,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37. ista: 'Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. 2024. Identifying general mechanism shifts in linear causal representations. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: Chen, Tianyu, et al. “Identifying General Mechanism Shifts in Linear Causal Representations.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024. short: T. Chen, K. Bello, F. Locatello, B. Aragam, P.K. Ravikumar, in:, 38th 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 date_created: 2025-02-04T13:09:34Z date_published: 2024-09-25T00:00:00Z date_updated: 2025-07-07T13:23:49Z day: '25' ddc: - '000' department: - _id: FrLo external_id: arxiv: - '2410.24059' file: - access_level: open_access checksum: 75c3091e70bd2916cd94afbf40a0c425 content_type: application/pdf creator: dernst date_created: 2025-02-04T13:09:08Z date_updated: 2025-02-04T13:09:08Z file_id: '18997' file_name: 2024_NeurIPS_Chen.pdf file_size: 5659119 relation: main_file success: 1 file_date_updated: 2025-02-04T13:09:08Z has_accepted_license: '1' intvolume: ' 37' language: - iso: eng month: '09' oa: 1 oa_version: Published Version publication: 38th 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: Identifying general mechanism shifts in linear causal representations 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: publisher OA_type: gold _id: '18998' abstract: - lang: eng text: Word embeddings represent language vocabularies as clouds of d-dimensional points. We investigate how information is conveyed by the general shape of these clouds, instead of representing the semantic meaning of each token. Specifically, we use the notion of persistent homology from topological data analysis (TDA) to measure the distances between language pairs from the shape of their unlabeled embeddings. These distances quantify the degree of non-isometry of the embeddings. To distinguish whether these differences are random training errors or capture real information about the languages, we use the computed distance matrices to construct language phylogenetic trees over 81 Indo-European languages. Careful evaluation shows that our reconstructed trees exhibit strong and statistically-significant similarities to the reference. article_processing_charge: No arxiv: 1 author: - first_name: Ondrej full_name: Draganov, Ondrej id: 2B23F01E-F248-11E8-B48F-1D18A9856A87 last_name: Draganov orcid: 0000-0003-0464-3823 - first_name: Steven full_name: Skiena, Steven last_name: Skiena citation: ama: 'Draganov O, Skiena S. The shape of word embeddings: Quantifying non-isometry with topological data analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics; 2024:12080-12099. doi:10.18653/v1/2024.findings-emnlp.705' apa: 'Draganov, O., & Skiena, S. (2024). The shape of word embeddings: Quantifying non-isometry with topological data analysis. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 12080–12099). Miami, FL, United States: Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.705' chicago: 'Draganov, Ondrej, and Steven Skiena. “The Shape of Word Embeddings: Quantifying Non-Isometry with Topological Data Analysis.” In Findings of the Association for Computational Linguistics: EMNLP 2024, 12080–99. Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.findings-emnlp.705.' ieee: 'O. Draganov and S. Skiena, “The shape of word embeddings: Quantifying non-isometry with topological data analysis,” in Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, FL, United States, 2024, pp. 12080–12099.' ista: 'Draganov O, Skiena S. 2024. The shape of word embeddings: Quantifying non-isometry with topological data analysis. Findings of the Association for Computational Linguistics: EMNLP 2024. EMNLP: Conference on Empirical Methods in Natural Language Processing, 12080–12099.' mla: 'Draganov, Ondrej, and Steven Skiena. “The Shape of Word Embeddings: Quantifying Non-Isometry with Topological Data Analysis.” Findings of the Association for Computational Linguistics: EMNLP 2024, Association for Computational Linguistics, 2024, pp. 12080–99, doi:10.18653/v1/2024.findings-emnlp.705.' short: 'O. Draganov, S. Skiena, in:, Findings of the Association for Computational Linguistics: EMNLP 2024, Association for Computational Linguistics, 2024, pp. 12080–12099.' conference: end_date: 2024-11-16 location: Miami, FL, United States name: 'EMNLP: Conference on Empirical Methods in Natural Language Processing' start_date: 2024-11-12 corr_author: '1' date_created: 2025-02-04T16:19:28Z date_published: 2024-11-01T00:00:00Z date_updated: 2025-02-10T08:21:37Z day: '01' ddc: - '500' department: - _id: GradSch - _id: HeEd doi: 10.18653/v1/2024.findings-emnlp.705 external_id: arxiv: - '2404.00500' file: - access_level: open_access checksum: f4416a5962194f0181ab0dc7f9ef93c0 content_type: application/pdf creator: dernst date_created: 2025-02-10T08:20:34Z date_updated: 2025-02-10T08:20:34Z file_id: '19016' file_name: 2024_EMNLP_Draganov.pdf file_size: 1312638 relation: main_file success: 1 file_date_updated: 2025-02-10T08:20:34Z has_accepted_license: '1' language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 12080-12099 publication: 'Findings of the Association for Computational Linguistics: EMNLP 2024' publication_status: published publisher: Association for Computational Linguistics quality_controlled: '1' scopus_import: '1' status: public title: 'The shape of word embeddings: Quantifying non-isometry with topological data analysis' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository OA_type: green _id: '18999' abstract: - lang: eng text: Exploring the shape of point configurations has been a key driver in the evolution of TDA (short for topological data analysis) since its infancy. This survey illustrates the recent efforts to broaden these ideas to model spatial interactions among multiple configurations, each distinguished by a color. It describes advances in this area and prepares the ground for further exploration by mentioning unresolved questions and promising research avenues while focusing on the overlap with discrete geometry. article_number: '2406.04102' article_processing_charge: No arxiv: 1 author: - first_name: Sebastiano full_name: Cultrera di Montesano, Sebastiano id: 34D2A09C-F248-11E8-B48F-1D18A9856A87 last_name: Cultrera di Montesano orcid: 0000-0001-6249-0832 - first_name: Ondrej full_name: Draganov, Ondrej id: 2B23F01E-F248-11E8-B48F-1D18A9856A87 last_name: Draganov orcid: 0000-0003-0464-3823 - first_name: Herbert full_name: Edelsbrunner, Herbert id: 3FB178DA-F248-11E8-B48F-1D18A9856A87 last_name: Edelsbrunner orcid: 0000-0002-9823-6833 - first_name: Morteza full_name: Saghafian, Morteza id: f86f7148-b140-11ec-9577-95435b8df824 last_name: Saghafian citation: ama: Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic topological data analysis. arXiv. doi:10.48550/ARXIV.2406.04102 apa: Cultrera di Montesano, S., Draganov, O., Edelsbrunner, H., & Saghafian, M. (n.d.). Chromatic topological data analysis. arXiv. https://doi.org/10.48550/ARXIV.2406.04102 chicago: Cultrera di Montesano, Sebastiano, Ondrej Draganov, Herbert Edelsbrunner, and Morteza Saghafian. “Chromatic Topological Data Analysis.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2406.04102. ieee: S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, and M. Saghafian, “Chromatic topological data analysis,” arXiv. . ista: Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic topological data analysis. arXiv, 2406.04102. mla: Cultrera di Montesano, Sebastiano, et al. “Chromatic Topological Data Analysis.” ArXiv, 2406.04102, doi:10.48550/ARXIV.2406.04102. short: S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, M. Saghafian, ArXiv (n.d.). corr_author: '1' date_created: 2025-02-04T16:21:21Z date_published: 2024-06-06T00:00:00Z date_updated: 2025-02-10T08:14:27Z day: '06' ddc: - '510' department: - _id: GradSch - _id: HeEd doi: 10.48550/ARXIV.2406.04102 external_id: arxiv: - '2406.04102' has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2406.04102 month: '06' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Chromatic topological data analysis tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ...