[{"ddc":["510"],"external_id":{"arxiv":["2411.09264"]},"date_updated":"2026-08-12T11:17:50Z","department":[{"_id":"TiBr"}],"abstract":[{"lang":"eng","text":"The large sieve is used to estimate the density of quadratic polynomials Q ∈ Z[x],\r\nsuch that there exists an odd degree polynomial defined over Z which has resultant ±1 with Q.\r\nGiven a monic polynomial R ∈ Z[x] of odd degree, this is used to show that for almost all\r\nquadratic polynomials Q ∈ Z[x], there exists a prime p such that Q and R share a common\r\nroot in Fp. Using recent work of Landesman, an application to the average size of the odd part\r\nof the class group of quadratic number fields is also given"},{"lang":"fre","text":" Le grand crible est utilisé pour estimer la densité des polynômes quadratiques Q ∈ Z[x] tels qu’il existe un polynôme de degré impair défini sur Z dont le résultant avec Q est égal à ±1. Étant donné un polynôme unitaire R ∈ Z[x] de degré impair, on s’en sert pour montrer que, pour presque tous les polynômes quadratiques Q ∈ Z[x], il existe un nombre premier p tel que Q et R aient une racine commune dans Fp. En utilisant des travaux récents de Landesman, on obtient également une application concernant la taille moyenne de la partie impaire du groupe de classe des corps quadratiques."}],"date_published":"2025-10-21T00:00:00Z","arxiv":1,"PlanS_conform":"1","publisher":"Ecole Polytechnique","language":[{"iso":"eng"}],"oa_version":"Published Version","citation":{"mla":"Browning, Timothy D., and Stephanie Chan. “Solubility of a Resultant Equation and Applications.” Journal de l’Ecole Polytechnique - Mathematiques, vol. 12, Ecole Polytechnique, 2025, pp. 1677–91, doi:10.5802/jep.320.","ista":"Browning TD, Chan S. 2025. Solubility of a resultant equation and applications. Journal de l’Ecole Polytechnique - Mathematiques. 12, 1677–1691.","ieee":"T. D. Browning and S. Chan, “Solubility of a resultant equation and applications,” Journal de l’Ecole Polytechnique - Mathematiques, vol. 12. Ecole Polytechnique, pp. 1677–1691, 2025.","chicago":"Browning, Timothy D, and Stephanie Chan. “Solubility of a Resultant Equation and Applications.” Journal de l’Ecole Polytechnique - Mathematiques. Ecole Polytechnique, 2025. https://doi.org/10.5802/jep.320.","short":"T.D. Browning, S. Chan, Journal de l’Ecole Polytechnique - Mathematiques 12 (2025) 1677–1691.","ama":"Browning TD, Chan S. Solubility of a resultant equation and applications. Journal de l’Ecole Polytechnique - Mathematiques. 2025;12:1677-1691. doi:10.5802/jep.320","apa":"Browning, T. D., & Chan, S. (2025). Solubility of a resultant equation and applications. Journal de l’Ecole Polytechnique - Mathematiques. Ecole Polytechnique. https://doi.org/10.5802/jep.320"},"corr_author":"1","researchdata_availability":"no","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"orcid":"0000-0002-8314-0177","full_name":"Browning, Timothy D","first_name":"Timothy D","last_name":"Browning","id":"35827D50-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0001-8467-4106","full_name":"Chan, Yik Tung","id":"c4c0afc8-9262-11ed-9231-d8b0bc743af1","first_name":"Yik Tung","last_name":"Chan"}],"_id":"21343","doi":"10.5802/jep.320","publication_identifier":{"issn":["2429-7100"],"eissn":["2270-518X"]},"has_accepted_license":"1","article_type":"original","oa":1,"quality_controlled":"1","supplementarymaterial":"no","file_date_updated":"2026-02-24T07:56:34Z","year":"2025","publication_status":"published","status":"public","type":"journal_article","das_tickbox":"0","intvolume":" 12","project":[{"_id":"bd8a4fdc-d553-11ed-ba76-80a0167441a3","grant_number":"P36278","name":"Rational curves via function field analytic number theory"}],"page":"1677-1691","title":"Solubility of a resultant equation and applications","article_processing_charge":"Yes","month":"10","volume":12,"file":[{"file_name":"2025_JEP_Browning.pdf","file_size":1003689,"content_type":"application/pdf","date_updated":"2026-02-24T07:56:34Z","relation":"main_file","file_id":"21356","creator":"dernst","checksum":"828577ea48ac6109d3e9dd1aeddd45c4","date_created":"2026-02-24T07:56:34Z","access_level":"open_access","success":1}],"scopus_import":"1","OA_type":"gold","DOAJ_listed":"1","acknowledgement":"While working on this paper, the first author was supported by a FWF grant (DOI 10.55776/P36278).","date_created":"2026-02-22T23:01:36Z","OA_place":"publisher","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"day":"21","publication":"Journal de l'Ecole Polytechnique - Mathematiques"},{"doi":"10.7554/elife.68993","publication_identifier":{"issn":["2050-084X"]},"_id":"15033","has_accepted_license":"1","oa":1,"article_type":"original","quality_controlled":"1","type":"journal_article","status":"public","publication_status":"published","file_date_updated":"2024-07-22T11:51:50Z","year":"2024","project":[{"_id":"261099A6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"742985","name":"Tracing Evolution of Auxin Transport and Polarity in Plants"},{"_id":"26538374-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Molecular mechanisms of endocytic cargo recognition in plants","grant_number":"I03630"},{"_id":"3AC91DDA-15DF-11EA-824D-93A3E7B544D1","call_identifier":"FWF","name":"FWF Open Access Fund"}],"keyword":["General Immunology and Microbiology","General Biochemistry","Genetics and Molecular Biology","General Medicine","General Neuroscience"],"intvolume":" 13","title":"Developmental patterning function of GNOM ARF-GEF mediated from the cell periphery","month":"02","article_processing_charge":"Yes","file":[{"access_level":"open_access","success":1,"creator":"dernst","file_id":"17310","relation":"main_file","date_created":"2024-07-22T11:51:50Z","checksum":"b2b2d583b433823af731842f1420113e","file_size":15675744,"content_type":"application/pdf","date_updated":"2024-07-22T11:51:50Z","file_name":"2024_eLife_Adamowski.pdf"}],"volume":13,"ec_funded":1,"OA_place":"publisher","acknowledgement":"The authors would like to gratefully acknowledge Dr Xixi Zhang for cloning the GNL1/pDONR221 construct and for useful discussions.H2020 European Research Council Advanced Grant ETAP742985 to Jiří Friml, Austrian Science Fund I 3630-B25 to Jiří Friml","date_created":"2024-02-27T07:10:11Z","DOAJ_listed":"1","OA_type":"gold","scopus_import":"1","day":"21","pmid":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publication":"eLife","external_id":{"pmid":["38381485"],"isi":["001174278000001"]},"isi":1,"ddc":["580"],"department":[{"_id":"JiFr"}],"date_updated":"2025-10-15T06:31:47Z","abstract":[{"lang":"eng","text":"The GNOM (GN) Guanine nucleotide Exchange Factor for ARF small GTPases (ARF-GEF) is among the best studied trafficking regulators in plants, playing crucial and unique developmental roles in patterning and polarity. The current models place GN at the Golgi apparatus (GA), where it mediates secretion/recycling, and at the plasma membrane (PM) presumably contributing to clathrin-mediated endocytosis (CME). The mechanistic basis of the developmental function of GN, distinct from the other ARF-GEFs including its closest homologue GNOM-LIKE1 (GNL1), remains elusive. Insights from this study largely extend the current notions of GN function. We show that GN, but not GNL1, localizes to the cell periphery at long-lived structures distinct from clathrin-coated pits, while CME and secretion proceed normally in gn knockouts. The functional GN mutant variant GNfewerroots, absent from the GA, suggests that the cell periphery is the major site of GN action responsible for its developmental function. Following inhibition by Brefeldin A, GN, but not GNL1, relocates to the PM likely on exocytic vesicles, suggesting selective molecular associations en route to the cell periphery. A study of GN-GNL1 chimeric ARF-GEFs indicates that all GN domains contribute to the specific GN function in a partially redundant manner. Together, this study offers significant steps toward the elucidation of the mechanism underlying unique cellular and development functions of GNOM."}],"date_published":"2024-02-21T00:00:00Z","publisher":"eLife Sciences Publications","oa_version":"Published Version","language":[{"iso":"eng"}],"APC_amount":"2792,52 EUR","citation":{"mla":"Adamowski, Maciek, et al. “Developmental Patterning Function of GNOM ARF-GEF Mediated from the Cell Periphery.” ELife, vol. 13, eLife Sciences Publications, 2024, doi:10.7554/elife.68993.","ista":"Adamowski M, Matijevic I, Friml J. 2024. Developmental patterning function of GNOM ARF-GEF mediated from the cell periphery. eLife. 13.","ieee":"M. Adamowski, I. Matijevic, and J. Friml, “Developmental patterning function of GNOM ARF-GEF mediated from the cell periphery,” eLife, vol. 13. eLife Sciences Publications, 2024.","chicago":"Adamowski, Maciek, Ivana Matijevic, and Jiří Friml. “Developmental Patterning Function of GNOM ARF-GEF Mediated from the Cell Periphery.” ELife. eLife Sciences Publications, 2024. https://doi.org/10.7554/elife.68993.","ama":"Adamowski M, Matijevic I, Friml J. Developmental patterning function of GNOM ARF-GEF mediated from the cell periphery. eLife. 2024;13. doi:10.7554/elife.68993","short":"M. Adamowski, I. Matijevic, J. Friml, ELife 13 (2024).","apa":"Adamowski, M., Matijevic, I., & Friml, J. (2024). Developmental patterning function of GNOM ARF-GEF mediated from the cell periphery. 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This research was supported by the Scientific Service Units of ISTA through resources provided by the Lab Support Facility, the Imaging and Optics Facility, the Machine Shop Unit and the Preclinical Facility, especially Freyja Langer and Michael Schunn. 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F., Barton, N. H., Faria, R., Larsson, J., Stankowski, S., Butlin, R., … Westram, A. M. (2024). Data and code for: Predicting rapid adaptation in time from adaptation in space: a 30-year field experiment in marine snails. Zenodo. https://doi.org/10.5281/ZENODO.12159343","short":"D.F. Garcia Castillo, N.H. Barton, R. Faria, J. Larsson, S. Stankowski, R. Butlin, K. Johannesson, A.M. Westram, (2024).","ama":"Garcia Castillo DF, Barton NH, Faria R, et al. Data and code for: Predicting rapid adaptation in time from adaptation in space: a 30-year field experiment in marine snails. 2024. doi:10.5281/ZENODO.12159343","chicago":"Garcia Castillo, Diego Fernando, Nicholas H Barton, Rui Faria, Jenny Larsson, Sean Stankowski, Roger Butlin, Kerstin Johannesson, and Anja M Westram. “Data and Code for: Predicting Rapid Adaptation in Time from Adaptation in Space: A 30-Year Field Experiment in Marine Snails.” Zenodo, 2024. https://doi.org/10.5281/ZENODO.12159343.","ieee":"D. F. 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Data and code for: Predicting rapid adaptation in time from adaptation in space: a 30-year field experiment in marine snails, Zenodo, 10.5281/ZENODO.12159343."},"month":"06","article_processing_charge":"No","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"day":"19","corr_author":"1","date_created":"2024-11-04T09:33:17Z","OA_place":"repository","author":[{"id":"ae681a14-dc74-11ea-a0a7-c6ef18161701","last_name":"Garcia Castillo","first_name":"Diego Fernando","full_name":"Garcia Castillo, Diego Fernando"},{"full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","first_name":"Nicholas H","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Faria","first_name":"Rui","full_name":"Faria, Rui"},{"full_name":"Larsson, Jenny","first_name":"Jenny","last_name":"Larsson"},{"id":"43161670-5719-11EA-8025-FABC3DDC885E","first_name":"Sean","last_name":"Stankowski","full_name":"Stankowski, Sean"},{"full_name":"Butlin, Roger","last_name":"Butlin","first_name":"Roger"},{"last_name":"Johannesson","first_name":"Kerstin","full_name":"Johannesson, Kerstin"},{"first_name":"Anja M","last_name":"Westram","id":"3C147470-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1050-4969","full_name":"Westram, Anja M"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"user_id":"68b8ca59-c5b3-11ee-8790-cd641c68093d","author":[{"full_name":"Lutsai, Kateryna","last_name":"Lutsai","first_name":"Kateryna"},{"full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","last_name":"Lampert"}],"corr_author":"1","citation":{"short":"K. Lutsai, C. Lampert, Journal of Spatial Information Science (2024) 69–99.","ama":"Lutsai K, Lampert C. Predicting the geolocation of tweets using transformer models on customized data. Journal of Spatial Information Science. 2024;(29):69-99. doi:10.5311/JOSIS.2024.29.295","apa":"Lutsai, K., & Lampert, C. (2024). Predicting the geolocation of tweets using transformer models on customized data. Journal of Spatial Information Science. University of Maine. https://doi.org/10.5311/JOSIS.2024.29.295","ieee":"K. Lutsai and C. Lampert, “Predicting the geolocation of tweets using transformer models on customized data,” Journal of Spatial Information Science, no. 29. University of Maine, pp. 69–99, 2024.","chicago":"Lutsai, Kateryna, and Christoph Lampert. “Predicting the Geolocation of Tweets Using Transformer Models on Customized Data.” Journal of Spatial Information Science. 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(29), 69–99."},"oa_version":"Published Version","language":[{"iso":"eng"}],"issue":"29","license":"https://creativecommons.org/licenses/by/3.0/","publisher":"University of Maine","related_material":{"link":[{"url":"https://github.com/K4TEL/geo-twitter.git","relation":"software"}]},"date_published":"2024-12-26T00:00:00Z","department":[{"_id":"ChLa"}],"date_updated":"2025-06-05T13:47:12Z","abstract":[{"lang":"eng","text":"This research is aimed to solve the tweet/user geolocation prediction task and provide a flexible methodology for the geo-tagging of textual big data. The suggested approach implements neural networks for natural language processing (NLP) to estimate the location as coordinate pairs (longitude, latitude) and two-dimensional Gaussian Mixture Models (GMMs). The scope of proposed models has been finetuned on a Twitter dataset using pretrained Bidirectional Encoder Representations from Transformers (BERT) as base models. Performance metrics show a median error of fewer than 30 km on a worldwide-level, and fewer than 15 km on the US-level datasets for the models trained and evaluated on text features of tweets' content and metadata context. Our source code and data are available at https://github.com/K4TEL/geo-twitter.git."}],"ddc":["500"],"publication":"Journal of Spatial Information Science","date_created":"2025-01-19T23:01:53Z","acknowledgement":"The authors acknowledge the Institute of Science and Technology (ISTA) for their material support and for granting access to the Twitter database archive, which was essential for the research.","OA_place":"publisher","OA_type":"gold","DOAJ_listed":"1","scopus_import":"1","day":"26","tmp":{"name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","short":"CC BY (3.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode"},"month":"12","article_processing_charge":"Yes","file":[{"file_size":7250655,"date_updated":"2025-01-20T08:41:10Z","content_type":"application/pdf","file_name":"2024_JourSpatialInfoScience_Lutsai.pdf","access_level":"open_access","success":1,"relation":"main_file","creator":"dernst","file_id":"18857","date_created":"2025-01-20T08:41:10Z","checksum":"b82413f00398ffb5168e8e747571a98d"}],"title":"Predicting the geolocation of tweets using transformer models on customized data","page":"69-99","quality_controlled":"1","status":"public","type":"journal_article","file_date_updated":"2025-01-20T08:41:10Z","publication_status":"published","year":"2024","has_accepted_license":"1","oa":1,"article_type":"original","publication_identifier":{"eissn":["1948-660X"]},"doi":"10.5311/JOSIS.2024.29.295","_id":"18856"},{"date_published":"2024-03-01T00:00:00Z","issue":"3","license":"https://creativecommons.org/licenses/by-nc-sa/4.0/","publisher":"American Society for Cell Biology","external_id":{"pmid":["38117593"]},"ddc":["570"],"department":[{"_id":"FlSc"}],"date_updated":"2025-01-29T08:16:20Z","abstract":[{"text":"The assembly of biomolecular condensate in eukaryotic cells and the accumulation of amyloid deposits in neurons are processes involving the nucleation and growth (NAG) of new protein phases. To therapeutically target protein phase separation, drug candidates are tested in in vitro assays that monitor the increase in the mass or size of the new phase. Limited mechanistic insight is, however, provided if empirical or untestable kinetic models are fitted to these progress curves. Here we present the web server NAGPKin that quantifies NAG rates using mass-based or size-based progress curves as the input data. A report is generated containing the fitted NAG parameters and elucidating the phase separation mechanisms at play. The NAG parameters can be used to predict particle size distributions of, for example, protein droplets formed by liquid-liquid phase separation (LLPS) or amyloid fibrils formed by protein aggregation. Because minimal intervention is required from the user, NAGPKin is a good platform for standardized reporting of LLPS and protein self-assembly data. NAGPKin is useful for drug discovery as well as for fundamental studies on protein phase separation. NAGPKin is freely available (no login required) at https://nagpkin.i3s.up.pt .","lang":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Sárkány, Zsuzsa","last_name":"Sárkány","first_name":"Zsuzsa"},{"full_name":"Figueiredo, Francisco","first_name":"Francisco","last_name":"Figueiredo","id":"8125cbe2-9661-11ed-a754-afe96018f37d"},{"first_name":"Sandra","last_name":"Macedo-Ribeiro","full_name":"Macedo-Ribeiro, Sandra"},{"full_name":"Martins, Pedro M.","first_name":"Pedro M.","last_name":"Martins"}],"oa_version":"Published Version","language":[{"iso":"eng"}],"citation":{"ista":"Sárkány Z, Figueiredo F, Macedo-Ribeiro S, Martins PM. 2024. NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation. Molecular Biology of the Cell. 35(3), mr1.","mla":"Sárkány, Zsuzsa, et al. “NAGPKin: Nucleation-and-Growth Parameters from the Kinetics of Protein Phase Separation.” Molecular Biology of the Cell, vol. 35, no. 3, mr1, American Society for Cell Biology, 2024, doi:10.1091/mbc.e23-07-0289.","chicago":"Sárkány, Zsuzsa, Francisco Figueiredo, Sandra Macedo-Ribeiro, and Pedro M. Martins. “NAGPKin: Nucleation-and-Growth Parameters from the Kinetics of Protein Phase Separation.” Molecular Biology of the Cell. American Society for Cell Biology, 2024. https://doi.org/10.1091/mbc.e23-07-0289.","ieee":"Z. Sárkány, F. Figueiredo, S. Macedo-Ribeiro, and P. M. Martins, “NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation,” Molecular Biology of the Cell, vol. 35, no. 3. American Society for Cell Biology, 2024.","apa":"Sárkány, Z., Figueiredo, F., Macedo-Ribeiro, S., & Martins, P. M. (2024). NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation. Molecular Biology of the Cell. American Society for Cell Biology. https://doi.org/10.1091/mbc.e23-07-0289","ama":"Sárkány Z, Figueiredo F, Macedo-Ribeiro S, Martins PM. NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation. Molecular Biology of the Cell. 2024;35(3). doi:10.1091/mbc.e23-07-0289","short":"Z. Sárkány, F. Figueiredo, S. Macedo-Ribeiro, P.M. Martins, Molecular Biology of the Cell 35 (2024)."},"quality_controlled":"1","type":"journal_article","status":"public","publication_status":"published","year":"2024","file_date_updated":"2025-01-29T08:12:11Z","doi":"10.1091/mbc.e23-07-0289","publication_identifier":{"issn":["1059-1524"],"eissn":["1939-4586"]},"_id":"18934","article_number":"mr1","has_accepted_license":"1","oa":1,"article_type":"original","date_created":"2025-01-29T07:58:40Z","OA_place":"publisher","acknowledgement":"We thank Professor José Paulo Leal, Department of Computer Science − Faculdade de Ciências da Universidade do Porto, for his invaluable help during the Implementation of NAGPKin. This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 952334 (PhasAGE). This research was funded by the Portuguese Foundation for Science and Technology (FCT) in the framework of project PTDC/QUI-COL/2444/2021.","OA_type":"hybrid","scopus_import":"1","day":"01","pmid":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","short":"CC BY-NC-SA (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","image":"/images/cc_by_nc_sa.png"},"publication":"Molecular Biology of the Cell","intvolume":" 35","title":"NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation","article_processing_charge":"Yes (in subscription journal)","month":"03","file":[{"file_name":"2024_MolecularBioCell_Sarkany.pdf","file_size":1699180,"content_type":"application/pdf","date_updated":"2025-01-29T08:12:11Z","relation":"main_file","file_id":"18935","creator":"dernst","checksum":"d7deb6390f294da69321cfbe352ed611","date_created":"2025-01-29T08:12:11Z","access_level":"open_access","success":1}],"volume":35},{"publication":"2024 IEEE International Conference on Image Processing","day":"01","OA_type":"green","OA_place":"repository","date_created":"2025-01-29T12:22:24Z","article_processing_charge":"No","month":"11","page":"977-983","title":"Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image","year":"2024","publication_status":"published","status":"public","type":"conference","quality_controlled":"1","oa":1,"_id":"18956","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2303.06439","open_access":"1"}],"publication_identifier":{"eisbn":["9798350349399"],"eissn":["2381-8549"]},"doi":"10.1109/icip51287.2024.10647499","author":[{"id":"8b4bc47f-3200-11ee-973b-8f0e7be21a9f","first_name":"Berker","last_name":"Demirel","full_name":"Demirel, Berker"},{"full_name":"Ozkan, Huseyin","last_name":"Ozkan","first_name":"Huseyin"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","corr_author":"1","citation":{"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.","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.","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.","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.","short":"B. Demirel, H. Ozkan, in:, 2024 IEEE International Conference on Image Processing, IEEE, 2024, pp. 977–983.","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"},"language":[{"iso":"eng"}],"oa_version":"Preprint","related_material":{"link":[{"relation":"software","url":"https://github.com/berkerdemirel/decompl"}]},"publisher":"IEEE","conference":{"end_date":"2024-10-30","name":"ICIP: International Conference on Image Processing","location":"Abu Dhabi, United Arab Emirates","start_date":"2024-10-27"},"arxiv":1,"date_published":"2024-11-01T00:00:00Z","abstract":[{"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","lang":"eng"}],"date_updated":"2025-09-09T12:13:12Z","department":[{"_id":"FrLo"}],"external_id":{"isi":["001442947000143"],"arxiv":["2303.06439"]},"isi":1},{"title":"Adaptive slot attention: Object discovery with dynamic slot number","month":"06","article_processing_charge":"No","day":"15","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.","OA_place":"repository","date_created":"2025-01-29T14:27:39Z","OA_type":"green","publication":"2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":"10.1109/cvpr52733.2024.02176","publication_identifier":{"eisbn":["9798350353006"]},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2406.09196"}],"_id":"18964","oa":1,"status":"public","type":"conference","publication_status":"published","year":"2024","quality_controlled":"1","oa_version":"Preprint","language":[{"iso":"eng"}],"citation":{"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.","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.","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.","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","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","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."},"author":[{"first_name":"Ke","last_name":"Fan","full_name":"Fan, Ke"},{"full_name":"Bai, Zechen","first_name":"Zechen","last_name":"Bai"},{"full_name":"Xiao, Tianjun","last_name":"Xiao","first_name":"Tianjun"},{"first_name":"Tong","last_name":"He","full_name":"He, Tong"},{"full_name":"Horn, Max","first_name":"Max","last_name":"Horn"},{"full_name":"Fu, Yanwei","last_name":"Fu","first_name":"Yanwei"},{"orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"full_name":"Zhang, Zheng","first_name":"Zheng","last_name":"Zhang"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","isi":1,"external_id":{"isi":["001342515506043"],"arxiv":["2406.09196"]},"abstract":[{"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/","lang":"eng"}],"department":[{"_id":"FrLo"}],"date_updated":"2025-09-09T12:15:17Z","arxiv":1,"date_published":"2024-06-15T00:00:00Z","publisher":"IEEE","related_material":{"link":[{"url":"https://kfan21.github.io/AdaSlot/","relation":"software"}]},"conference":{"location":"Seattle, WA, United States","start_date":"2024-06-16","name":"CVPR: Conference on Computer Vision and Pattern Recognition","end_date":"2024-06-22"}},{"alternative_title":["PMLR"],"quality_controlled":"1","type":"conference","status":"public","year":"2024","publication_status":"published","oa":1,"publication_identifier":{"eissn":["2640-3498"]},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2402.13368"}],"_id":"18971","publication":"Proceedings of the 41st International Conference on Machine Learning","OA_place":"repository","date_created":"2025-01-30T07:21:57Z","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","OA_type":"green","scopus_import":"1","day":"30","month":"07","article_processing_charge":"No","volume":235,"intvolume":" 235","page":"1672-1688","title":"Unsupervised concept discovery mitigates spurious correlations","conference":{"name":"ICML: International Conference on Machine Learning","end_date":"2024-07-27","location":"Vienna, Austria","start_date":"2024-07-21"},"related_material":{"link":[{"url":"https://github.com/rarefin/CoBalT","relation":"software"}]},"publisher":"ML Research Press","date_published":"2024-07-30T00:00:00Z","arxiv":1,"department":[{"_id":"FrLo"}],"date_updated":"2025-01-30T07:23:10Z","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"}],"external_id":{"arxiv":["2402.13368"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Arefin, Rifat","first_name":"Rifat","last_name":"Arefin"},{"first_name":"Yan","last_name":"Zhang","full_name":"Zhang, Yan"},{"first_name":"Aristide","last_name":"Baratin","full_name":"Baratin, Aristide"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"full_name":"Rish, Irina","last_name":"Rish","first_name":"Irina"},{"first_name":"Dianbo","last_name":"Liu","full_name":"Liu, Dianbo"},{"last_name":"Kawaguchi","first_name":"Kenji","full_name":"Kawaguchi, Kenji"}],"citation":{"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.","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.","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.","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."},"oa_version":"Preprint","language":[{"iso":"eng"}]},{"scopus_import":"1","OA_type":"green","date_created":"2025-02-04T13:09:34Z","OA_place":"repository","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"day":"25","publication":"38th Conference on Neural Information Processing Systems","intvolume":" 37","title":"Identifying general mechanism shifts in linear causal representations","article_processing_charge":"No","month":"09","volume":37,"file":[{"checksum":"75c3091e70bd2916cd94afbf40a0c425","date_created":"2025-02-04T13:09:08Z","creator":"dernst","file_id":"18997","relation":"main_file","success":1,"access_level":"open_access","file_name":"2024_NeurIPS_Chen.pdf","date_updated":"2025-02-04T13:09:08Z","content_type":"application/pdf","file_size":5659119}],"quality_controlled":"1","file_date_updated":"2025-02-04T13:09:08Z","publication_status":"published","year":"2024","status":"public","type":"conference","alternative_title":["Advances in Neural Information Processing Systems"],"_id":"18996","publication_identifier":{"eissn":["1049-5258"]},"has_accepted_license":"1","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Chen, Tianyu","first_name":"Tianyu","last_name":"Chen"},{"last_name":"Bello","first_name":"Kevin","full_name":"Bello, Kevin"},{"first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"last_name":"Aragam","first_name":"Bryon","full_name":"Aragam, Bryon"},{"last_name":"Ravikumar","first_name":"Pradeep Kumar","full_name":"Ravikumar, Pradeep Kumar"}],"language":[{"iso":"eng"}],"oa_version":"Published Version","citation":{"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.","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.","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.","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.","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."},"date_published":"2024-09-25T00:00:00Z","arxiv":1,"conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2024-12-16","location":"Vancouver, Canada","start_date":"2024-12-16"},"publisher":"Neural Information Processing Systems Foundation","ddc":["000"],"external_id":{"arxiv":["2410.24059"]},"date_updated":"2025-07-07T13:23:49Z","department":[{"_id":"FrLo"}],"abstract":[{"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.","lang":"eng"}]},{"corr_author":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Yao, Dingling","id":"d3e02e50-48a8-11ee-8f62-c108061797fa","last_name":"Yao","first_name":"Dingling"},{"id":"f978ccb0-3f7f-11eb-b193-b0e2bd13182b","last_name":"Muller","first_name":"Caroline J","full_name":"Muller, Caroline J","orcid":"0000-0001-5836-5350"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","first_name":"Francesco","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"}],"language":[{"iso":"eng"}],"oa_version":"Published Version","citation":{"mla":"Yao, Dingling, et al. “Marrying Causal Representation Learning with Dynamical Systems for Science.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.","ista":"Yao D, Muller CJ, Locatello F. 2024. Marrying causal representation learning with dynamical systems for science. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.","chicago":"Yao, Dingling, Caroline J Muller, and Francesco Locatello. “Marrying Causal Representation Learning with Dynamical Systems for Science.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.","ieee":"D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning with dynamical systems for science,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.","apa":"Yao, D., Muller, C. J., & Locatello, F. (2024). Marrying causal representation learning with dynamical systems for science. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.","ama":"Yao D, Muller CJ, Locatello F. Marrying causal representation learning with dynamical systems for science. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.","short":"D. Yao, C.J. Muller, F. Locatello, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024."},"date_published":"2024-12-01T00:00:00Z","arxiv":1,"conference":{"start_date":"2024-12-16","location":"Vancouver, Canada","end_date":"2024-12-16","name":"NeurIPS: Neural Information Processing Systems"},"related_material":{"link":[{"url":"https://github.com/CausalLearningAI/crl-dynamical-systems","relation":"software"}]},"publisher":"Neural Information Processing Systems Foundation","ddc":["000","550"],"external_id":{"arxiv":["2405.13888"]},"date_updated":"2025-07-10T11:51:32Z","department":[{"_id":"CaMu"},{"_id":"FrLo"}],"abstract":[{"lang":"eng","text":"Causal representation learning promises to extend causal models to hidden causal\r\nvariables from raw entangled measurements. However, most progress has focused\r\non proving identifiability results in different settings, and we are not aware of any\r\nsuccessful real-world application. At the same time, the field of dynamical systems\r\nbenefited from deep learning and scaled to countless applications but does not allow\r\nparameter identification. In this paper, we draw a clear connection between the two\r\nand their key assumptions, allowing us to apply identifiable methods developed\r\nin causal representation learning to dynamical systems. At the same time, we can\r\nleverage scalable differentiable solvers developed for differential equations to build\r\nmodels that are both identifiable and practical. Overall, we learn explicitly controllable models that isolate the trajectory-specific parameters for further downstream\r\ntasks such as out-of-distribution classification or treatment effect estimation. We\r\nexperiment with a wind simulator with partially known factors of variation. We\r\nalso apply the resulting model to real-world climate data and successfully answer\r\ndownstream causal questions in line with existing literature on climate change.\r\nCode is available at https://github.com/CausalLearningAI/crl-dynamical-systems."}],"scopus_import":"1","OA_type":"gold","date_created":"2025-02-05T07:49:00Z","OA_place":"publisher","acknowledgement":"We thank Niklas Boers for recommending the SpeedyWeather simulator and Valentino Maiorca\r\nfor guidance on Fourier transformation for SST data. We are also grateful to Shimeng Huang and Riccardo Cadei for their feedback on the treatment effect estimation experiment and to Jiale Chen and Adeel Pervez for their assistance with the solver implementation. Finally, we appreciate the anonymous reviewers for their insightful suggestions, which helped improve the manuscript. ","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"day":"01","publication":"38th Conference on Neural Information Processing Systems","intvolume":" 37","title":"Marrying causal representation learning with dynamical systems for science","article_processing_charge":"No","month":"12","volume":37,"file":[{"file_name":"2024_NeurIPS_Yao.pdf","content_type":"application/pdf","date_updated":"2025-02-05T07:44:58Z","file_size":2595855,"checksum":"fe8832367e7143876f178244385d859e","date_created":"2025-02-05T07:44:58Z","file_id":"19006","creator":"dernst","relation":"main_file","success":1,"access_level":"open_access"}],"quality_controlled":"1","file_date_updated":"2025-02-05T07:44:58Z","year":"2024","publication_status":"published","type":"conference","status":"public","alternative_title":["Advances in Neural Information Processing Systems"],"_id":"19005","has_accepted_license":"1","oa":1},{"arxiv":1,"date_published":"2024-05-01T00:00:00Z","publisher":"Oxford University Press","issue":"9","external_id":{"arxiv":["2211.06062"]},"abstract":[{"text":"Consider the family of elliptic curves En:y2=x3+n2, where n varies over positive cubefree integers. There is a rational 3-isogeny ϕ from En to E^n:y2=x3−27n2 and a dual isogeny ϕ^:E^n→En. We show that for almost all n, the rank of Selϕ(En) is 0, and the rank of Selϕ^(E^n) is determined by the number of prime factors of n that are congruent to 2mod3 and the congruence class of nmod9.","lang":"eng"}],"date_updated":"2025-07-10T11:51:44Z","extern":"1","author":[{"last_name":"Chan","first_name":"Yik Tung","id":"c4c0afc8-9262-11ed-9231-d8b0bc743af1","full_name":"Chan, Yik Tung","orcid":"0000-0001-8467-4106"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"oa_version":"Preprint","citation":{"chicago":"Chan, Stephanie. “The 3-Isogeny Selmer Groups of the Elliptic Curves Y2=x3+n2.” International Mathematics Research Notices. Oxford University Press, 2024. https://doi.org/10.1093/imrn/rnad266.","ieee":"S. Chan, “The 3-isogeny selmer groups of the elliptic curves y2=x3+n2,” International Mathematics Research Notices, vol. 2024, no. 9. Oxford University Press, pp. 7571–7593, 2024.","mla":"Chan, Stephanie. “The 3-Isogeny Selmer Groups of the Elliptic Curves Y2=x3+n2.” International Mathematics Research Notices, vol. 2024, no. 9, Oxford University Press, 2024, pp. 7571–93, doi:10.1093/imrn/rnad266.","ista":"Chan S. 2024. The 3-isogeny selmer groups of the elliptic curves y2=x3+n2. International Mathematics Research Notices. 2024(9), 7571–7593.","apa":"Chan, S. (2024). The 3-isogeny selmer groups of the elliptic curves y2=x3+n2. International Mathematics Research Notices. Oxford University Press. https://doi.org/10.1093/imrn/rnad266","ama":"Chan S. The 3-isogeny selmer groups of the elliptic curves y2=x3+n2. International Mathematics Research Notices. 2024;2024(9):7571-7593. doi:10.1093/imrn/rnad266","short":"S. Chan, International Mathematics Research Notices 2024 (2024) 7571–7593."},"year":"2024","publication_status":"published","status":"public","type":"journal_article","quality_controlled":"1","_id":"19486","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2211.06062"}],"doi":"10.1093/imrn/rnad266","publication_identifier":{"issn":["1073-7928"],"eissn":["1687-0247"]},"article_type":"original","oa":1,"day":"01","scopus_import":"1","OA_type":"green","acknowledgement":"The author would like to thank Peter Koymans and Carlo Pagano for helpful discussions.","OA_place":"repository","date_created":"2025-04-05T10:50:33Z","publication":"International Mathematics Research Notices","title":"The 3-isogeny selmer groups of the elliptic curves y2=x3+n2","page":"7571-7593","intvolume":" 2024","volume":2024,"month":"05","article_processing_charge":"No"},{"external_id":{"arxiv":["2405.15593"]},"abstract":[{"text":"We propose a new variant of the Adam optimizer [Kingma and Ba, 2014] called\r\nMICROADAM that specifically minimizes memory overheads, while maintaining\r\ntheoretical convergence guarantees. We achieve this by compressing the gradient\r\ninformation before it is fed into the optimizer state, thereby reducing its memory\r\nfootprint significantly. We control the resulting compression error via a novel\r\ninstance of the classical error feedback mechanism from distributed optimization [Seide et al., 2014, Alistarh et al., 2018, Karimireddy et al., 2019] in which\r\nthe error correction information is itself compressed to allow for practical memory\r\ngains. We prove that the resulting approach maintains theoretical convergence\r\nguarantees competitive to those of AMSGrad, while providing good practical performance. Specifically, we show that MICROADAM can be implemented efficiently\r\non GPUs: on both million-scale (BERT) and billion-scale (LLaMA) models, MICROADAM provides practical convergence competitive to that of the uncompressed\r\nAdam baseline, with lower memory usage and similar running time. Our code is\r\navailable at https://github.com/IST-DASLab/MicroAdam.","lang":"eng"}],"department":[{"_id":"DaAl"}],"date_updated":"2025-05-14T11:32:52Z","arxiv":1,"date_published":"2024-12-20T00:00:00Z","publisher":"Neural Information Processing Systems Foundation","related_material":{"link":[{"url":"https://github.com/IST-DASLab/MicroAdam","relation":"software"}]},"oa_version":"Preprint","language":[{"iso":"eng"}],"citation":{"ista":"Modoranu I-V, Safaryan M, Malinovsky G, Kurtic E, Robert T, Richtárik P, Alistarh D-A. 2024. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems. , Advances in Neural Information Processing Systems, vol. 37.","mla":"Modoranu, Ionut-Vlad, et al. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.","chicago":"Modoranu, Ionut-Vlad, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, and Dan-Adrian Alistarh. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.","ieee":"I.-V. Modoranu et al., “MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence,” in 38th Conference on Neural Information Processing Systems, 2024, vol. 37.","apa":"Modoranu, I.-V., Safaryan, M., Malinovsky, G., Kurtic, E., Robert, T., Richtárik, P., & Alistarh, D.-A. (2024). MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In 38th Conference on Neural Information Processing Systems (Vol. 37). Neural Information Processing Systems Foundation.","ama":"Modoranu I-V, Safaryan M, Malinovsky G, et al. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.","short":"I.-V. Modoranu, M. Safaryan, G. Malinovsky, E. Kurtic, T. Robert, P. Richtárik, D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024."},"acknowledged_ssus":[{"_id":"CampIT"}],"corr_author":"1","author":[{"full_name":"Modoranu, Ionut-Vlad","last_name":"Modoranu","first_name":"Ionut-Vlad","id":"449f7a18-f128-11eb-9611-9b430c0c6333"},{"last_name":"Safaryan","first_name":"Mher","id":"dd546b39-0804-11ed-9c55-ef075c39778d","full_name":"Safaryan, Mher"},{"full_name":"Malinovsky, Grigory","last_name":"Malinovsky","first_name":"Grigory"},{"last_name":"Kurtic","first_name":"Eldar","id":"47beb3a5-07b5-11eb-9b87-b108ec578218","full_name":"Kurtic, Eldar"},{"id":"de632733-1457-11f0-ae22-b5914b8c1c41","last_name":"Robert","first_name":"Thomas","full_name":"Robert, Thomas"},{"full_name":"Richtárik, Peter","first_name":"Peter","last_name":"Richtárik"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_identifier":{"issn":["1049-5258"]},"_id":"19510","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2405.15593"}],"oa":1,"status":"public","type":"conference","publication_status":"published","year":"2024","quality_controlled":"1","alternative_title":["Advances in Neural Information Processing Systems"],"title":"MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence","project":[{"_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","grant_number":"101034413","name":"IST-BRIDGE: International postdoctoral program","call_identifier":"H2020"}],"intvolume":" 37","volume":37,"article_processing_charge":"No","month":"12","day":"20","date_created":"2025-04-06T22:01:32Z","acknowledgement":"The authors thank Razvan Pascanu, Mahdi Nikdan and Soroush Tabesh for their valuable feedback, the IT department from Institute of Science and Technology Austria for the hardware support and Weights and Biases for the infrastructure to track all our experiments. Mher Safaryan has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 101034413.","ec_funded":1,"OA_place":"repository","OA_type":"green","scopus_import":"1","publication":"38th Conference on Neural Information Processing Systems"},{"citation":{"mla":"Carls-Diamante, Sidney, and Alice Laciny. “Stuck in Uncertainty: A Predictive Processing/ Active Inference Account of Procrastination-like Behaviour in Autism.” Lógoi. Revista de Filosofía, no. 45, Universidad Católica Andrés Bello, 2024, pp. 88–114, doi:10.62876/lr.vi45.6481.","ista":"Carls-Diamante S, Laciny A. 2024. Stuck in uncertainty: A predictive processing/ active inference account of procrastination-like behaviour in autism. Lógoi. Revista de Filosofía. (45), 88–114.","ieee":"S. Carls-Diamante and A. Laciny, “Stuck in uncertainty: A predictive processing/ active inference account of procrastination-like behaviour in autism,” Lógoi. Revista de Filosofía, no. 45. Universidad Católica Andrés Bello, pp. 88–114, 2024.","chicago":"Carls-Diamante, Sidney, and Alice Laciny. “Stuck in Uncertainty: A Predictive Processing/ Active Inference Account of Procrastination-like Behaviour in Autism.” Lógoi. Revista de Filosofía. Universidad Católica Andrés Bello, 2024. https://doi.org/10.62876/lr.vi45.6481.","short":"S. Carls-Diamante, A. Laciny, Lógoi. Revista de Filosofía (2024) 88–114.","ama":"Carls-Diamante S, Laciny A. Stuck in uncertainty: A predictive processing/ active inference account of procrastination-like behaviour in autism. Lógoi Revista de Filosofía. 2024;(45):88-114. doi:10.62876/lr.vi45.6481","apa":"Carls-Diamante, S., & Laciny, A. (2024). Stuck in uncertainty: A predictive processing/ active inference account of procrastination-like behaviour in autism. Lógoi. Revista de Filosofía. Universidad Católica Andrés Bello. https://doi.org/10.62876/lr.vi45.6481"},"oa_version":"Published Version","language":[{"iso":"eng"}],"author":[{"last_name":"Carls-Diamante","first_name":"Sidney","full_name":"Carls-Diamante, Sidney"},{"orcid":"0000-0002-5485-1391","full_name":"Laciny, Alice","id":"accace3f-3f77-11eb-b1df-f1221b04cb95","first_name":"Alice","last_name":"Laciny"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","corr_author":"1","abstract":[{"text":"Un fenómeno a menudo asociado con el autismo es un modo atípico de función ejecutiva, cuyas manifestaciones incluyen dificultad para iniciar tareas. En algunos casos, esto va acompañado de sentimientos de inercia y sensaciones que pueden describirse como inquietud y parálisis simultáneas. En consecuencia, la dificultad para iniciar las tareas puede dar lugar a la procrastinación, ya sea simplemente posponiendo el trabajo en la tarea objetivo o realizando otras tareas no relacionadas antes de dedicarse a la tarea objetivo. Curiosamente, sin embargo, también está documentado que, una vez iniciada una tarea, los autistas pueden centrarse en ella intensamente y durante periodos prolongados de tiempo, especialmente cuando les resulta interesante.
\r\nEste trabajo utiliza el procesamiento predictivo y la inferencia activa para modelar la relación entre la función ejecutiva, la procrastinación y la hiperfocalización en el autismo. Este modelo integra las causas conocidas y propuestas de los déficits en la función ejecutiva y el papel que desempeña el interés en la regulación de la atención y la motivación. El modelo propone que la procrastinación es el resultado de procesos diferenciales de minimización de errores de predicción, como la ponderación de estímulos sensoriales. Se discuten los vínculos con modelos propuestos previamente, como la coherencia central débil (CCC), y la teoría de los priores altos e inflexibles de los errores de predicción en el autismo (HIPPEA).","lang":"spa"},{"lang":"eng","text":"A phenomenon often associated with autism is an atypical mode of executive function, manifestations of which include difficulty in initiating tasks. In some cases, this is accompanied by feelings of inertia and sensations that can be described as simultaneous restlessness and paralysis. Consequently, difficulty in getting started on tasks can result in procrastination, either by simply postponing working on the target task or by performing other unrelated tasks before engaging in the target task. Interestingly, however, it is also documented that once a task has been started, autistic persons may focus on it intensely and for prolonged periods of time, especially when it is interesting to them.This paper uses predictive processing and active inference to model therelationship between executive function, procrastination, and hyperfocus in autism. This model integrates the known and proposed causes of deficits in executive function and the role played by interest in attention regulation and motivation. The model proposes that procrastination is the outcome of differential prediction-error minimizing processes, such as weighting of sensory stimuli. Links to previously proposed models such as weak central coherence (WCC), and the theory of high, inflexible priors of prediction errors in autism (HIPPEA) are discussed"}],"date_updated":"2025-09-09T08:51:00Z","ddc":["570"],"publisher":"Universidad Católica Andrés Bello","issue":"45","PlanS_conform":"1","date_published":"2024-03-19T00:00:00Z","file":[{"success":1,"access_level":"open_access","date_created":"2025-09-09T08:48:53Z","checksum":"04c950f26ad68455c3a303c87ccf2349","relation":"main_file","creator":"dernst","file_id":"20317","content_type":"application/pdf","date_updated":"2025-09-09T08:48:53Z","file_size":354124,"file_name":"2025_Logoi_CarlsDiamante.pdf"}],"article_processing_charge":"Yes","month":"03","title":"Stuck in uncertainty: A predictive processing/ active inference account of procrastination-like behaviour in autism","page":"88-114","publication":"Lógoi. Revista de Filosofía","day":"19","tmp":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","short":"CC BY-NC-SA (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","image":"/images/cc_by_nc_sa.png"},"date_created":"2025-07-21T10:35:39Z","acknowledgement":"Sidney Carls-Diamante was supported by the Federal Ministry of Education and Research (BMBF) and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments. Alice Laciny was supported by the 2021 UNESCO L’Oréal grant for women in science, awarded for the project “Neurodiversity and anthropomorphism in social insect research”.","OA_place":"publisher","OA_type":"gold","DOAJ_listed":"1","oa":1,"article_type":"original","has_accepted_license":"1","doi":"10.62876/lr.vi45.6481","publication_identifier":{"issn":["2790-5144"],"eissn":["1316-693X"]},"_id":"20060","type":"journal_article","status":"public","file_date_updated":"2025-09-09T08:48:53Z","publication_status":"published","year":"2024","quality_controlled":"1"},{"page":"1767-1769","title":"Questioning claims of monitoring the Michael addition reaction at the single-molecule level","intvolume":" 16","volume":16,"article_processing_charge":"No","month":"11","pmid":1,"day":"01","OA_type":"closed access","scopus_import":"1","date_created":"2025-10-23T12:16:57Z","publication":"Nature Chemistry","_id":"20527","doi":"10.1038/s41557-024-01631-9","publication_identifier":{"eissn":["1755-4349"],"issn":["1755-4330"]},"article_type":"letter_note","year":"2024","publication_status":"published","type":"journal_article","status":"public","quality_controlled":"1","language":[{"iso":"eng"}],"oa_version":"None","citation":{"apa":"Venkataraman, L., & van Ruitenbeek, J. (2024). Questioning claims of monitoring the Michael addition reaction at the single-molecule level. Nature Chemistry. Springer Nature. https://doi.org/10.1038/s41557-024-01631-9","ama":"Venkataraman L, van Ruitenbeek J. Questioning claims of monitoring the Michael addition reaction at the single-molecule level. Nature Chemistry. 2024;16(11):1767-1769. doi:10.1038/s41557-024-01631-9","short":"L. Venkataraman, J. van Ruitenbeek, Nature Chemistry 16 (2024) 1767–1769.","ista":"Venkataraman L, van Ruitenbeek J. 2024. Questioning claims of monitoring the Michael addition reaction at the single-molecule level. Nature Chemistry. 16(11), 1767–1769.","mla":"Venkataraman, Latha, and Jan van Ruitenbeek. “Questioning Claims of Monitoring the Michael Addition Reaction at the Single-Molecule Level.” Nature Chemistry, vol. 16, no. 11, Springer Nature, 2024, pp. 1767–69, doi:10.1038/s41557-024-01631-9.","chicago":"Venkataraman, Latha, and Jan van Ruitenbeek. “Questioning Claims of Monitoring the Michael Addition Reaction at the Single-Molecule Level.” Nature Chemistry. Springer Nature, 2024. https://doi.org/10.1038/s41557-024-01631-9.","ieee":"L. Venkataraman and J. van Ruitenbeek, “Questioning claims of monitoring the Michael addition reaction at the single-molecule level,” Nature Chemistry, vol. 16, no. 11. Springer Nature, pp. 1767–1769, 2024."},"extern":"1","author":[{"id":"9ebb78a5-cc0d-11ee-8322-fae086a32caf","first_name":"Latha","last_name":"Venkataraman","full_name":"Venkataraman, Latha","orcid":"0000-0002-6957-6089"},{"full_name":"van Ruitenbeek, Jan","last_name":"van Ruitenbeek","first_name":"Jan"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"pmid":["39313629"]},"abstract":[{"text":"Arising from C. Yang et al. Nature Chemistry https://doi.org/10.1038/s41557-023-01212-2 (2023)\r\n\r\nIn this work Yang et al.1 claim that an enantioselective Michael addition reaction with a barrier of 16 kcal mol−1 occurs at the single-molecule level in frozen solvent by measuring fluctuations in current flowing across graphene-based molecular devices. The article, however, contains major scientific errors that undermine their conclusions. We highlight issues with the fabrication of the devices, a lack of characterization, discrepancies between theory and experiment, unreliable inelastic electron tunnelling spectra (IETS) and a perceived misinterpretation of noise as evidence of reaction.","lang":"eng"}],"date_updated":"2025-10-23T12:58:52Z","date_published":"2024-11-01T00:00:00Z","publisher":"Springer Nature","issue":"11"},{"_id":"20615","doi":"10.1142/13476","publication_identifier":{"eisbn":["9789811278556"],"isbn":["9789811278532"]},"date_updated":"2025-11-10T15:28:49Z","abstract":[{"lang":"eng","text":"Spin/Pin-structures on vector bundles have long featured prominently in differential geometry, in particular providing part of the foundation for the original proof of the renowned Atiyah–Singer Index Theory. More recently, they have underpinned the symplectic topology foundations of the so-called real sector of the mirror symmetry of string theory.\r\n\r\nThis semi-expository three-part monograph provides an accessible introduction to Spin- and Pin-structures in general, demonstrates their role in the orientability considerations in symplectic topology, and presents their applications in enumerative geometry.\r\n\r\nPart I contains a systematic treatment of Spin/Pin-structures from different topological perspectives and may be suitable for an advanced undergraduate reading seminar. This leads to Part II, which systematically studies orientability problems for the determinants of real Cauchy–Riemann operators on vector bundles. Part III introduces enumerative geometry of curves in complex projective varieties and in symplectic manifolds, demonstrating some applications of the first two parts in the process. Two appendices review the Čech cohomology perspective on fiber bundles and Lie group covering spaces."}],"quality_controlled":"1","date_published":"2024-01-01T00:00:00Z","publication_status":"published","year":"2024","status":"public","type":"book","publisher":"World Scientific Publishing","language":[{"iso":"eng"}],"title":"Spin/Pin-structures and real enumerative geometry","oa_version":"None","month":"01","article_processing_charge":"No","citation":{"mla":"Chen, Xujia, and Aleksey Zinger. Spin/Pin-Structures and Real Enumerative Geometry. World Scientific Publishing, 2024, doi:10.1142/13476.","ista":"Chen X, Zinger A. 2024. Spin/Pin-structures and real enumerative geometry, World Scientific Publishing,p.","chicago":"Chen, Xujia, and Aleksey Zinger. Spin/Pin-Structures and Real Enumerative Geometry. World Scientific Publishing, 2024. https://doi.org/10.1142/13476.","ieee":"X. Chen and A. Zinger, Spin/Pin-structures and real enumerative geometry. World Scientific Publishing, 2024.","apa":"Chen, X., & Zinger, A. (2024). Spin/Pin-structures and real enumerative geometry. World Scientific Publishing. https://doi.org/10.1142/13476","short":"X. Chen, A. Zinger, Spin/Pin-Structures and Real Enumerative Geometry, World Scientific Publishing, 2024.","ama":"Chen X, Zinger A. Spin/Pin-Structures and Real Enumerative Geometry. World Scientific Publishing; 2024. doi:10.1142/13476"},"OA_type":"closed access","scopus_import":"1","date_created":"2025-11-10T08:40:10Z","day":"01","extern":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"id":"968ad14a-fd86-11ee-a420-ea29715511a3","last_name":"Chen","first_name":"Xujia","full_name":"Chen, Xujia"},{"last_name":"Zinger","first_name":"Aleksey","full_name":"Zinger, Aleksey"}]},{"volume":6,"article_processing_charge":"No","month":"04","title":"L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning","intvolume":" 6","publication":"Proceedings of Machine Learning and Systems ","day":"01","date_created":"2024-08-22T08:29:25Z","oa":1,"main_file_link":[{"url":"https://proceedings.mlsys.org/paper_files/paper/2024/hash/9069a8976ff06f6443e7f4172990a580-Abstract-Conference.html","open_access":"1"}],"_id":"17456","type":"conference","status":"public","year":"2024","publication_status":"published","quality_controlled":"1","citation":{"mla":"Markov, Ilia, et al. “L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel Deep Learning.” Proceedings of Machine Learning and Systems , edited by P. Gibbons et al., vol. 6, Association for Computing Machinery, 2024.","ista":"Markov I, Alimohammadi K, Frantar E, Alistarh D-A. 2024. L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. Proceedings of Machine Learning and Systems . MLSys: Machine Learning and Systems vol. 6.","chicago":"Markov, Ilia, Kaveh Alimohammadi, Elias Frantar, and Dan-Adrian Alistarh. “L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel Deep Learning.” In Proceedings of Machine Learning and Systems , edited by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6. Association for Computing Machinery, 2024.","ieee":"I. Markov, K. Alimohammadi, E. Frantar, and D.-A. Alistarh, “L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning,” in Proceedings of Machine Learning and Systems , Athens, Greece, 2024, vol. 6.","apa":"Markov, I., Alimohammadi, K., Frantar, E., & Alistarh, D.-A. (2024). L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine Learning and Systems (Vol. 6). Athens, Greece: Association for Computing Machinery.","ama":"Markov I, Alimohammadi K, Frantar E, Alistarh D-A. L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems . Vol 6. Association for Computing Machinery; 2024.","short":"I. Markov, K. Alimohammadi, E. Frantar, D.-A. Alistarh, in:, P. Gibbons, G. Pekhimenko, C. De Sa (Eds.), Proceedings of Machine Learning and Systems , Association for Computing Machinery, 2024."},"oa_version":"Published Version","language":[{"iso":"eng"}],"author":[{"first_name":"Ilia","last_name":"Markov","id":"D0CF4148-C985-11E9-8066-0BDEE5697425","full_name":"Markov, Ilia"},{"full_name":"Alimohammadi, Kaveh","last_name":"Alimohammadi","first_name":"Kaveh"},{"full_name":"Frantar, Elias","first_name":"Elias","last_name":"Frantar","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f"},{"orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","last_name":"Alistarh","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","corr_author":"1","abstract":[{"text":"Data-parallel distributed training of deep neural networks (DNN) has gained very widespread adoption, but can still experience communication bottlenecks. To address this issue, entire families of compression mechanisms have been developed, including quantization, sparsification, and low-rank approximation, some of which are seeing significant practical adoption. Despite this progress, almost all known compression schemes apply compression uniformly across DNN layers, although layers are heterogeneous in terms of parameter count and their impact on model accuracy.In this work, we provide a general framework for adapting the degree of compression across the model's layers dynamically during training, improving the overall compression, while leading to substantial speedups, without sacrificing accuracy. Our framework, called L-GreCo, is based on an adaptive algorithm, which automatically picks the optimal compression parameters for model layers guaranteeing the best compression ratio while satisfying an error constraint. Extensive experiments over image classification and language modeling tasks shows that L-GreCo is effective across all existing families of compression methods, and achieves up to 2.5\r\n×\r\n training speedup and up to 5\r\n×\r\n compression improvement over efficient implementations of existing approaches, while recovering full accuracy. Moreover, L-GreCo is complementary to existing adaptive algorithms, improving their compression ratio by 50\\% and practical throughput by 66\\%. An anonymized implementation is available at https://github.com/LGrCo/L-GreCo.","lang":"eng"}],"department":[{"_id":"DaAl"}],"date_updated":"2026-06-18T17:55:24Z","external_id":{"arxiv":["2210.17357"]},"editor":[{"first_name":"P.","last_name":"Gibbons","full_name":"Gibbons, P."},{"full_name":"Pekhimenko, G.","first_name":"G.","last_name":"Pekhimenko"},{"full_name":"De Sa, C.","last_name":"De Sa","first_name":"C."}],"ddc":["000"],"related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"17490"}]},"publisher":"Association for Computing Machinery","conference":{"start_date":"2024-04-22","location":"Athens, Greece","name":"MLSys: Machine Learning and Systems","end_date":"2024-04-22"},"arxiv":1,"date_published":"2024-04-01T00:00:00Z"},{"language":[{"iso":"eng"}],"oa_version":"Published Version","citation":{"chicago":"Pervez, Adeel A, Francesco Locatello, and Efstratios Gavves. “Mechanistic Neural Networks for Scientific Machine Learning.” In Proceedings of the 41st International Conference on Machine Learning, 235:40484–501. ML Research Press, 2024.","ieee":"A. A. Pervez, F. Locatello, and E. Gavves, “Mechanistic neural networks for scientific machine learning,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 40484–40501.","mla":"Pervez, Adeel A., et al. “Mechanistic Neural Networks for Scientific Machine Learning.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 40484–501.","ista":"Pervez AA, Locatello F, Gavves E. 2024. Mechanistic neural networks for scientific machine learning. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 40484–40501.","apa":"Pervez, A. A., Locatello, F., & Gavves, E. (2024). Mechanistic neural networks for scientific machine learning. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 40484–40501). Vienna, Austria: ML Research Press.","short":"A.A. Pervez, F. Locatello, E. Gavves, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 40484–40501.","ama":"Pervez AA, Locatello F, Gavves E. Mechanistic neural networks for scientific machine learning. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:40484-40501."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Pervez, Adeel A","first_name":"Adeel A","last_name":"Pervez","id":"fca6d90c-d47f-11ee-bc87-93ff51604981"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","first_name":"Francesco","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"full_name":"Gavves, Efstratios","last_name":"Gavves","first_name":"Efstratios"}],"ddc":["000"],"external_id":{"arxiv":["2402.13077"]},"date_updated":"2026-06-18T17:59:46Z","department":[{"_id":"FrLo"}],"abstract":[{"text":"This paper presents Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations, revealing the underlying dynamics of data and enhancing interpretability and efficiency in data modeling. Central to our approach is a novel Relaxed Linear Programming Solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs. This integrates well with neural networks and surpasses the limitations of traditional ODE solvers enabling scalable GPU parallel processing. Overall, Mechanistic Neural Networks demonstrate their versatility for scientific machine learning applications, adeptly managing tasks from equation discovery to dynamic systems modeling. We prove their comprehensive capabilities in analyzing and interpreting complex scientific data across various applications, showing significant performance against specialized state-of-the-art methods. Source code is available at https://github.com/alpz/mech-nn.","lang":"eng"}],"date_published":"2024-09-01T00:00:00Z","arxiv":1,"conference":{"start_date":"2024-07-21","location":"Vienna, Austria","name":"ICML: International Conference on Machine Learning","end_date":"2024-07-27"},"related_material":{"link":[{"url":"https://github.com/alpz/mech-nn","relation":"software"}]},"publisher":"ML Research Press","intvolume":" 235","page":"40484-40501","title":"Mechanistic neural networks for scientific machine learning","month":"09","article_processing_charge":"No","volume":235,"scopus_import":"1","OA_type":"diamond","date_created":"2024-09-22T22:01:43Z","OA_place":"publisher","day":"01","publication":"Proceedings of the 41st International Conference on Machine Learning","_id":"18114","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2402.13077"}],"publication_identifier":{"eissn":["2640-3498"]},"oa":1,"quality_controlled":"1","year":"2024","publication_status":"published","type":"conference","status":"public","alternative_title":["PMLR"]},{"publication":"Proceedings of the 41st International Conference on Machine Learning","day":"01","date_created":"2024-09-22T22:01:44Z","acknowledgement":"The authors would like to thank Eldar Kurtic for experimental support and useful suggestions throughout the project","scopus_import":"1","volume":235,"month":"09","article_processing_charge":"No","title":"RoSA: Accurate parameter-efficient fine-tuning via robust adaptation","page":"38187-38206","intvolume":" 235","status":"public","type":"conference","publication_status":"published","year":"2024","quality_controlled":"1","oa":1,"publication_identifier":{"eissn":["2640-3498"]},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2401.04679"}],"_id":"18117","author":[{"full_name":"Nikdan, Mahdi","id":"66374281-f394-11eb-9cf6-869147deecc0","last_name":"Nikdan","first_name":"Mahdi"},{"first_name":"Soroush","last_name":"Tabesh","id":"06000900-6068-11ef-8d61-c2472ef2e752","full_name":"Tabesh, Soroush","orcid":"0009-0003-4119-6281"},{"full_name":"Crncevic, Elvir","id":"41888001-440d-11ef-8299-d0e838b8185e","first_name":"Elvir","last_name":"Crncevic"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","corr_author":"1","citation":{"short":"M. Nikdan, S. Tabesh, E. Crncevic, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 38187–38206.","ama":"Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.","apa":"Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206). Vienna, Austria: ML Research Press.","ista":"Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. 2024. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 235, 38187–38206.","mla":"Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 38187–206.","ieee":"M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.","chicago":"Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In Proceedings of the 41st International Conference on Machine Learning, 235:38187–206. ML Research Press, 2024."},"oa_version":"Preprint","language":[{"iso":"eng"}],"related_material":{"link":[{"relation":"software","url":"https://github.com/IST-DASLab/RoSA"}]},"publisher":"ML Research Press","conference":{"name":"ICML: International Conference on Machine Learning","end_date":"2024-07-27","location":"Vienna, Austria","start_date":"2024-07-21"},"arxiv":1,"date_published":"2024-09-01T00:00:00Z","abstract":[{"text":"We investigate parameter-efficient fine-tuning (PEFT) methods that can provide good accuracy under limited computational and memory budgets in the context of large language models (LLMs). We present a new PEFT method called Robust Adaptation (RoSA) inspired by robust principal component analysis that jointly trains low-rank\r\n and highly-sparse components on top of a set of fixed pretrained weights to efficiently approximate the performance of a full-fine-tuning (FFT) solution. Across a series of challenging generative tasks such as grade-school math and SQL query generation, which require fine-tuning for good performance, we show that RoSA outperforms LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter budget, and can even recover the performance of FFT on some tasks. We provide system support for RoSA to complement the training algorithm, specifically in the form of sparse GPU kernels which enable memory- and computationally-efficient training, and show that it is also compatible with low-precision base weights, resulting in the first joint representation combining quantization, low-rank and sparse approximations. Our code is available at https://github.com/IST-DASLab/RoSA.","lang":"eng"}],"department":[{"_id":"DaAl"},{"_id":"GradSch"}],"date_updated":"2024-10-01T08:22:01Z","external_id":{"arxiv":["2401.04679"]}},{"date_updated":"2026-07-14T11:10:45Z","abstract":[{"text":"We study the distribution of consecutive sums of two squares\r\nin arithmetic progressions. If {En}n∈N is the sequence of\r\nsums of two squares in increasing order, we show that for\r\nany modulus q and any congruence classes a1, a2, a3 mod q\r\nwhich are admissible in the sense that there are solutions\r\nto x2 + y2 ≡ ai mod q, there exist infinitely many n with\r\nEn+i−1 ≡ ai mod q, for i =1, 2, 3. We also show that for\r\nany r1, r2 ≥ 1, there exist infinitely many n with En+i−1 ≡\r\na1 mod q for 1 ≤ i ≤ r1 and En+i−1 ≡ a2 mod q for\r\nr1 +1 ≤ i ≤ r1 + r2","lang":"eng"}],"external_id":{"arxiv":["2306.12855"]},"publisher":"Elsevier","date_published":"2024-11-01T00:00:00Z","arxiv":1,"citation":{"mla":"Kimmel, Noam, and Vivian Zieve Kuperberg. “Consecutive Runs of Sums of Two Squares.” Journal of Number Theory, vol. 264, Elsevier, 2024, pp. 135–47, doi:10.1016/j.jnt.2024.05.003.","ista":"Kimmel N, Kuperberg VZ. 2024. Consecutive runs of sums of two squares. Journal of Number Theory. 264, 135–147.","ieee":"N. Kimmel and V. Z. Kuperberg, “Consecutive runs of sums of two squares,” Journal of Number Theory, vol. 264. Elsevier, pp. 135–147, 2024.","chicago":"Kimmel, Noam, and Vivian Zieve Kuperberg. “Consecutive Runs of Sums of Two Squares.” Journal of Number Theory. Elsevier, 2024. https://doi.org/10.1016/j.jnt.2024.05.003.","short":"N. Kimmel, V.Z. Kuperberg, Journal of Number Theory 264 (2024) 135–147.","ama":"Kimmel N, Kuperberg VZ. Consecutive runs of sums of two squares. Journal of Number Theory. 2024;264:135-147. doi:10.1016/j.jnt.2024.05.003","apa":"Kimmel, N., & Kuperberg, V. Z. (2024). Consecutive runs of sums of two squares. Journal of Number Theory. Elsevier. https://doi.org/10.1016/j.jnt.2024.05.003"},"language":[{"iso":"eng"}],"oa_version":"Preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Kimmel, Noam","first_name":"Noam","last_name":"Kimmel"},{"full_name":"Kuperberg, Vivian Zieve","id":"c3bac823-112d-11f0-a3f5-c264f852e697","last_name":"Kuperberg","first_name":"Vivian Zieve"}],"extern":"1","article_type":"original","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2306.12855"}],"_id":"22197","doi":"10.1016/j.jnt.2024.05.003","publication_identifier":{"issn":["0022-314X"]},"quality_controlled":"1","publication_status":"published","year":"2024","type":"journal_article","status":"public","month":"11","article_processing_charge":"No","volume":264,"intvolume":" 264","title":"Consecutive runs of sums of two squares","page":"135-147","publication":"Journal of Number Theory","scopus_import":"1","OA_type":"green","OA_place":"repository","date_created":"2026-06-29T12:58:28Z","day":"01"}]