[{"oa_version":"None","date_created":"2024-02-14T12:05:50Z","department":[{"_id":"NiBa"}],"title":"Hybrid Zones","publication":"Encyclopedia of Life Sciences","publisher":"Wiley","abstract":[{"lang":"eng","text":"Hybrid zones are narrow geographic regions where different populations, races or interbreeding species meet and mate, producing mixed ‘hybrid’ offspring. They are relatively common and can be found in a diverse range of organisms and environments. The study of hybrid zones has played an important role in our understanding of the origin of species, with hybrid zones having been described as ‘natural laboratories’. This is because they allow us to study,in situ, the conditions and evolutionary forces that enable divergent taxa to remain distinct despite some ongoing gene exchange between them."}],"quality_controlled":"1","series_title":"eLS","author":[{"last_name":"Stankowski","first_name":"Sean","id":"43161670-5719-11EA-8025-FABC3DDC885E","full_name":"Stankowski, Sean"},{"full_name":"Shipilina, Daria","id":"428A94B0-F248-11E8-B48F-1D18A9856A87","first_name":"Daria","orcid":"0000-0002-1145-9226","last_name":"Shipilina"},{"last_name":"Westram","orcid":"0000-0003-1050-4969","first_name":"Anja M","id":"3C147470-F248-11E8-B48F-1D18A9856A87","full_name":"Westram, Anja M"}],"date_published":"2021-05-28T00:00:00Z","doi":"10.1002/9780470015902.a0029355","date_updated":"2024-02-19T09:54:18Z","article_processing_charge":"No","_id":"14984","intvolume":"         2","publication_status":"published","year":"2021","volume":2,"type":"book_chapter","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Stankowski, Sean, Daria Shipilina, and Anja M Westram. “Hybrid Zones.” In <i>Encyclopedia of Life Sciences</i>, Vol. 2. ELS. Wiley, 2021. <a href=\"https://doi.org/10.1002/9780470015902.a0029355\">https://doi.org/10.1002/9780470015902.a0029355</a>.","ieee":"S. Stankowski, D. Shipilina, and A. M. Westram, “Hybrid Zones,” in <i>Encyclopedia of Life Sciences</i>, vol. 2, Wiley, 2021.","apa":"Stankowski, S., Shipilina, D., &#38; Westram, A. M. (2021). Hybrid Zones. In <i>Encyclopedia of Life Sciences</i> (Vol. 2). Wiley. <a href=\"https://doi.org/10.1002/9780470015902.a0029355\">https://doi.org/10.1002/9780470015902.a0029355</a>","ama":"Stankowski S, Shipilina D, Westram AM. Hybrid Zones. In: <i>Encyclopedia of Life Sciences</i>. Vol 2. eLS. Wiley; 2021. doi:<a href=\"https://doi.org/10.1002/9780470015902.a0029355\">10.1002/9780470015902.a0029355</a>","ista":"Stankowski S, Shipilina D, Westram AM. 2021.Hybrid Zones. In: Encyclopedia of Life Sciences. vol. 2.","mla":"Stankowski, Sean, et al. “Hybrid Zones.” <i>Encyclopedia of Life Sciences</i>, vol. 2, Wiley, 2021, doi:<a href=\"https://doi.org/10.1002/9780470015902.a0029355\">10.1002/9780470015902.a0029355</a>.","short":"S. Stankowski, D. Shipilina, A.M. Westram, in:, Encyclopedia of Life Sciences, Wiley, 2021."},"publication_identifier":{"isbn":["9780470016176"],"eisbn":["9780470015902"]},"language":[{"iso":"eng"}],"month":"05","status":"public","day":"28"},{"year":"2021","publication_status":"published","place":"Cham","_id":"14987","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"book_chapter","edition":"2","page":"1395-1397","month":"10","publication_identifier":{"isbn":["9783030634155"],"eisbn":["9783030634162"]},"language":[{"iso":"eng"}],"citation":{"ama":"Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. <i>Computer Vision</i>. 2nd ed. Cham: Springer; 2021:1395-1397. doi:<a href=\"https://doi.org/10.1007/978-3-030-63416-2_874\">10.1007/978-3-030-63416-2_874</a>","short":"C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397.","mla":"Lampert, Christoph. “Zero-Shot Learning.” <i>Computer Vision</i>, edited by Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:<a href=\"https://doi.org/10.1007/978-3-030-63416-2_874\">10.1007/978-3-030-63416-2_874</a>.","ista":"Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397.","ieee":"C. Lampert, “Zero-Shot Learning,” in <i>Computer Vision</i>, 2nd ed., K. Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.","chicago":"Lampert, Christoph. “Zero-Shot Learning.” In <i>Computer Vision</i>, edited by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-63416-2_874\">https://doi.org/10.1007/978-3-030-63416-2_874</a>.","apa":"Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), <i>Computer Vision</i> (2nd ed., pp. 1395–1397). Cham: Springer. <a href=\"https://doi.org/10.1007/978-3-030-63416-2_874\">https://doi.org/10.1007/978-3-030-63416-2_874</a>"},"day":"13","status":"public","date_created":"2024-02-14T14:05:32Z","oa_version":"None","publisher":"Springer","publication":"Computer Vision","department":[{"_id":"ChLa"}],"title":"Zero-Shot Learning","quality_controlled":"1","abstract":[{"lang":"eng","text":"The goal of zero-shot learning is to construct a classifier that can identify object classes for which no training examples are available. When training data for some of the object classes is available but not for others, the name generalized zero-shot learning is commonly used.\r\nIn a wider sense, the phrase zero-shot is also used to describe other machine learning-based approaches that require no training data from the problem of interest, such as zero-shot action recognition or zero-shot machine translation."}],"article_processing_charge":"No","date_updated":"2024-02-19T10:59:04Z","doi":"10.1007/978-3-030-63416-2_874","author":[{"orcid":"0000-0001-8622-7887","last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph"}],"date_published":"2021-10-13T00:00:00Z","editor":[{"full_name":"Ikeuchi, Katsushi","last_name":"Ikeuchi","first_name":"Katsushi"}]},{"type":"research_data_reference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"14988","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5747100"}],"year":"2021","has_accepted_license":"1","status":"public","oa":1,"day":"01","ddc":["580"],"citation":{"apa":"Johnson, A. J. (2021). Raw data from Johnson et al, PNAS, 2021. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5747100\">https://doi.org/10.5281/ZENODO.5747100</a>","chicago":"Johnson, Alexander J. “Raw Data from Johnson et Al, PNAS, 2021.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5747100\">https://doi.org/10.5281/ZENODO.5747100</a>.","ieee":"A. J. Johnson, “Raw data from Johnson et al, PNAS, 2021.” Zenodo, 2021.","ista":"Johnson AJ. 2021. Raw data from Johnson et al, PNAS, 2021, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5747100\">10.5281/ZENODO.5747100</a>.","short":"A.J. Johnson, (2021).","mla":"Johnson, Alexander J. <i>Raw Data from Johnson et Al, PNAS, 2021</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5747100\">10.5281/ZENODO.5747100</a>.","ama":"Johnson AJ. Raw data from Johnson et al, PNAS, 2021. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5747100\">10.5281/ZENODO.5747100</a>"},"month":"12","department":[{"_id":"JiFr"}],"title":"Raw data from Johnson et al, PNAS, 2021","publisher":"Zenodo","oa_version":"Published Version","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_created":"2024-02-14T14:13:48Z","date_published":"2021-12-01T00:00:00Z","author":[{"id":"46A62C3A-F248-11E8-B48F-1D18A9856A87","full_name":"Johnson, Alexander J","last_name":"Johnson","orcid":"0000-0002-2739-8843","first_name":"Alexander J"}],"doi":"10.5281/ZENODO.5747100","date_updated":"2024-02-19T11:06:09Z","related_material":{"record":[{"id":"9887","relation":"used_in_publication","status":"public"}]},"article_processing_charge":"No","abstract":[{"lang":"eng","text":"Raw data generated from the publication - The TPLATE complex mediates membrane bending during plant clathrin-mediated endocytosis by Johnson et al., 2021 In PNAS"}]},{"publication_status":"published","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1907.13631","open_access":"1"}],"intvolume":"         2","language":[{"iso":"eng"}],"project":[{"call_identifier":"FP7","name":"Random matrices, universality and disordered quantum systems","grant_number":"338804","_id":"258DCDE6-B435-11E9-9278-68D0E5697425"}],"month":"05","day":"21","quality_controlled":"1","scopus_import":"1","date_updated":"2024-02-19T08:30:00Z","doi":"10.2140/pmp.2021.2.221","article_processing_charge":"No","author":[{"full_name":"Alt, Johannes","id":"36D3D8B6-F248-11E8-B48F-1D18A9856A87","first_name":"Johannes","last_name":"Alt"},{"id":"4DBD5372-F248-11E8-B48F-1D18A9856A87","full_name":"Erdös, László","last_name":"Erdös","orcid":"0000-0001-5366-9603","first_name":"László"},{"orcid":"0000-0002-4821-3297","last_name":"Krüger","first_name":"Torben H","id":"3020C786-F248-11E8-B48F-1D18A9856A87","full_name":"Krüger, Torben H"}],"date_published":"2021-05-21T00:00:00Z","year":"2021","_id":"15013","type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"221-280","volume":2,"publication_identifier":{"issn":["2690-0998"],"eissn":["2690-1005"]},"citation":{"apa":"Alt, J., Erdös, L., &#38; Krüger, T. H. (2021). Spectral radius of random matrices with independent entries. <i>Probability and Mathematical Physics</i>. Mathematical Sciences Publishers. <a href=\"https://doi.org/10.2140/pmp.2021.2.221\">https://doi.org/10.2140/pmp.2021.2.221</a>","chicago":"Alt, Johannes, László Erdös, and Torben H Krüger. “Spectral Radius of Random Matrices with Independent Entries.” <i>Probability and Mathematical Physics</i>. Mathematical Sciences Publishers, 2021. <a href=\"https://doi.org/10.2140/pmp.2021.2.221\">https://doi.org/10.2140/pmp.2021.2.221</a>.","ieee":"J. Alt, L. Erdös, and T. H. Krüger, “Spectral radius of random matrices with independent entries,” <i>Probability and Mathematical Physics</i>, vol. 2, no. 2. Mathematical Sciences Publishers, pp. 221–280, 2021.","short":"J. Alt, L. Erdös, T.H. Krüger, Probability and Mathematical Physics 2 (2021) 221–280.","mla":"Alt, Johannes, et al. “Spectral Radius of Random Matrices with Independent Entries.” <i>Probability and Mathematical Physics</i>, vol. 2, no. 2, Mathematical Sciences Publishers, 2021, pp. 221–80, doi:<a href=\"https://doi.org/10.2140/pmp.2021.2.221\">10.2140/pmp.2021.2.221</a>.","ista":"Alt J, Erdös L, Krüger TH. 2021. Spectral radius of random matrices with independent entries. Probability and Mathematical Physics. 2(2), 221–280.","ama":"Alt J, Erdös L, Krüger TH. Spectral radius of random matrices with independent entries. <i>Probability and Mathematical Physics</i>. 2021;2(2):221-280. doi:<a href=\"https://doi.org/10.2140/pmp.2021.2.221\">10.2140/pmp.2021.2.221</a>"},"article_type":"original","external_id":{"arxiv":["1907.13631"]},"issue":"2","oa":1,"status":"public","acknowledgement":"Partially supported by ERC Starting Grant RandMat No. 715539 and the SwissMap grant of Swiss National Science Foundation. Partially supported by ERC Advanced Grant RanMat No. 338804. Partially supported by the Hausdorff Center for Mathematics in Bonn.","oa_version":"Preprint","ec_funded":1,"date_created":"2024-02-18T23:01:03Z","publisher":"Mathematical Sciences Publishers","title":"Spectral radius of random matrices with independent entries","department":[{"_id":"LaEr"}],"publication":"Probability and Mathematical Physics","abstract":[{"text":"We consider random n×n matrices X with independent and centered entries and a general variance profile. We show that the spectral radius of X converges with very high probability to the square root of the spectral radius of the variance matrix of X when n tends to infinity. We also establish the optimal rate of convergence, that is a new result even for general i.i.d. matrices beyond the explicitly solvable Gaussian cases. The main ingredient is the proof of the local inhomogeneous circular law [arXiv:1612.07776] at the spectral edge.","lang":"eng"}],"arxiv":1},{"month":"10","citation":{"apa":"Peruzzo, M., Hassani, F., Szep, G., Trioni, A., Redchenko, E., Zemlicka, M., &#38; Fink, J. M. (2021). Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5592103\">https://doi.org/10.5281/ZENODO.5592103</a>","ieee":"M. Peruzzo <i>et al.</i>, “Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction.” Zenodo, 2021.","chicago":"Peruzzo, Matilda, Farid Hassani, Grisha Szep, Andrea Trioni, Elena Redchenko, Martin Zemlicka, and Johannes M Fink. “Geometric Superinductance Qubits: Controlling Phase Delocalization across a Single Josephson Junction.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5592103\">https://doi.org/10.5281/ZENODO.5592103</a>.","ista":"Peruzzo M, Hassani F, Szep G, Trioni A, Redchenko E, Zemlicka M, Fink JM. 2021. Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5592103\">10.5281/ZENODO.5592103</a>.","short":"M. Peruzzo, F. Hassani, G. Szep, A. Trioni, E. Redchenko, M. Zemlicka, J.M. Fink, (2021).","mla":"Peruzzo, Matilda, et al. <i>Geometric Superinductance Qubits: Controlling Phase Delocalization across a Single Josephson Junction</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5592103\">10.5281/ZENODO.5592103</a>.","ama":"Peruzzo M, Hassani F, Szep G, et al. Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5592103\">10.5281/ZENODO.5592103</a>"},"day":"22","ddc":["530"],"oa":1,"status":"public","year":"2021","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5592104"}],"_id":"13057","type":"research_data_reference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"This dataset comprises all data shown in the figures of the submitted article \"Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction\". Additional raw data are available from the corresponding author on reasonable request.","lang":"eng"}],"date_updated":"2023-08-11T10:44:21Z","doi":"10.5281/ZENODO.5592103","related_material":{"record":[{"id":"9928","relation":"used_in_publication","status":"public"}]},"article_processing_charge":"No","author":[{"id":"3F920B30-F248-11E8-B48F-1D18A9856A87","full_name":"Peruzzo, Matilda","last_name":"Peruzzo","orcid":"0000-0002-3415-4628","first_name":"Matilda"},{"full_name":"Hassani, Farid","id":"2AED110C-F248-11E8-B48F-1D18A9856A87","first_name":"Farid","orcid":"0000-0001-6937-5773","last_name":"Hassani"},{"first_name":"Grisha","last_name":"Szep","full_name":"Szep, Grisha"},{"last_name":"Trioni","first_name":"Andrea","id":"42F71B44-F248-11E8-B48F-1D18A9856A87","full_name":"Trioni, Andrea"},{"id":"2C21D6E8-F248-11E8-B48F-1D18A9856A87","full_name":"Redchenko, Elena","last_name":"Redchenko","first_name":"Elena"},{"id":"2DCF8DE6-F248-11E8-B48F-1D18A9856A87","full_name":"Zemlicka, Martin","last_name":"Zemlicka","first_name":"Martin"},{"id":"4B591CBA-F248-11E8-B48F-1D18A9856A87","full_name":"Fink, Johannes M","orcid":"0000-0001-8112-028X","last_name":"Fink","first_name":"Johannes M"}],"date_published":"2021-10-22T00:00:00Z","oa_version":"Published Version","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_created":"2023-05-23T13:42:27Z","publisher":"Zenodo","department":[{"_id":"JoFi"}],"title":"Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction"},{"publisher":"Zenodo","department":[{"_id":"EdHa"}],"title":"Source data for the manuscript \"Theory of branching morphogenesis by local interactions and global guidance\"","oa_version":"Published Version","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_created":"2023-05-23T13:46:34Z","doi":"10.5281/ZENODO.5257160","date_updated":"2023-08-14T13:18:46Z","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"10402"}]},"article_processing_charge":"No","author":[{"full_name":"Ucar, Mehmet C","id":"50B2A802-6007-11E9-A42B-EB23E6697425","first_name":"Mehmet C","last_name":"Ucar","orcid":"0000-0003-0506-4217"}],"date_published":"2021-08-25T00:00:00Z","abstract":[{"lang":"eng","text":"The zip file includes source data used in the main text of the manuscript \"Theory of branching morphogenesis by local interactions and global guidance\", as well as a representative Jupyter notebook to reproduce the main figures. A sample script for the simulations of branching and annihilating random walks is also included (Sample_script_for_simulations_of_BARWs.ipynb) to generate exemplary branched networks under external guidance. A detailed description of the simulation setup is provided in the supplementary information of the manuscipt."}],"type":"research_data_reference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2021","_id":"13058","main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5257161","open_access":"1"}],"day":"25","ddc":["570"],"oa":1,"status":"public","month":"08","citation":{"ama":"Ucar MC. Source data for the manuscript “Theory of branching morphogenesis by local interactions and global guidance.” 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>","short":"M.C. Ucar, (2021).","mla":"Ucar, Mehmet C. <i>Source Data for the Manuscript “Theory of Branching Morphogenesis by Local Interactions and Global Guidance.”</i> Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>.","ista":"Ucar MC. 2021. Source data for the manuscript ‘Theory of branching morphogenesis by local interactions and global guidance’, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>.","ieee":"M. C. Ucar, “Source data for the manuscript ‘Theory of branching morphogenesis by local interactions and global guidance.’” Zenodo, 2021.","chicago":"Ucar, Mehmet C. “Source Data for the Manuscript ‘Theory of Branching Morphogenesis by Local Interactions and Global Guidance.’” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5257160\">https://doi.org/10.5281/ZENODO.5257160</a>.","apa":"Ucar, M. C. (2021). Source data for the manuscript “Theory of branching morphogenesis by local interactions and global guidance.” Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5257160\">https://doi.org/10.5281/ZENODO.5257160</a>"}},{"main_file_link":[{"url":"https://doi.org/10.5061/dryad.7pvmcvdtj","open_access":"1"}],"_id":"13061","year":"2021","license":"https://creativecommons.org/publicdomain/zero/1.0/","type":"research_data_reference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Casillas Perez, Barbara E., et al. <i>Early Queen Infection Shapes Developmental Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>.","ista":"Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. 2021. Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>.","short":"B.E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, S. Cremer, (2021).","ama":"Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>","apa":"Casillas Perez, B. E., Pull, C., Naiser, F., Naderlinger, E., Matas, J., &#38; Cremer, S. (2021). Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">https://doi.org/10.5061/DRYAD.7PVMCVDTJ</a>","chicago":"Casillas Perez, Barbara E, Christopher Pull, Filip Naiser, Elisabeth Naderlinger, Jiri Matas, and Sylvia Cremer. “Early Queen Infection Shapes Developmental Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">https://doi.org/10.5061/DRYAD.7PVMCVDTJ</a>.","ieee":"B. E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, and S. Cremer, “Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies.” Dryad, 2021."},"project":[{"call_identifier":"H2020","_id":"2649B4DE-B435-11E9-9278-68D0E5697425","name":"Epidemics in ant societies on a chip","grant_number":"771402"}],"month":"10","status":"public","oa":1,"day":"29","ddc":["570"],"oa_version":"Published Version","tmp":{"short":"CC0 (1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"date_created":"2023-05-23T16:14:35Z","ec_funded":1,"title":"Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies","department":[{"_id":"SyCr"}],"publisher":"Dryad","abstract":[{"lang":"eng","text":"Infections early in life can have enduring effects on an organism’s development and immunity. In this study, we show that this equally applies to developing “superorganisms” – incipient social insect colonies. When we exposed newly mated Lasius niger ant queens to a low pathogen dose, their colonies grew more slowly than controls before winter, but reached similar sizes afterwards. Independent of exposure, queen hibernation survival improved when the ratio of pupae to workers was small. Queens that reared fewer pupae before worker emergence exhibited lower pathogen levels, indicating that high brood rearing efforts interfere with the ability of the queen’s immune system to suppress pathogen proliferation. Early-life queen pathogen-exposure also improved the immunocompetence of her worker offspring, as demonstrated by challenging the workers to the same pathogen a year later. Transgenerational transfer of the queen’s pathogen experience to her workforce can hence durably reduce the disease susceptibility of the whole superorganism."}],"date_published":"2021-10-29T00:00:00Z","author":[{"last_name":"Casillas Perez","first_name":"Barbara E","id":"351ED2AA-F248-11E8-B48F-1D18A9856A87","full_name":"Casillas Perez, Barbara E"},{"id":"3C7F4840-F248-11E8-B48F-1D18A9856A87","full_name":"Pull, Christopher","last_name":"Pull","orcid":"0000-0003-1122-3982","first_name":"Christopher"},{"last_name":"Naiser","first_name":"Filip","full_name":"Naiser, Filip"},{"full_name":"Naderlinger, Elisabeth","first_name":"Elisabeth","last_name":"Naderlinger"},{"first_name":"Jiri","last_name":"Matas","full_name":"Matas, Jiri"},{"orcid":"0000-0002-2193-3868","last_name":"Cremer","first_name":"Sylvia","id":"2F64EC8C-F248-11E8-B48F-1D18A9856A87","full_name":"Cremer, Sylvia"}],"date_updated":"2023-08-14T11:45:28Z","related_material":{"record":[{"id":"10284","status":"public","relation":"used_in_publication"}]},"doi":"10.5061/DRYAD.7PVMCVDTJ","article_processing_charge":"No"},{"main_file_link":[{"url":"https://doi.org/10.5061/dryad.8gtht76p1","open_access":"1"}],"_id":"13062","year":"2021","type":"research_data_reference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Szep E, Sachdeva H, Barton NH. Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>","ista":"Szep E, Sachdeva H, Barton NH. 2021. Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>.","mla":"Szep, Eniko, et al. <i>Supplementary Code for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary Model</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>.","short":"E. Szep, H. Sachdeva, N.H. Barton, (2021).","ieee":"E. Szep, H. Sachdeva, and N. H. Barton, “Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model.” Dryad, 2021.","chicago":"Szep, Eniko, Himani Sachdeva, and Nicholas H Barton. “Supplementary Code for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary Model.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">https://doi.org/10.5061/DRYAD.8GTHT76P1</a>.","apa":"Szep, E., Sachdeva, H., &#38; Barton, N. H. (2021). Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">https://doi.org/10.5061/DRYAD.8GTHT76P1</a>"},"month":"03","status":"public","oa":1,"day":"02","ddc":["570"],"oa_version":"Published Version","tmp":{"short":"CC0 (1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"date_created":"2023-05-23T16:17:02Z","department":[{"_id":"NiBa"}],"title":"Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model","publisher":"Dryad","abstract":[{"lang":"eng","text":"This paper analyzes the conditions for local adaptation in a metapopulation with infinitely many islands under a model of hard selection, where population size depends on local fitness. Each island belongs to one of two distinct ecological niches or habitats. Fitness is influenced by an additive trait which is under habitat-dependent directional selection. Our analysis is based on the diffusion approximation and  accounts for both genetic drift and demographic stochasticity. By neglecting linkage disequilibria, it yields the joint distribution of allele frequencies and population size on each island. We find that under hard selection, the conditions for local adaptation in a rare habitat are more restrictive for more polygenic traits: even moderate migration load per locus at very many loci is sufficient for population sizes to decline. This further reduces the efficacy of selection at individual loci due to increased drift and because smaller populations are more prone to swamping due to migration, causing a positive feedback between increasing maladaptation and declining population sizes. Our analysis also highlights the importance of demographic stochasticity, which  exacerbates the decline in numbers of maladapted populations, leading to population collapse in the rare habitat at significantly lower migration than predicted by deterministic arguments."}],"author":[{"full_name":"Szep, Eniko","id":"485BB5A4-F248-11E8-B48F-1D18A9856A87","first_name":"Eniko","last_name":"Szep"},{"id":"42377A0A-F248-11E8-B48F-1D18A9856A87","full_name":"Sachdeva, Himani","last_name":"Sachdeva","first_name":"Himani"},{"first_name":"Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton","full_name":"Barton, Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"}],"date_published":"2021-03-02T00:00:00Z","date_updated":"2023-09-05T15:44:05Z","doi":"10.5061/DRYAD.8GTHT76P1","related_material":{"record":[{"id":"9252","relation":"used_in_publication","status":"public"}]},"article_processing_charge":"No"},{"article_processing_charge":"No","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"8429"}],"link":[{"relation":"software","url":"https://github.com/medical-genomics-group/gmrm"}]},"doi":"10.5061/dryad.sqv9s4n51","date_updated":"2023-09-26T10:36:15Z","date_published":"2021-11-04T00:00:00Z","author":[{"first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"abstract":[{"lang":"eng","text":"We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\\leq$ 10\\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having &gt;95% probability of contributing &gt;0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data."}],"publisher":"Dryad","title":"Probabilistic inference of the genetic architecture of functional enrichment of complex traits","department":[{"_id":"MaRo"}],"date_created":"2023-05-23T16:20:16Z","tmp":{"short":"CC0 (1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"oa_version":"Published Version","ddc":["570"],"day":"04","status":"public","oa":1,"month":"11","citation":{"apa":"Robinson, M. R. (2021). Probabilistic inference of the genetic architecture of functional enrichment of complex traits. Dryad. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>","ieee":"M. R. Robinson, “Probabilistic inference of the genetic architecture of functional enrichment of complex traits.” Dryad, 2021.","chicago":"Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits.” Dryad, 2021. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>.","mla":"Robinson, Matthew Richard. <i>Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","ista":"Robinson MR. 2021. Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","short":"M.R. Robinson, (2021).","ama":"Robinson MR. Probabilistic inference of the genetic architecture of functional enrichment of complex traits. 2021. doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"research_data_reference","year":"2021","main_file_link":[{"url":"https://doi.org/10.5061/dryad.sqv9s4n51","open_access":"1"}],"_id":"13063"},{"year":"2021","_id":"13068","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.6577226"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"research_data_reference","month":"07","citation":{"ista":"Randriamanantsoa S, Papargyriou A, Maurer C, Peschke K, Schuster M, Zecchin G, Steiger K, Öllinger R, Saur D, Scheel C, Rad R, Hannezo EB, Reichert M, Bausch AR. 2021. Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>.","mla":"Randriamanantsoa, Samuel, et al. <i>Spatiotemporal Dynamics of Self-Organized Branching in Pancreas-Derived Organoids</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>.","short":"S. Randriamanantsoa, A. Papargyriou, C. Maurer, K. Peschke, M. Schuster, G. Zecchin, K. Steiger, R. Öllinger, D. Saur, C. Scheel, R. Rad, E.B. Hannezo, M. Reichert, A.R. Bausch, (2021).","ama":"Randriamanantsoa S, Papargyriou A, Maurer C, et al. Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>","apa":"Randriamanantsoa, S., Papargyriou, A., Maurer, C., Peschke, K., Schuster, M., Zecchin, G., … Bausch, A. R. (2021). Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5148117\">https://doi.org/10.5281/ZENODO.5148117</a>","ieee":"S. Randriamanantsoa <i>et al.</i>, “Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids.” Zenodo, 2021.","chicago":"Randriamanantsoa, Samuel, Aristeidis Papargyriou, Carlo Maurer, Katja Peschke, Maximilian Schuster, Giulia Zecchin, Katja Steiger, et al. “Spatiotemporal Dynamics of Self-Organized Branching in Pancreas-Derived Organoids.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5148117\">https://doi.org/10.5281/ZENODO.5148117</a>."},"ddc":["570"],"day":"30","status":"public","oa":1,"date_created":"2023-05-23T16:39:24Z","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"oa_version":"Published Version","publisher":"Zenodo","title":"Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids","department":[{"_id":"EdHa"}],"abstract":[{"text":"Source data and source code for the graphs in \"Spatiotemporal dynamics of self-organized branching pancreatic cancer-derived organoids\".","lang":"eng"}],"article_processing_charge":"No","doi":"10.5281/ZENODO.5148117","date_updated":"2023-08-04T09:25:23Z","related_material":{"record":[{"id":"12217","relation":"used_in_publication","status":"public"}]},"author":[{"full_name":"Randriamanantsoa, Samuel","last_name":"Randriamanantsoa","first_name":"Samuel"},{"full_name":"Papargyriou, Aristeidis","first_name":"Aristeidis","last_name":"Papargyriou"},{"last_name":"Maurer","first_name":"Carlo","full_name":"Maurer, Carlo"},{"last_name":"Peschke","first_name":"Katja","full_name":"Peschke, Katja"},{"last_name":"Schuster","first_name":"Maximilian","full_name":"Schuster, Maximilian"},{"full_name":"Zecchin, Giulia","last_name":"Zecchin","first_name":"Giulia"},{"last_name":"Steiger","first_name":"Katja","full_name":"Steiger, Katja"},{"last_name":"Öllinger","first_name":"Rupert","full_name":"Öllinger, Rupert"},{"last_name":"Saur","first_name":"Dieter","full_name":"Saur, Dieter"},{"last_name":"Scheel","first_name":"Christina","full_name":"Scheel, Christina"},{"first_name":"Roland","last_name":"Rad","full_name":"Rad, Roland"},{"last_name":"Hannezo","orcid":"0000-0001-6005-1561","first_name":"Edouard B","id":"3A9DB764-F248-11E8-B48F-1D18A9856A87","full_name":"Hannezo, Edouard B"},{"first_name":"Maximilian","last_name":"Reichert","full_name":"Reichert, Maximilian"},{"full_name":"Bausch, Andreas R.","last_name":"Bausch","first_name":"Andreas R."}],"date_published":"2021-07-30T00:00:00Z"},{"title":"Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans","department":[{"_id":"MaDe"}],"publisher":"Zenodo","date_created":"2023-05-23T16:40:56Z","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"oa_version":"Published Version","date_published":"2021-12-25T00:00:00Z","author":[{"full_name":"Chauve, Laetitia","last_name":"Chauve","first_name":"Laetitia"},{"full_name":"Hodge, Francesca","first_name":"Francesca","last_name":"Hodge"},{"full_name":"Murdoch, Sharlene","last_name":"Murdoch","first_name":"Sharlene"},{"full_name":"Masoudzadeh, Fatemah","last_name":"Masoudzadeh","first_name":"Fatemah"},{"first_name":"Harry-Jack","last_name":"Mann","full_name":"Mann, Harry-Jack"},{"full_name":"Lopez-Clavijo, Andrea","first_name":"Andrea","last_name":"Lopez-Clavijo"},{"full_name":"Okkenhaug, Hanneke","first_name":"Hanneke","last_name":"Okkenhaug"},{"full_name":"West, Greg","first_name":"Greg","last_name":"West"},{"full_name":"Sousa, Bebiana C.","last_name":"Sousa","first_name":"Bebiana C."},{"full_name":"Segonds-Pichon, Anne","first_name":"Anne","last_name":"Segonds-Pichon"},{"first_name":"Cheryl","last_name":"Li","full_name":"Li, Cheryl"},{"first_name":"Steven","last_name":"Wingett","full_name":"Wingett, Steven"},{"last_name":"Kienberger","first_name":"Hermine","full_name":"Kienberger, Hermine"},{"full_name":"Kleigrewe, Karin","last_name":"Kleigrewe","first_name":"Karin"},{"first_name":"Mario","orcid":"0000-0001-8347-0443","last_name":"de Bono","full_name":"de Bono, Mario","id":"4E3FF80E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Wakelam","first_name":"Michael","full_name":"Wakelam, Michael"},{"first_name":"Olivia","last_name":"Casanueva","full_name":"Casanueva, Olivia"}],"article_processing_charge":"No","related_material":{"record":[{"id":"10322","status":"public","relation":"used_in_publication"}]},"doi":"10.5281/ZENODO.5519410","date_updated":"2023-08-14T11:53:26Z","abstract":[{"lang":"eng","text":"To survive elevated temperatures, ectotherms adjust the fluidity of membranes by fine-tuning lipid desaturation levels in a process previously described to be cell-autonomous. We have discovered that, in Caenorhabditis elegans, neuronal Heat shock Factor 1 (HSF-1), the conserved master regulator of the heat shock response (HSR)- causes extensive fat remodelling in peripheral tissues. These changes include a decrease in fat desaturase and acid lipase expression in the intestine, and a global shift in the saturation levels of plasma membrane’s phospholipids. The observed remodelling of plasma membrane is in line with ectothermic adaptive responses and gives worms a cumulative advantage to warm temperatures. We have determined that at least six TAX-2/TAX-4 cGMP gated channel expressing sensory neurons and TGF-β/BMP are required for signalling across tissues to modulate fat desaturation. We also find neuronal hsf-1  is not only sufficient but also partially necessary to control the fat remodelling response and for survival at warm temperatures. This is the first study to show that a thermostat-based mechanism can cell non-autonomously coordinate membrane saturation and composition across tissues in a multicellular animal."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"research_data_reference","main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5547464","open_access":"1"}],"_id":"13069","year":"2021","status":"public","oa":1,"ddc":["570"],"day":"25","citation":{"ista":"Chauve L, Hodge F, Murdoch S, Masoudzadeh F, Mann H-J, Lopez-Clavijo A, Okkenhaug H, West G, Sousa BC, Segonds-Pichon A, Li C, Wingett S, Kienberger H, Kleigrewe K, de Bono M, Wakelam M, Casanueva O. 2021. Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>.","short":"L. Chauve, F. Hodge, S. Murdoch, F. Masoudzadeh, H.-J. Mann, A. Lopez-Clavijo, H. Okkenhaug, G. West, B.C. Sousa, A. Segonds-Pichon, C. Li, S. Wingett, H. Kienberger, K. Kleigrewe, M. de Bono, M. Wakelam, O. Casanueva, (2021).","mla":"Chauve, Laetitia, et al. <i>Neuronal HSF-1 Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>.","ama":"Chauve L, Hodge F, Murdoch S, et al. Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>","apa":"Chauve, L., Hodge, F., Murdoch, S., Masoudzadeh, F., Mann, H.-J., Lopez-Clavijo, A., … Casanueva, O. (2021). Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5519410\">https://doi.org/10.5281/ZENODO.5519410</a>","chicago":"Chauve, Laetitia, Francesca Hodge, Sharlene Murdoch, Fatemah Masoudzadeh, Harry-Jack Mann, Andrea Lopez-Clavijo, Hanneke Okkenhaug, et al. “Neuronal HSF-1 Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5519410\">https://doi.org/10.5281/ZENODO.5519410</a>.","ieee":"L. Chauve <i>et al.</i>, “Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans.” Zenodo, 2021."},"month":"12"},{"abstract":[{"text":"CpGs and corresponding mean weights for DNAm-based prediction of cognitive abilities (6 traits)","lang":"eng"}],"doi":"10.5281/ZENODO.5794028","date_updated":"2023-08-02T14:05:12Z","related_material":{"record":[{"id":"10702","relation":"used_in_publication","status":"public"}]},"article_processing_charge":"No","date_published":"2021-12-20T00:00:00Z","author":[{"last_name":"McCartney","first_name":"Daniel L","full_name":"McCartney, Daniel L"},{"full_name":"Hillary, Robert F","first_name":"Robert F","last_name":"Hillary"},{"first_name":"Eleanor LS","last_name":"Conole","full_name":"Conole, Eleanor LS"},{"full_name":"Trejo Banos, Daniel","first_name":"Daniel","last_name":"Trejo Banos"},{"full_name":"Gadd, Danni A","last_name":"Gadd","first_name":"Danni A"},{"full_name":"Walker, Rosie M","last_name":"Walker","first_name":"Rosie M"},{"full_name":"Nangle, Cliff","last_name":"Nangle","first_name":"Cliff"},{"full_name":"Flaig, Robin","last_name":"Flaig","first_name":"Robin"},{"last_name":"Campbell","first_name":"Archie","full_name":"Campbell, Archie"},{"full_name":"Murray, Alison D","first_name":"Alison D","last_name":"Murray"},{"full_name":"Munoz Maniega, Susana","last_name":"Munoz Maniega","first_name":"Susana"},{"first_name":"Maria","last_name":"del C Valdes-Hernandez","full_name":"del C Valdes-Hernandez, Maria"},{"full_name":"Harris, Mathew A","first_name":"Mathew A","last_name":"Harris"},{"full_name":"Bastin, Mark E","first_name":"Mark E","last_name":"Bastin"},{"full_name":"Wardlaw, Joanna M","first_name":"Joanna M","last_name":"Wardlaw"},{"full_name":"Harris, Sarah E","last_name":"Harris","first_name":"Sarah E"},{"first_name":"David J","last_name":"Porteous","full_name":"Porteous, David J"},{"last_name":"Tucker-Drob","first_name":"Elliot M","full_name":"Tucker-Drob, Elliot M"},{"full_name":"McIntosh, Andrew M","first_name":"Andrew M","last_name":"McIntosh"},{"full_name":"Evans, Kathryn L","last_name":"Evans","first_name":"Kathryn L"},{"full_name":"Deary, Ian J","first_name":"Ian J","last_name":"Deary"},{"full_name":"Cox, Simon R","last_name":"Cox","first_name":"Simon R"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson","first_name":"Matthew Richard"},{"first_name":"Riccardo E","last_name":"Marioni","full_name":"Marioni, Riccardo E"}],"oa_version":"Published Version","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_created":"2023-05-23T16:46:20Z","publisher":"Zenodo","department":[{"_id":"MaRo"}],"title":"Blood-based epigenome-wide analyses of cognitive abilities","month":"12","citation":{"short":"D.L. McCartney, R.F. Hillary, E.L. Conole, D. Trejo Banos, D.A. Gadd, R.M. Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S. Munoz Maniega, M. del C Valdes-Hernandez, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J. Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R. Robinson, R.E. Marioni, (2021).","mla":"McCartney, Daniel L., et al. <i>Blood-Based Epigenome-Wide Analyses of Cognitive Abilities</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","ista":"McCartney DL, Hillary RF, Conole EL, Trejo Banos D, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Munoz Maniega S, del C Valdes-Hernandez M, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2021. Blood-based epigenome-wide analyses of cognitive abilities, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","ama":"McCartney DL, Hillary RF, Conole EL, et al. Blood-based epigenome-wide analyses of cognitive abilities. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>","apa":"McCartney, D. L., Hillary, R. F., Conole, E. L., Trejo Banos, D., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2021). Blood-based epigenome-wide analyses of cognitive abilities. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>","ieee":"D. L. McCartney <i>et al.</i>, “Blood-based epigenome-wide analyses of cognitive abilities.” Zenodo, 2021.","chicago":"McCartney, Daniel L, Robert F Hillary, Eleanor LS Conole, Daniel Trejo Banos, Danni A Gadd, Rosie M Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>."},"day":"20","ddc":["570"],"status":"public","oa":1,"year":"2021","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5794029"}],"_id":"13072","type":"research_data_reference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"abstract":[{"lang":"eng","text":"Data for the manuscript 'Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire' ([2006.01275] Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire (arxiv.org))\r\n\r\nWe upload a pdf with extended data sets, and the raw data for these extended datasets as well."}],"doi":"10.5281/ZENODO.4592435","related_material":{"link":[{"url":"https://github.com/caslu85/Induced-Gap-Closing-Shared/tree/1.1.3","relation":"software"}],"record":[{"id":"9570","relation":"used_in_publication","status":"public"}]},"date_updated":"2023-08-08T14:08:07Z","article_processing_charge":"No","author":[{"id":"4D495994-AE37-11E9-AC72-31CAE5697425","full_name":"Puglia, Denise","last_name":"Puglia","first_name":"Denise"},{"full_name":"Martinez, Esteban","first_name":"Esteban","last_name":"Martinez"},{"first_name":"Gerbold","last_name":"Menard","full_name":"Menard, Gerbold"},{"first_name":"Andreas","last_name":"Pöschl","full_name":"Pöschl, Andreas"},{"full_name":"Gronin, Sergei","last_name":"Gronin","first_name":"Sergei"},{"full_name":"Gardner, Geoffrey","first_name":"Geoffrey","last_name":"Gardner"},{"full_name":"Kallaher, Ray","last_name":"Kallaher","first_name":"Ray"},{"first_name":"Michael","last_name":"Manfra","full_name":"Manfra, Michael"},{"first_name":"Charles","last_name":"Marcus","full_name":"Marcus, Charles"},{"orcid":"0000-0003-2607-2363","last_name":"Higginbotham","first_name":"Andrew P","id":"4AD6785A-F248-11E8-B48F-1D18A9856A87","full_name":"Higginbotham, Andrew P"},{"first_name":"Lucas","last_name":"Casparis","full_name":"Casparis, Lucas"}],"date_published":"2021-03-09T00:00:00Z","oa_version":"Published Version","date_created":"2023-05-23T17:11:28Z","publisher":"Zenodo","department":[{"_id":"AnHi"}],"title":"Data for 'Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire","month":"03","citation":{"chicago":"Puglia, Denise, Esteban Martinez, Gerbold Menard, Andreas Pöschl, Sergei Gronin, Geoffrey Gardner, Ray Kallaher, et al. “Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.4592435\">https://doi.org/10.5281/ZENODO.4592435</a>.","ieee":"D. Puglia <i>et al.</i>, “Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire.” Zenodo, 2021.","apa":"Puglia, D., Martinez, E., Menard, G., Pöschl, A., Gronin, S., Gardner, G., … Casparis, L. (2021). Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.4592435\">https://doi.org/10.5281/ZENODO.4592435</a>","ama":"Puglia D, Martinez E, Menard G, et al. Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.4592435\">10.5281/ZENODO.4592435</a>","short":"D. Puglia, E. Martinez, G. Menard, A. Pöschl, S. Gronin, G. Gardner, R. Kallaher, M. Manfra, C. Marcus, A.P. Higginbotham, L. Casparis, (2021).","mla":"Puglia, Denise, et al. <i>Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.4592435\">10.5281/ZENODO.4592435</a>.","ista":"Puglia D, Martinez E, Menard G, Pöschl A, Gronin S, Gardner G, Kallaher R, Manfra M, Marcus C, Higginbotham AP, Casparis L. 2021. Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.4592435\">10.5281/ZENODO.4592435</a>."},"day":"09","ddc":["530"],"status":"public","oa":1,"year":"2021","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.4592460"}],"_id":"13080","type":"research_data_reference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"publication_identifier":{"isbn":["9781713845065"],"eissn":["2640-3498"]},"citation":{"chicago":"Nguyen, Quynh, Marco Mondelli, and Guido Montufar. “Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, 139:8119–29. ML Research Press, 2021.","ieee":"Q. Nguyen, M. Mondelli, and G. Montufar, “Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 8119–8129.","apa":"Nguyen, Q., Mondelli, M., &#38; Montufar, G. (2021). Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.","ama":"Nguyen Q, Mondelli M, Montufar G. Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In: <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:8119-8129.","ista":"Nguyen Q, Mondelli M, Montufar G. 2021. Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning vol. 139, 8119–8129.","short":"Q. Nguyen, M. Mondelli, G. Montufar, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 8119–8129.","mla":"Nguyen, Quynh, et al. “Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 8119–29."},"external_id":{"arxiv":["2012.11654"]},"oa":1,"acknowledgement":"The authors would like to thank the anonymous reviewers for their helpful comments. MM was partially supported by the 2019 Lopez-Loreta Prize. QN and GM acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 757983).","status":"public","has_accepted_license":"1","year":"2021","_id":"13146","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"conference","volume":139,"page":"8119-8129","abstract":[{"lang":"eng","text":"A recent line of work has analyzed the theoretical properties of deep neural networks via the Neural Tangent Kernel (NTK). In particular, the smallest eigenvalue of the NTK has been related to the memorization capacity, the global convergence of gradient descent algorithms and the generalization of deep nets. However, existing results either provide bounds in the two-layer setting or assume that the spectrum of the NTK matrices is bounded away from 0 for multi-layer networks. In this paper, we provide tight bounds on the smallest eigenvalue of NTK matrices for deep ReLU nets, both in the limiting case of infinite widths and for finite widths. In the finite-width setting, the network architectures we consider are fairly general: we require the existence of a wide layer with roughly order of N neurons, N being the number of data samples; and the scaling of the remaining layer widths is arbitrary (up to logarithmic factors). To obtain our results, we analyze various quantities of independent interest: we give lower bounds on the smallest singular value of hidden feature matrices, and upper bounds on the Lipschitz constant of input-output feature maps."}],"arxiv":1,"date_created":"2023-06-18T22:00:48Z","oa_version":"Published Version","publisher":"ML Research Press","publication":"Proceedings of the 38th International Conference on Machine Learning","department":[{"_id":"MaMo"}],"title":"Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks","month":"07","language":[{"iso":"eng"}],"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"ddc":["000"],"day":"01","file":[{"date_created":"2023-06-19T10:49:12Z","success":1,"content_type":"application/pdf","access_level":"open_access","creator":"dernst","relation":"main_file","checksum":"19489cf5e16a0596b1f92e317d97c9b0","file_name":"2021_PMLR_Nguyen.pdf","file_size":591332,"date_updated":"2023-06-19T10:49:12Z","file_id":"13155"}],"publication_status":"published","intvolume":"       139","quality_controlled":"1","scopus_import":"1","article_processing_charge":"No","date_updated":"2024-09-10T13:03:17Z","author":[{"first_name":"Quynh","last_name":"Nguyen","full_name":"Nguyen, Quynh"},{"id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","first_name":"Marco"},{"first_name":"Guido","last_name":"Montufar","full_name":"Montufar, Guido"}],"date_published":"2021-07-01T00:00:00Z","conference":{"start_date":"2021-07-18","location":"Virtual","end_date":"2021-07-24","name":"International Conference on Machine Learning"},"tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"file_date_updated":"2023-06-19T10:49:12Z"},{"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"196-206","volume":139,"has_accepted_license":"1","year":"2021","_id":"13147","external_id":{"arxiv":["2102.07214"]},"acknowledgement":"The authors would like to thank Janne Korhonen, Aurelien Lucchi, Celestine MendlerDunner and Antonio Orvieto for helpful discussions. FA ¨and DA were supported during this work by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). PD was supported by the European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No. 754411.","status":"public","oa":1,"publication_identifier":{"eissn":["2640-3498"],"isbn":["9781713845065"]},"citation":{"ama":"Alimisis F, Davies P, Alistarh D-A. Communication-efficient distributed optimization with quantized preconditioners. In: <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:196-206.","ista":"Alimisis F, Davies P, Alistarh D-A. 2021. Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning vol. 139, 196–206.","short":"F. Alimisis, P. Davies, D.-A. Alistarh, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 196–206.","mla":"Alimisis, Foivos, et al. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 196–206.","chicago":"Alimisis, Foivos, Peter Davies, and Dan-Adrian Alistarh. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, 139:196–206. ML Research Press, 2021.","ieee":"F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 196–206.","apa":"Alimisis, F., Davies, P., &#38; Alistarh, D.-A. (2021). Communication-efficient distributed optimization with quantized preconditioners. In <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 196–206). Virtual: ML Research Press."},"publisher":"ML Research Press","title":"Communication-efficient distributed optimization with quantized preconditioners","department":[{"_id":"DaAl"}],"publication":"Proceedings of the 38th International Conference on Machine Learning","oa_version":"Published Version","ec_funded":1,"date_created":"2023-06-18T22:00:48Z","arxiv":1,"abstract":[{"lang":"eng","text":"We investigate fast and communication-efficient algorithms for the classic problem of minimizing a sum of strongly convex and smooth functions that are distributed among n\r\n different nodes, which can communicate using a limited number of bits. Most previous communication-efficient approaches for this problem are limited to first-order optimization, and therefore have \\emph{linear} dependence on the condition number in their communication complexity. We show that this dependence is not inherent: communication-efficient methods can in fact have sublinear dependence on the condition number. For this, we design and analyze the first communication-efficient distributed variants of preconditioned gradient descent for Generalized Linear Models, and for Newton’s method. Our results rely on a new technique for quantizing both the preconditioner and the descent direction at each step of the algorithms, while controlling their convergence rate. We also validate our findings experimentally, showing faster convergence and reduced communication relative to previous methods."}],"publication_status":"published","file":[{"file_id":"13154","date_updated":"2023-06-19T10:41:05Z","checksum":"7ec0d59bac268b49c76bf2e036dedd7a","file_name":"2021_PMLR_Alimisis.pdf","file_size":429087,"creator":"dernst","access_level":"open_access","relation":"main_file","date_created":"2023-06-19T10:41:05Z","success":1,"content_type":"application/pdf"}],"intvolume":"       139","day":"01","ddc":["000"],"project":[{"call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223"},{"call_identifier":"H2020","grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425"}],"language":[{"iso":"eng"}],"month":"07","file_date_updated":"2023-06-19T10:41:05Z","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"conference":{"name":"International Conference on Machine Learning","end_date":"2021-07-24","location":"Virtual","start_date":"2021-07-18"},"date_updated":"2023-06-19T10:44:38Z","article_processing_charge":"No","date_published":"2021-07-01T00:00:00Z","author":[{"last_name":"Alimisis","first_name":"Foivos","full_name":"Alimisis, Foivos"},{"full_name":"Davies, Peter","id":"11396234-BB50-11E9-B24C-90FCE5697425","first_name":"Peter","last_name":"Davies","orcid":"0000-0002-5646-9524"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","last_name":"Alistarh","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian"}],"quality_controlled":"1","scopus_import":"1"},{"language":[{"iso":"eng"}],"month":"05","day":"01","intvolume":"       109","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1109/JPROC.2021.3058954"}],"publication_status":"published","scopus_import":"1","keyword":["Electrical and Electronic Engineering"],"quality_controlled":"1","author":[{"full_name":"Scholkopf, Bernhard","last_name":"Scholkopf","first_name":"Bernhard"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco"},{"first_name":"Stefan","last_name":"Bauer","full_name":"Bauer, Stefan"},{"last_name":"Ke","first_name":"Nan Rosemary","full_name":"Ke, Nan Rosemary"},{"last_name":"Kalchbrenner","first_name":"Nal","full_name":"Kalchbrenner, Nal"},{"full_name":"Goyal, Anirudh","first_name":"Anirudh","last_name":"Goyal"},{"first_name":"Yoshua","last_name":"Bengio","full_name":"Bengio, Yoshua"}],"date_published":"2021-05-01T00:00:00Z","doi":"10.1109/jproc.2021.3058954","date_updated":"2023-09-11T11:43:35Z","article_processing_charge":"No","citation":{"ama":"Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning. <i>Proceedings of the IEEE</i>. 2021;109(5):612-634. doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>","ista":"Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5), 612–634.","mla":"Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>.","short":"B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.","ieee":"B. Scholkopf <i>et al.</i>, “Toward causal representation learning,” <i>Proceedings of the IEEE</i>, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021.","chicago":"Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>.","apa":"Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., &#38; Bengio, Y. (2021). Toward causal representation learning. <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>"},"publication_identifier":{"issn":["0018-9219"],"eissn":["1558-2256"]},"status":"public","oa":1,"article_type":"original","external_id":{"arxiv":["2102.11107"]},"issue":"5","_id":"14117","year":"2021","page":"612-634","volume":109,"type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.","lang":"eng"}],"extern":"1","arxiv":1,"oa_version":"Published Version","date_created":"2023-08-21T12:19:30Z","title":"Toward causal representation learning","department":[{"_id":"FrLo"}],"publication":"Proceedings of the IEEE","publisher":"Institute of Electrical and Electronics Engineers"},{"conference":{"location":"Virtual","start_date":"2021-07-18","name":"International Conference on Machine Learning","end_date":"2021-07-24"},"author":[{"full_name":"Yèche, Hugo","first_name":"Hugo","last_name":"Yèche"},{"last_name":"Dresdner","first_name":"Gideon","full_name":"Dresdner, Gideon"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683"},{"full_name":"Hüser, Matthias","last_name":"Hüser","first_name":"Matthias"},{"full_name":"Rätsch, Gunnar","last_name":"Rätsch","first_name":"Gunnar"}],"date_published":"2021-08-01T00:00:00Z","alternative_title":["PMLR"],"date_updated":"2023-09-11T10:16:55Z","article_processing_charge":"No","scopus_import":"1","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2106.05142"}],"intvolume":"       139","publication_status":"published","day":"01","language":[{"iso":"eng"}],"month":"08","title":"Neighborhood contrastive learning applied to online patient monitoring","department":[{"_id":"FrLo"}],"publication":"Proceedings of 38th International Conference on Machine Learning","publisher":"ML Research Press","oa_version":"Preprint","date_created":"2023-08-22T14:03:04Z","arxiv":1,"abstract":[{"lang":"eng","text":"Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by\r\nsupplementing time-series data augmentation techniques with a novel contrastive\r\nlearning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series."}],"extern":"1","page":"11964-11974","volume":139,"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"14176","year":"2021","status":"public","oa":1,"external_id":{"arxiv":["2106.05142"]},"citation":{"short":"H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974.","ista":"Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive learning applied to online patient monitoring. Proceedings of 38th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 139, 11964–11974.","mla":"Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” <i>Proceedings of 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 11964–74.","ama":"Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive learning applied to online patient monitoring. In: <i>Proceedings of 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:11964-11974.","apa":"Yèche, H., Dresdner, G., Locatello, F., Hüser, M., &#38; Rätsch, G. (2021). Neighborhood contrastive learning applied to online patient monitoring. In <i>Proceedings of 38th International Conference on Machine Learning</i> (Vol. 139, pp. 11964–11974). Virtual: ML Research Press.","chicago":"Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” In <i>Proceedings of 38th International Conference on Machine Learning</i>, 139:11964–74. ML Research Press, 2021.","ieee":"H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in <i>Proceedings of 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 11964–11974."}},{"citation":{"short":"F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412.","mla":"Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated Data.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 10401–12.","ista":"Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf B, Bauer S. 2021. On disentangled representations learned from correlated data. Proceedings of the 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 139, 10401–10412.","ama":"Träuble F, Creager E, Kilbertus N, et al. On disentangled representations learned from correlated data. In: <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:10401-10412.","apa":"Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., … Bauer, S. (2021). On disentangled representations learned from correlated data. In <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 10401–10412). Virtual: ML Research Press.","ieee":"F. Träuble <i>et al.</i>, “On disentangled representations learned from correlated data,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 10401–10412.","chicago":"Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled Representations Learned from Correlated Data.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, 139:10401–12. ML Research Press, 2021."},"oa":1,"status":"public","external_id":{"arxiv":["2006.07886"]},"_id":"14177","year":"2021","page":"10401-10412","volume":139,"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during\r\ntraining or by post-hoc correcting a pre-trained model with a small number of labels."}],"extern":"1","arxiv":1,"oa_version":"Published Version","date_created":"2023-08-22T14:03:47Z","department":[{"_id":"FrLo"}],"title":"On disentangled representations learned from correlated data","publication":"Proceedings of the 38th International Conference on Machine Learning","publisher":"ML Research Press","language":[{"iso":"eng"}],"month":"08","day":"01","intvolume":"       139","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2006.07886"}],"publication_status":"published","scopus_import":"1","quality_controlled":"1","author":[{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"full_name":"Creager, Elliot","last_name":"Creager","first_name":"Elliot"},{"last_name":"Kilbertus","first_name":"Niki","full_name":"Kilbertus, Niki"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683"},{"last_name":"Dittadi","first_name":"Andrea","full_name":"Dittadi, Andrea"},{"last_name":"Goyal","first_name":"Anirudh","full_name":"Goyal, Anirudh"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"},{"full_name":"Bauer, Stefan","first_name":"Stefan","last_name":"Bauer"}],"date_published":"2021-08-01T00:00:00Z","date_updated":"2023-09-11T10:18:48Z","alternative_title":["PMLR"],"article_processing_charge":"No","conference":{"end_date":"2021-07-24","name":"ICML: International Conference on Machine Learning","start_date":"2021-07-18","location":"Virtual"}},{"quality_controlled":"1","extern":"1","abstract":[{"text":"Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable. We introduce a new high-resolution dataset with 1M simulated images and over 1,800 annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution. We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance.","lang":"eng"}],"date_updated":"2023-09-11T10:55:30Z","article_processing_charge":"No","arxiv":1,"author":[{"last_name":"Dittadi","first_name":"Andrea","full_name":"Dittadi, Andrea"},{"last_name":"Träuble","first_name":"Frederik","full_name":"Träuble, Frederik"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Wüthrich, Manuel","first_name":"Manuel","last_name":"Wüthrich"},{"first_name":"Vaibhav","last_name":"Agrawal","full_name":"Agrawal, Vaibhav"},{"full_name":"Winther, Ole","first_name":"Ole","last_name":"Winther"},{"full_name":"Bauer, Stefan","last_name":"Bauer","first_name":"Stefan"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"}],"date_published":"2021-05-04T00:00:00Z","oa_version":"Preprint","conference":{"location":"Virtual","start_date":"2021-05-03","name":"ICLR: International Conference on Learning Representations","end_date":"2021-05-07"},"date_created":"2023-08-22T14:04:16Z","department":[{"_id":"FrLo"}],"title":"On the transfer of disentangled representations in realistic settings","publication":"The Ninth International Conference on Learning Representations","language":[{"iso":"eng"}],"month":"05","citation":{"ieee":"A. Dittadi <i>et al.</i>, “On the transfer of disentangled representations in realistic settings,” in <i>The Ninth International Conference on Learning Representations</i>, Virtual, 2021.","chicago":"Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer of Disentangled Representations in Realistic Settings.” In <i>The Ninth International Conference on Learning Representations</i>, 2021.","apa":"Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther, O., … Schölkopf, B. (2021). On the transfer of disentangled representations in realistic settings. In <i>The Ninth International Conference on Learning Representations</i>. Virtual.","ama":"Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled representations in realistic settings. In: <i>The Ninth International Conference on Learning Representations</i>. ; 2021.","ista":"Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic settings. The Ninth International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","short":"A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021.","mla":"Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in Realistic Settings.” <i>The Ninth International Conference on Learning Representations</i>, 2021."},"external_id":{"arxiv":["2010.14407"]},"day":"04","oa":1,"status":"public","publication_status":"published","year":"2021","_id":"14178","main_file_link":[{"url":"https://arxiv.org/abs/2010.14407","open_access":"1"}],"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"conference":{"start_date":"2021-12-07","location":"Virtual","end_date":"2021-12-10","name":"NeurIPS: Neural Information Processing Systems"},"quality_controlled":"1","article_processing_charge":"No","date_updated":"2023-09-11T10:33:19Z","author":[{"full_name":"Kügelgen, Julius von","first_name":"Julius von","last_name":"Kügelgen"},{"full_name":"Sharma, Yash","last_name":"Sharma","first_name":"Yash"},{"full_name":"Gresele, Luigi","last_name":"Gresele","first_name":"Luigi"},{"first_name":"Wieland","last_name":"Brendel","full_name":"Brendel, Wieland"},{"first_name":"Bernhard","last_name":"Schölkopf","full_name":"Schölkopf, Bernhard"},{"first_name":"Michel","last_name":"Besserve","full_name":"Besserve, Michel"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco"}],"date_published":"2021-06-08T00:00:00Z","publication_status":"published","intvolume":"        34","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2106.04619"}],"month":"06","language":[{"iso":"eng"}],"day":"08","date_created":"2023-08-22T14:04:36Z","oa_version":"Preprint","publication":"Advances in Neural Information Processing Systems","department":[{"_id":"FrLo"}],"title":"Self-supervised learning with data augmentations provably isolates content from style","extern":"1","abstract":[{"text":"Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice.","lang":"eng"}],"arxiv":1,"year":"2021","_id":"14179","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"conference","volume":34,"page":"16451-16467","publication_identifier":{"isbn":["9781713845393"]},"citation":{"ista":"Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M, Locatello F. 2021. Self-supervised learning with data augmentations provably isolates content from style. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 16451–16467.","mla":"Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” <i>Advances in Neural Information Processing Systems</i>, vol. 34, 2021, pp. 16451–67.","short":"J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467.","ama":"Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with data augmentations provably isolates content from style. In: <i>Advances in Neural Information Processing Systems</i>. Vol 34. ; 2021:16451-16467.","apa":"Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., &#38; Locatello, F. (2021). Self-supervised learning with data augmentations provably isolates content from style. In <i>Advances in Neural Information Processing Systems</i> (Vol. 34, pp. 16451–16467). Virtual.","ieee":"J. von Kügelgen <i>et al.</i>, “Self-supervised learning with data augmentations provably isolates content from style,” in <i>Advances in Neural Information Processing Systems</i>, Virtual, 2021, vol. 34, pp. 16451–16467.","chicago":"Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” In <i>Advances in Neural Information Processing Systems</i>, 34:16451–67, 2021."},"external_id":{"arxiv":["2106.04619"]},"status":"public","oa":1}]