[{"year":"2022","external_id":{"arxiv":["1912.12685"]},"status":"public","publication":"European Journal of Mathematics","project":[{"_id":"266A2E9E-B435-11E9-9278-68D0E5697425","name":"Alpha Shape Theory Extended","grant_number":"788183","call_identifier":"H2020"},{"name":"IST Austria Open Access Fund","_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854"}],"ec_funded":1,"acknowledgement":"AA was supported by European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 78818 Alpha). RK was supported by the Federal professorship program Grant 1.456.2016/1.4 and the Russian Foundation for Basic Research Grants 18-01-00036 and 19-01-00169. Open access funding provided by Institute of Science and Technology (IST Austria). The authors thank Alexey Balitskiy, Milena Radnović, and Serge Tabachnikov for useful discussions.","date_published":"2022-12-01T00:00:00Z","article_processing_charge":"Yes (via OA deal)","doi":"10.1007/s40879-020-00405-0","publisher":"Springer Nature","_id":"7791","date_updated":"2024-02-22T15:58:42Z","type":"journal_article","page":"1309 - 1312","ddc":["510"],"quality_controlled":"1","arxiv":1,"month":"12","department":[{"_id":"HeEd"}],"file":[{"file_id":"7796","file_size":263926,"date_created":"2020-05-04T10:33:42Z","date_updated":"2020-07-14T12:48:03Z","creator":"dernst","relation":"main_file","checksum":"f53e71fd03744075adcd0b8fc1b8423d","file_name":"2020_EuropMathematics_Akopyan.pdf","content_type":"application/pdf","access_level":"open_access"}],"oa":1,"language":[{"iso":"eng"}],"citation":{"ieee":"A. Akopyan and R. Karasev, “When different norms lead to same billiard trajectories?,” <i>European Journal of Mathematics</i>, vol. 8, no. 4. Springer Nature, pp. 1309–1312, 2022.","short":"A. Akopyan, R. Karasev, European Journal of Mathematics 8 (2022) 1309–1312.","ama":"Akopyan A, Karasev R. When different norms lead to same billiard trajectories? <i>European Journal of Mathematics</i>. 2022;8(4):1309-1312. doi:<a href=\"https://doi.org/10.1007/s40879-020-00405-0\">10.1007/s40879-020-00405-0</a>","apa":"Akopyan, A., &#38; Karasev, R. (2022). When different norms lead to same billiard trajectories? <i>European Journal of Mathematics</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s40879-020-00405-0\">https://doi.org/10.1007/s40879-020-00405-0</a>","mla":"Akopyan, Arseniy, and Roman Karasev. “When Different Norms Lead to Same Billiard Trajectories?” <i>European Journal of Mathematics</i>, vol. 8, no. 4, Springer Nature, 2022, pp. 1309–12, doi:<a href=\"https://doi.org/10.1007/s40879-020-00405-0\">10.1007/s40879-020-00405-0</a>.","ista":"Akopyan A, Karasev R. 2022. When different norms lead to same billiard trajectories? European Journal of Mathematics. 8(4), 1309–1312.","chicago":"Akopyan, Arseniy, and Roman Karasev. “When Different Norms Lead to Same Billiard Trajectories?” <i>European Journal of Mathematics</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1007/s40879-020-00405-0\">https://doi.org/10.1007/s40879-020-00405-0</a>."},"issue":"4","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","day":"01","author":[{"first_name":"Arseniy","orcid":"0000-0002-2548-617X","last_name":"Akopyan","full_name":"Akopyan, Arseniy","id":"430D2C90-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Roman","last_name":"Karasev","full_name":"Karasev, Roman"}],"oa_version":"Published Version","title":"When different norms lead to same billiard trajectories?","volume":8,"date_created":"2020-05-03T22:00:48Z","article_type":"original","has_accepted_license":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"         8","abstract":[{"lang":"eng","text":"Extending a result of Milena Radnovic and Serge Tabachnikov, we establish conditionsfor two different non-symmetric norms to define the same billiard reflection law."}],"file_date_updated":"2020-07-14T12:48:03Z","publication_status":"published","publication_identifier":{"eissn":["2199-6768"],"issn":["2199-675X"]}},{"citation":{"ama":"Podlaski WF, Agnes EJ, Vogels TP. High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. <i>bioRxiv</i>. 2022. doi:<a href=\"https://doi.org/10.1101/2020.01.08.898528\">10.1101/2020.01.08.898528</a>","ieee":"W. F. Podlaski, E. J. Agnes, and T. P. Vogels, “High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory, 2022.","short":"W.F. Podlaski, E.J. Agnes, T.P. Vogels, BioRxiv (2022).","ista":"Podlaski WF, Agnes EJ, Vogels TP. 2022. High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. bioRxiv, <a href=\"https://doi.org/10.1101/2020.01.08.898528\">10.1101/2020.01.08.898528</a>.","chicago":"Podlaski, William F., Everton J. Agnes, and Tim P Vogels. “High Capacity and Dynamic Accessibility in Associative Memory Networks with Context-Dependent Neuronal and Synaptic Gating.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, 2022. <a href=\"https://doi.org/10.1101/2020.01.08.898528\">https://doi.org/10.1101/2020.01.08.898528</a>.","apa":"Podlaski, W. F., Agnes, E. J., &#38; Vogels, T. P. (2022). High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. <i>bioRxiv</i>. Cold Spring Harbor Laboratory. <a href=\"https://doi.org/10.1101/2020.01.08.898528\">https://doi.org/10.1101/2020.01.08.898528</a>","mla":"Podlaski, William F., et al. “High Capacity and Dynamic Accessibility in Associative Memory Networks with Context-Dependent Neuronal and Synaptic Gating.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, 2022, doi:<a href=\"https://doi.org/10.1101/2020.01.08.898528\">10.1101/2020.01.08.898528</a>."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-12-21T00:00:00Z","oa":1,"language":[{"iso":"eng"}],"status":"public","publication":"bioRxiv","department":[{"_id":"TiVo"}],"year":"2022","month":"12","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2020.01.08.898528 "}],"publication_status":"published","abstract":[{"text":"Context, such as behavioral state, is known to modulate memory formation and retrieval, but is usually ignored in associative memory models. Here, we propose several types of contextual modulation for associative memory networks that greatly increase their performance. In these networks, context inactivates specific neurons and connections, which modulates the effective connectivity of the network. Memories are stored only by the active components, thereby reducing interference from memories acquired in other contexts. Such networks exhibit several beneficial characteristics, including enhanced memory capacity, high robustness to noise, increased robustness to memory overloading, and better memory retention during continual learning. Furthermore, memories can be biased to have different relative strengths, or even gated on or off, according to contextual cues, providing a candidate model for cognitive control of memory and efficient memory search. An external context-encoding network can dynamically switch the memory network to a desired state, which we liken to experimentally observed contextual signals in prefrontal cortex and hippocampus. Overall, our work illustrates the benefits of organizing memory around context, and provides an important link between behavioral studies of memory and mechanistic details of neural circuits.</jats:p><jats:sec><jats:title>SIGNIFICANCE</jats:title><jats:p>Memory is context dependent — both encoding and recall vary in effectiveness and speed depending on factors like location and brain state during a task. We apply this idea to a simple computational model of associative memory through contextual gating of neurons and synaptic connections. Intriguingly, this results in several advantages, including vastly enhanced memory capacity, better robustness, and flexible memory gating. Our model helps to explain (i) how gating and inhibition contribute to memory processes, (ii) how memory access dynamically changes over time, and (iii) how context representations, such as those observed in hippocampus and prefrontal cortex, may interact with and control memory processes.","lang":"eng"}],"_id":"8125","date_updated":"2024-03-06T12:03:59Z","locked":"1","date_created":"2020-07-16T12:24:28Z","type":"preprint","day":"21","article_processing_charge":"No","author":[{"full_name":"Podlaski, William F.","last_name":"Podlaski","orcid":"0000-0001-6619-7502","first_name":"William F."},{"first_name":"Everton J.","orcid":"0000-0001-7184-7311","last_name":"Agnes","full_name":"Agnes, Everton J."},{"first_name":"Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P"}],"doi":"10.1101/2020.01.08.898528","oa_version":"Preprint","title":"High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating","publisher":"Cold Spring Harbor Laboratory"},{"language":[{"iso":"eng"}],"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Shehu Y, Iyiola OS. 2022. Weak convergence for variational inequalities with inertial-type method. Applicable Analysis. 101(1), 192–216.","chicago":"Shehu, Yekini, and Olaniyi S. Iyiola. “Weak Convergence for Variational Inequalities with Inertial-Type Method.” <i>Applicable Analysis</i>. Taylor &#38; Francis, 2022. <a href=\"https://doi.org/10.1080/00036811.2020.1736287\">https://doi.org/10.1080/00036811.2020.1736287</a>.","mla":"Shehu, Yekini, and Olaniyi S. Iyiola. “Weak Convergence for Variational Inequalities with Inertial-Type Method.” <i>Applicable Analysis</i>, vol. 101, no. 1, Taylor &#38; Francis, 2022, pp. 192–216, doi:<a href=\"https://doi.org/10.1080/00036811.2020.1736287\">10.1080/00036811.2020.1736287</a>.","apa":"Shehu, Y., &#38; Iyiola, O. S. (2022). Weak convergence for variational inequalities with inertial-type method. <i>Applicable Analysis</i>. Taylor &#38; Francis. <a href=\"https://doi.org/10.1080/00036811.2020.1736287\">https://doi.org/10.1080/00036811.2020.1736287</a>","ama":"Shehu Y, Iyiola OS. Weak convergence for variational inequalities with inertial-type method. <i>Applicable Analysis</i>. 2022;101(1):192-216. doi:<a href=\"https://doi.org/10.1080/00036811.2020.1736287\">10.1080/00036811.2020.1736287</a>","short":"Y. Shehu, O.S. Iyiola, Applicable Analysis 101 (2022) 192–216.","ieee":"Y. Shehu and O. S. Iyiola, “Weak convergence for variational inequalities with inertial-type method,” <i>Applicable Analysis</i>, vol. 101, no. 1. Taylor &#38; Francis, pp. 192–216, 2022."},"issue":"1","arxiv":1,"month":"01","file":[{"date_updated":"2021-03-16T23:30:06Z","creator":"dernst","embargo":"2021-03-15","file_size":4282586,"date_created":"2020-10-12T10:42:54Z","file_id":"8648","content_type":"application/pdf","access_level":"open_access","file_name":"2020_ApplicAnalysis_Shehu.pdf","checksum":"869efe8cb09505dfa6012f67d20db63d","relation":"main_file"}],"department":[{"_id":"VlKo"}],"intvolume":"       101","abstract":[{"text":"Weak convergence of inertial iterative method for solving variational inequalities is the focus of this paper. The cost function is assumed to be non-Lipschitz and monotone. We propose a projection-type method with inertial terms and give weak convergence analysis under appropriate conditions. Some test results are performed and compared with relevant methods in the literature to show the efficiency and advantages given by our proposed methods.","lang":"eng"}],"has_accepted_license":"1","file_date_updated":"2021-03-16T23:30:06Z","publication_identifier":{"issn":["0003-6811"],"eissn":["1563-504X"]},"publication_status":"published","title":"Weak convergence for variational inequalities with inertial-type method","oa_version":"Submitted Version","day":"01","scopus_import":"1","author":[{"last_name":"Shehu","full_name":"Shehu, Yekini","id":"3FC7CB58-F248-11E8-B48F-1D18A9856A87","first_name":"Yekini","orcid":"0000-0001-9224-7139"},{"full_name":"Iyiola, Olaniyi S.","last_name":"Iyiola","first_name":"Olaniyi S."}],"date_created":"2020-03-09T07:06:52Z","article_type":"original","volume":101,"status":"public","publication":"Applicable Analysis","project":[{"_id":"25FBA906-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Discrete Optimization in Computer Vision: Theory and Practice","grant_number":"616160"}],"date_published":"2022-01-01T00:00:00Z","acknowledgement":"The project of the first author has received funding from the European Research Council (ERC) under the European Union's Seventh Framework Program (FP7 - 2007-2013) (Grant agreement No. 616160).","ec_funded":1,"external_id":{"arxiv":["2101.08057"],"isi":["000518364100001"]},"year":"2022","isi":1,"ddc":["510","515","518"],"page":"192-216","quality_controlled":"1","publisher":"Taylor & Francis","article_processing_charge":"No","doi":"10.1080/00036811.2020.1736287","type":"journal_article","_id":"7577","date_updated":"2024-03-05T14:01:52Z"},{"publisher":"Elsevier","article_processing_charge":"No","doi":"10.1016/j.gim.2022.07.013","type":"journal_article","_id":"14355","date_updated":"2023-09-25T08:57:07Z","ddc":["570"],"page":"2194-2203","quality_controlled":"1","year":"2022","keyword":["Human mediator complex","MED11","MEDopathies"],"publication":"Genetics in Medicine","status":"public","extern":"1","date_published":"2022-10-01T00:00:00Z","title":"A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease","oa_version":"Published Version","day":"01","scopus_import":"1","author":[{"last_name":"Cali","full_name":"Cali, Elisa","first_name":"Elisa"},{"last_name":"Lin","full_name":"Lin, Sheng-Jia","first_name":"Sheng-Jia"},{"first_name":"Clarissa","full_name":"Rocca, Clarissa","last_name":"Rocca"},{"full_name":"Sahin, Yavuz","last_name":"Sahin","first_name":"Yavuz"},{"first_name":"Aisha","last_name":"Al Shamsi","full_name":"Al Shamsi, Aisha"},{"first_name":"Salima","last_name":"El Chehadeh","full_name":"El Chehadeh, Salima"},{"first_name":"Myriam","last_name":"Chaabouni","full_name":"Chaabouni, Myriam"},{"first_name":"Kshitij","full_name":"Mankad, Kshitij","last_name":"Mankad"},{"full_name":"Galanaki, Evangelia","last_name":"Galanaki","first_name":"Evangelia"},{"first_name":"Stephanie","last_name":"Efthymiou","full_name":"Efthymiou, Stephanie"},{"full_name":"Sudhakar, Sniya","last_name":"Sudhakar","first_name":"Sniya"},{"full_name":"Athanasiou-Fragkouli, Alkyoni","last_name":"Athanasiou-Fragkouli","first_name":"Alkyoni"},{"first_name":"Tamer","last_name":"Celik","full_name":"Celik, Tamer"},{"first_name":"Nejat","full_name":"Narli, Nejat","last_name":"Narli"},{"first_name":"Sebastiano","full_name":"Bianca, Sebastiano","last_name":"Bianca"},{"first_name":"David","last_name":"Murphy","full_name":"Murphy, David"},{"last_name":"Moreira","full_name":"Moreira, Francisco Martins De Carvalho","first_name":"Francisco Martins De Carvalho"},{"first_name":"Andrea","full_name":"Accogli, Andrea","last_name":"Accogli"},{"full_name":"Petree, Cassidy","last_name":"Petree","first_name":"Cassidy"},{"orcid":"0000-0002-2512-7812","first_name":"Kevin","id":"3b3d2888-1ff6-11ee-9fa6-8f209ca91fe3","full_name":"Huang, Kevin","last_name":"Huang"},{"full_name":"Monastiri, Kamel","last_name":"Monastiri","first_name":"Kamel"},{"first_name":"Masoud","full_name":"Edizadeh, Masoud","last_name":"Edizadeh"},{"last_name":"Nardello","full_name":"Nardello, Rosaria","first_name":"Rosaria"},{"first_name":"Marzia","last_name":"Ognibene","full_name":"Ognibene, Marzia"},{"first_name":"Patrizia","last_name":"De Marco","full_name":"De Marco, Patrizia"},{"first_name":"Martino","full_name":"Ruggieri, Martino","last_name":"Ruggieri"},{"first_name":"Federico","last_name":"Zara","full_name":"Zara, Federico"},{"last_name":"Striano","full_name":"Striano, Pasquale","first_name":"Pasquale"},{"full_name":"Sahin, Yavuz","last_name":"Sahin","first_name":"Yavuz"},{"last_name":"Al-Gazali","full_name":"Al-Gazali, Lihadh","first_name":"Lihadh"},{"first_name":"Marie Therese Abi","last_name":"Warde","full_name":"Warde, Marie Therese Abi"},{"first_name":"Benedicte","full_name":"Gerard, Benedicte","last_name":"Gerard"},{"first_name":"Giovanni","full_name":"Zifarelli, Giovanni","last_name":"Zifarelli"},{"full_name":"Beetz, Christian","last_name":"Beetz","first_name":"Christian"},{"first_name":"Sara","last_name":"Fortuna","full_name":"Fortuna, Sara"},{"first_name":"Miguel","full_name":"Soler, Miguel","last_name":"Soler"},{"full_name":"Valente, Enza Maria","last_name":"Valente","first_name":"Enza Maria"},{"last_name":"Varshney","full_name":"Varshney, Gaurav","first_name":"Gaurav"},{"first_name":"Reza","full_name":"Maroofian, Reza","last_name":"Maroofian"},{"first_name":"Vincenzo","full_name":"Salpietro, Vincenzo","last_name":"Salpietro"},{"first_name":"Henry","full_name":"Houlden, Henry","last_name":"Houlden"},{"first_name":"SYNaPS Study","full_name":"Grp, SYNaPS Study","last_name":"Grp"}],"date_created":"2023-09-20T20:57:18Z","article_type":"original","volume":24,"intvolume":"        24","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"text":"Purpose: The mediator (MED) multisubunit-complex modulates the activity of the transcriptional machinery, and genetic defects in different MED subunits (17, 20, 27) have been implicated in neurologic diseases. In this study, we identified a recurrent homozygous variant in MED11 (c.325C>T; p.Arg109Ter) in 7 affected individuals from 5 unrelated families. Methods: To investigate the genetic cause of the disease, exome or genome sequencing were performed in 5 unrelated families identified via different research networks and Matchmaker Exchange. Deep clinical and brain imaging evaluations were performed by clinical pediatric neurologists and neuroradiologists. The functional effect of the candidate variant on both MED11 RNA and protein was assessed using reverse transcriptase polymerase chain reaction and western blotting using fibroblast cell lines derived from 1 affected individual and controls and through computational approaches. Knockouts in zebrafish were generated using clustered regularly interspaced short palindromic repeats/Cas9. Results: The disease was characterized by microcephaly, profound neurodevelopmental impairment, exaggerated startle response, myoclonic seizures, progressive widespread neurodegeneration, and premature death. Functional studies on patient-derived fibroblasts did not show a loss of protein function but rather disruption of the C-terminal of MED11, likely impairing binding to other MED subunits. A zebrafish knockout model recapitulates key clinical phenotypes. Conclusion: Loss of the C-terminal of MED subunit 11 may affect its binding efficiency to other MED subunits, thus implicating the MED-complex stability in brain development and neurodegeneration. (C) 2022 The Authors. Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics.","lang":"eng"}],"has_accepted_license":"1","file_date_updated":"2023-09-25T08:56:06Z","publication_identifier":{"issn":["1098-3600"]},"publication_status":"published","month":"10","file":[{"success":1,"file_name":"2022_GeneticsMedicine_Calin.pdf","access_level":"open_access","content_type":"application/pdf","relation":"main_file","checksum":"8117175a89129eb5022d81ffe7625f9f","file_size":1434037,"date_created":"2023-09-25T08:56:06Z","date_updated":"2023-09-25T08:56:06Z","creator":"dernst","file_id":"14371"}],"department":[{"_id":"GradSch"}],"language":[{"iso":"eng"}],"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Cali, Elisa, Sheng-Jia Lin, Clarissa Rocca, Yavuz Sahin, Aisha Al Shamsi, Salima El Chehadeh, Myriam Chaabouni, et al. “A Homozygous MED11 C-Terminal Variant Causes a Lethal Neurodegenerative Disease.” <i>Genetics in Medicine</i>. Elsevier, 2022. <a href=\"https://doi.org/10.1016/j.gim.2022.07.013\">https://doi.org/10.1016/j.gim.2022.07.013</a>.","ista":"Cali E, Lin S-J, Rocca C, Sahin Y, Al Shamsi A, El Chehadeh S, Chaabouni M, Mankad K, Galanaki E, Efthymiou S, Sudhakar S, Athanasiou-Fragkouli A, Celik T, Narli N, Bianca S, Murphy D, Moreira FMDC, Accogli A, Petree C, Huang K, Monastiri K, Edizadeh M, Nardello R, Ognibene M, De Marco P, Ruggieri M, Zara F, Striano P, Sahin Y, Al-Gazali L, Warde MTA, Gerard B, Zifarelli G, Beetz C, Fortuna S, Soler M, Valente EM, Varshney G, Maroofian R, Salpietro V, Houlden H, Grp SynS. 2022. A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease. Genetics in Medicine. 24(10), 2194–2203.","mla":"Cali, Elisa, et al. “A Homozygous MED11 C-Terminal Variant Causes a Lethal Neurodegenerative Disease.” <i>Genetics in Medicine</i>, vol. 24, no. 10, Elsevier, 2022, pp. 2194–203, doi:<a href=\"https://doi.org/10.1016/j.gim.2022.07.013\">10.1016/j.gim.2022.07.013</a>.","apa":"Cali, E., Lin, S.-J., Rocca, C., Sahin, Y., Al Shamsi, A., El Chehadeh, S., … Grp, Syn. S. (2022). A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease. <i>Genetics in Medicine</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.gim.2022.07.013\">https://doi.org/10.1016/j.gim.2022.07.013</a>","ama":"Cali E, Lin S-J, Rocca C, et al. A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease. <i>Genetics in Medicine</i>. 2022;24(10):2194-2203. doi:<a href=\"https://doi.org/10.1016/j.gim.2022.07.013\">10.1016/j.gim.2022.07.013</a>","short":"E. Cali, S.-J. Lin, C. Rocca, Y. Sahin, A. Al Shamsi, S. El Chehadeh, M. Chaabouni, K. Mankad, E. Galanaki, S. Efthymiou, S. Sudhakar, A. Athanasiou-Fragkouli, T. Celik, N. Narli, S. Bianca, D. Murphy, F.M.D.C. Moreira, A. Accogli, C. Petree, K. Huang, K. Monastiri, M. Edizadeh, R. Nardello, M. Ognibene, P. De Marco, M. Ruggieri, F. Zara, P. Striano, Y. Sahin, L. Al-Gazali, M.T.A. Warde, B. Gerard, G. Zifarelli, C. Beetz, S. Fortuna, M. Soler, E.M. Valente, G. Varshney, R. Maroofian, V. Salpietro, H. Houlden, Syn.S. Grp, Genetics in Medicine 24 (2022) 2194–2203.","ieee":"E. Cali <i>et al.</i>, “A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease,” <i>Genetics in Medicine</i>, vol. 24, no. 10. Elsevier, pp. 2194–2203, 2022."},"issue":"10"},{"date_created":"2023-10-01T22:01:14Z","type":"journal_article","article_type":"original","volume":438,"_id":"14381","date_updated":"2023-10-03T08:04:03Z","title":"High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others)","oa_version":"None","publisher":"Societe Mathematique de France","day":"01","scopus_import":"1","article_processing_charge":"No","doi":"10.24033/ast.1188","author":[{"full_name":"Wagner, Uli","id":"36690CA2-F248-11E8-B48F-1D18A9856A87","last_name":"Wagner","first_name":"Uli","orcid":"0000-0002-1494-0568"}],"quality_controlled":"1","publication_status":"published","publication_identifier":{"eissn":["2102-622X"],"issn":["0037-9484"]},"abstract":[{"lang":"eng","text":"Expander graphs (sparse but highly connected graphs) have, since their inception, been the source of deep links between Mathematics and Computer Science as well as applications to other areas. In recent years, a fascinating theory of high-dimensional expanders has begun to emerge, which is still in a formative stage but has nonetheless already lead to a number of striking results. Unlike for graphs, in higher dimensions there is a rich array of non-equivalent notions of expansion (coboundary expansion, cosystolic expansion, topological expansion, spectral expansion, etc.), with differents strengths and applications. In this talk, we will survey this landscape of high-dimensional expansion, with a focus on two main results. First, we will present Gromov’s Topological Overlap Theorem, which asserts that coboundary expansion (a quantitative version of vanishing mod 2 cohomology) implies topological expansion (roughly, the property that for every map from a simplicial complex to a manifold of the same dimension, the images of a positive fraction of the simplices have a point in common). Second, we will outline a construction of bounded degree 2-dimensional topological expanders, due to Kaufman, Kazhdan, and Lubotzky."}],"intvolume":"       438","page":"281-294","department":[{"_id":"UlWa"}],"month":"01","year":"2022","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-01-01T00:00:00Z","citation":{"mla":"Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and Others).” <i>Bulletin de La Societe Mathematique de France</i>, vol. 438, Societe Mathematique de France, 2022, pp. 281–94, doi:<a href=\"https://doi.org/10.24033/ast.1188\">10.24033/ast.1188</a>.","apa":"Wagner, U. (2022). High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). <i>Bulletin de La Societe Mathematique de France</i>. Societe Mathematique de France. <a href=\"https://doi.org/10.24033/ast.1188\">https://doi.org/10.24033/ast.1188</a>","ista":"Wagner U. 2022. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). Bulletin de la Societe Mathematique de France. 438, 281–294.","chicago":"Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and Others).” <i>Bulletin de La Societe Mathematique de France</i>. Societe Mathematique de France, 2022. <a href=\"https://doi.org/10.24033/ast.1188\">https://doi.org/10.24033/ast.1188</a>.","short":"U. Wagner, Bulletin de La Societe Mathematique de France 438 (2022) 281–294.","ieee":"U. Wagner, “High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others),” <i>Bulletin de la Societe Mathematique de France</i>, vol. 438. Societe Mathematique de France, pp. 281–294, 2022.","ama":"Wagner U. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). <i>Bulletin de la Societe Mathematique de France</i>. 2022;438:281-294. doi:<a href=\"https://doi.org/10.24033/ast.1188\">10.24033/ast.1188</a>"},"publication":"Bulletin de la Societe Mathematique de France","status":"public","language":[{"iso":"eng"}]},{"title":"Molecular engineering enables bright blue LEDs","oa_version":"None","author":[{"last_name":"Utzat","full_name":"Utzat, Hendrik","first_name":"Hendrik"},{"id":"43C61214-F248-11E8-B48F-1D18A9856A87","full_name":"Ibáñez, Maria","last_name":"Ibáñez","orcid":"0000-0001-5013-2843","first_name":"Maria"}],"day":"21","article_type":"letter_note","date_created":"2023-10-17T11:14:43Z","volume":612,"intvolume":"       612","abstract":[{"text":"Future LEDs could be based on lead halide perovskites. A breakthrough in preparing device-compatible solids composed of nanoscale perovskite crystals overcomes a long-standing hurdle in making blue perovskite LEDs.","lang":"eng"}],"publication_status":"published","publication_identifier":{"eissn":["1476-4687"],"issn":["0028-0836"]},"month":"12","department":[{"_id":"MaIb"}],"language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","issue":"7941","citation":{"apa":"Utzat, H., &#38; Ibáñez, M. (2022). Molecular engineering enables bright blue LEDs. <i>Nature</i>. Springer Nature. <a href=\"https://doi.org/10.1038/d41586-022-04447-0\">https://doi.org/10.1038/d41586-022-04447-0</a>","mla":"Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue LEDs.” <i>Nature</i>, vol. 612, no. 7941, Springer Nature, 2022, pp. 638–39, doi:<a href=\"https://doi.org/10.1038/d41586-022-04447-0\">10.1038/d41586-022-04447-0</a>.","ista":"Utzat H, Ibáñez M. 2022. Molecular engineering enables bright blue LEDs. Nature. 612(7941), 638–639.","chicago":"Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue LEDs.” <i>Nature</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1038/d41586-022-04447-0\">https://doi.org/10.1038/d41586-022-04447-0</a>.","ieee":"H. Utzat and M. Ibáñez, “Molecular engineering enables bright blue LEDs,” <i>Nature</i>, vol. 612, no. 7941. Springer Nature, pp. 638–639, 2022.","short":"H. Utzat, M. Ibáñez, Nature 612 (2022) 638–639.","ama":"Utzat H, Ibáñez M. Molecular engineering enables bright blue LEDs. <i>Nature</i>. 2022;612(7941):638-639. doi:<a href=\"https://doi.org/10.1038/d41586-022-04447-0\">10.1038/d41586-022-04447-0</a>"},"publisher":"Springer Nature","doi":"10.1038/d41586-022-04447-0","article_processing_charge":"No","type":"journal_article","date_updated":"2023-10-18T06:26:30Z","_id":"14437","page":"638-639","quality_controlled":"1","external_id":{"pmid":["36543947"]},"year":"2022","keyword":["Multidisciplinary"],"publication":"Nature","status":"public","date_published":"2022-12-21T00:00:00Z","pmid":1},{"related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"14517"}]},"month":"06","year":"2022","department":[{"_id":"JoFi"}],"status":"public","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-06-28T00:00:00Z","citation":{"apa":"Zemlicka, M., Redchenko, E., Peruzzo, M., Hassani, F., Trioni, A., Barzanjeh, S., &#38; Fink, J. M. (2022). Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.8408897\">https://doi.org/10.5281/ZENODO.8408897</a>","mla":"Zemlicka, Martin, et al. <i>Compact Vacuum Gap Transmon Qubits: Selective and Sensitive Probes for Superconductor Surface Losses</i>. Zenodo, 2022, doi:<a href=\"https://doi.org/10.5281/ZENODO.8408897\">10.5281/ZENODO.8408897</a>.","chicago":"Zemlicka, Martin, Elena Redchenko, Matilda Peruzzo, Farid Hassani, Andrea Trioni, Shabir Barzanjeh, and Johannes M Fink. “Compact Vacuum Gap Transmon Qubits: Selective and Sensitive Probes for Superconductor Surface Losses.” Zenodo, 2022. <a href=\"https://doi.org/10.5281/ZENODO.8408897\">https://doi.org/10.5281/ZENODO.8408897</a>.","ista":"Zemlicka M, Redchenko E, Peruzzo M, Hassani F, Trioni A, Barzanjeh S, Fink JM. 2022. Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.8408897\">10.5281/ZENODO.8408897</a>.","ieee":"M. Zemlicka <i>et al.</i>, “Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses.” Zenodo, 2022.","short":"M. Zemlicka, E. Redchenko, M. Peruzzo, F. Hassani, A. Trioni, S. Barzanjeh, J.M. Fink, (2022).","ama":"Zemlicka M, Redchenko E, Peruzzo M, et al. Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses. 2022. doi:<a href=\"https://doi.org/10.5281/ZENODO.8408897\">10.5281/ZENODO.8408897</a>"},"oa_version":"Published Version","title":"Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses","publisher":"Zenodo","day":"28","article_processing_charge":"No","doi":"10.5281/ZENODO.8408897","author":[{"id":"2DCF8DE6-F248-11E8-B48F-1D18A9856A87","full_name":"Zemlicka, Martin","last_name":"Zemlicka","first_name":"Martin"},{"last_name":"Redchenko","full_name":"Redchenko, Elena","id":"2C21D6E8-F248-11E8-B48F-1D18A9856A87","first_name":"Elena"},{"first_name":"Matilda","orcid":"0000-0002-3415-4628","last_name":"Peruzzo","full_name":"Peruzzo, Matilda","id":"3F920B30-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Hassani, Farid","id":"2AED110C-F248-11E8-B48F-1D18A9856A87","last_name":"Hassani","first_name":"Farid","orcid":"0000-0001-6937-5773"},{"first_name":"Andrea","full_name":"Trioni, Andrea","id":"42F71B44-F248-11E8-B48F-1D18A9856A87","last_name":"Trioni"},{"first_name":"Shabir","orcid":"0000-0003-0415-1423","last_name":"Barzanjeh","id":"2D25E1F6-F248-11E8-B48F-1D18A9856A87","full_name":"Barzanjeh, Shabir"},{"orcid":"0000-0001-8112-028X","first_name":"Johannes M","last_name":"Fink","id":"4B591CBA-F248-11E8-B48F-1D18A9856A87","full_name":"Fink, Johannes M"}],"date_created":"2023-11-13T08:09:10Z","type":"research_data_reference","_id":"14520","date_updated":"2024-09-10T12:23:57Z","ddc":["530"],"license":"https://creativecommons.org/publicdomain/zero/1.0/","abstract":[{"text":"This dataset comprises all data shown in the figures of the submitted article \"Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses\" at arxiv.org/abs/2206.14104. Additional raw data are available from the corresponding author on reasonable request.","lang":"eng"}],"tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","image":"/images/cc_0.png","short":"CC0 (1.0)"},"has_accepted_license":"1","main_file_link":[{"url":"https://doi.org/10.5281/ZENODO.8408897","open_access":"1"}]},{"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2203.17143"}],"publication_status":"submitted","abstract":[{"text":"Phase-field models such as the Allen-Cahn equation may give rise to the formation and evolution of geometric shapes, a phenomenon that may be analyzed rigorously in suitable scaling regimes. In its sharp-interface limit, the vectorial Allen-Cahn equation with a potential with N≥3 distinct minima has been conjectured to describe the evolution of branched interfaces by multiphase mean curvature flow.\r\nIn the present work, we give a rigorous proof for this statement in two and three ambient dimensions and for a suitable class of potentials: As long as a strong solution to multiphase mean curvature flow exists, solutions to the vectorial Allen-Cahn equation with well-prepared initial data converge towards multiphase mean curvature flow in the limit of vanishing interface width parameter ε↘0. We even establish the rate of convergence O(ε1/2).\r\nOur approach is based on the gradient flow structure of the Allen-Cahn equation and its limiting motion: Building on the recent concept of \"gradient flow calibrations\" for multiphase mean curvature flow, we introduce a notion of relative entropy for the vectorial Allen-Cahn equation with multi-well potential. This enables us to overcome the limitations of other approaches, e.g. avoiding the need for a stability analysis of the Allen-Cahn operator or additional convergence hypotheses for the energy at positive times.","lang":"eng"}],"date_updated":"2023-11-30T13:25:02Z","_id":"14597","type":"preprint","date_created":"2023-11-23T09:30:02Z","doi":"10.48550/ARXIV.2203.17143","author":[{"orcid":"0000-0002-0479-558X","first_name":"Julian L","id":"2C12A0B0-F248-11E8-B48F-1D18A9856A87","full_name":"Fischer, Julian L","last_name":"Fischer"},{"first_name":"Alice","last_name":"Marveggio","id":"25647992-AA84-11E9-9D75-8427E6697425","full_name":"Marveggio, Alice"}],"article_processing_charge":"No","day":"31","title":"Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow","oa_version":"Preprint","ec_funded":1,"citation":{"short":"J.L. Fischer, A. Marveggio, ArXiv (n.d.).","ieee":"J. L. Fischer and A. Marveggio, “Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow,” <i>arXiv</i>. .","ama":"Fischer JL, Marveggio A. Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/ARXIV.2203.17143\">10.48550/ARXIV.2203.17143</a>","mla":"Fischer, Julian L., and Alice Marveggio. “Quantitative Convergence of the Vectorial Allen-Cahn Equation towards Multiphase Mean Curvature Flow.” <i>ArXiv</i>, doi:<a href=\"https://doi.org/10.48550/ARXIV.2203.17143\">10.48550/ARXIV.2203.17143</a>.","apa":"Fischer, J. L., &#38; Marveggio, A. (n.d.). Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/ARXIV.2203.17143\">https://doi.org/10.48550/ARXIV.2203.17143</a>","chicago":"Fischer, Julian L, and Alice Marveggio. “Quantitative Convergence of the Vectorial Allen-Cahn Equation towards Multiphase Mean Curvature Flow.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/ARXIV.2203.17143\">https://doi.org/10.48550/ARXIV.2203.17143</a>.","ista":"Fischer JL, Marveggio A. Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. arXiv, <a href=\"https://doi.org/10.48550/ARXIV.2203.17143\">10.48550/ARXIV.2203.17143</a>."},"date_published":"2022-03-31T00:00:00Z","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","oa":1,"project":[{"_id":"0aa76401-070f-11eb-9043-b5bb049fa26d","call_identifier":"H2020","name":"Bridging Scales in Random Materials","grant_number":"948819"}],"status":"public","language":[{"iso":"eng"}],"publication":"arXiv","department":[{"_id":"JuFi"}],"year":"2022","external_id":{"arxiv":["2203.17143"]},"arxiv":1,"related_material":{"record":[{"id":"14587","status":"public","relation":"dissertation_contains"}]},"month":"03"},{"department":[{"_id":"KrCh"},{"_id":"ToHe"}],"month":"11","related_material":{"record":[{"id":"14539","status":"public","relation":"dissertation_contains"},{"id":"14830","relation":"later_version","status":"public"}]},"arxiv":1,"external_id":{"arxiv":["2210.05308"]},"year":"2022","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_published":"2022-11-29T00:00:00Z","citation":{"apa":"Zikelic, D., Lechner, M., Henzinger, T. A., &#38; Chatterjee, K. (n.d.). Learning control policies for stochastic systems with reach-avoid guarantees. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/ARXIV.2210.05308\">https://doi.org/10.48550/ARXIV.2210.05308</a>","mla":"Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” <i>ArXiv</i>, doi:<a href=\"https://doi.org/10.48550/ARXIV.2210.05308\">10.48550/ARXIV.2210.05308</a>.","ista":"Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. arXiv, <a href=\"https://doi.org/10.48550/ARXIV.2210.05308\">10.48550/ARXIV.2210.05308</a>.","chicago":"Zikelic, Dorde, Mathias Lechner, Thomas A Henzinger, and Krishnendu Chatterjee. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/ARXIV.2210.05308\">https://doi.org/10.48550/ARXIV.2210.05308</a>.","ieee":"D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” <i>arXiv</i>. .","short":"D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, ArXiv (n.d.).","ama":"Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/ARXIV.2210.05308\">10.48550/ARXIV.2210.05308</a>"},"ec_funded":1,"status":"public","language":[{"iso":"eng"}],"publication":"arXiv","project":[{"_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","call_identifier":"H2020","grant_number":"863818","name":"Formal Methods for Stochastic Models: Algorithms and Applications"},{"_id":"62781420-2b32-11ec-9570-8d9b63373d4d","call_identifier":"H2020","name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093"},{"call_identifier":"H2020","grant_number":"665385","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"}],"oa":1,"date_created":"2023-11-24T13:10:09Z","type":"preprint","_id":"14600","date_updated":"2025-07-14T09:10:02Z","title":"Learning control policies for stochastic systems with reach-avoid guarantees","oa_version":"Preprint","day":"29","article_processing_charge":"No","author":[{"first_name":"Dorde","orcid":"0000-0002-4681-1699","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","full_name":"Zikelic, Dorde","last_name":"Zikelic"},{"first_name":"Mathias","full_name":"Lechner, Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87","last_name":"Lechner"},{"last_name":"Henzinger","full_name":"Henzinger, Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A","orcid":"0000-0002-2985-7724"},{"first_name":"Krishnendu","orcid":"0000-0002-4561-241X","last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu"}],"doi":"10.48550/ARXIV.2210.05308","publication_status":"submitted","main_file_link":[{"url":"https://arxiv.org/abs/2210.05308","open_access":"1"}],"abstract":[{"lang":"eng","text":"We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold $p\\in[0,1]$ over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on $3$ stochastic non-linear reinforcement learning tasks."}],"license":"https://creativecommons.org/licenses/by-sa/4.0/","tmp":{"short":"CC BY-SA (4.0)","image":"/images/cc_by_sa.png","name":"Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode"}},{"type":"preprint","date_created":"2023-11-24T13:22:30Z","date_updated":"2025-07-14T09:10:00Z","_id":"14601","title":"Learning stabilizing policies in stochastic control systems","oa_version":"Preprint","doi":"10.48550/arXiv.2205.11991","author":[{"full_name":"Zikelic, Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","last_name":"Zikelic","first_name":"Dorde","orcid":"0000-0002-4681-1699"},{"first_name":"Mathias","last_name":"Lechner","full_name":"Lechner, Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","last_name":"Chatterjee","orcid":"0000-0002-4561-241X","first_name":"Krishnendu"},{"orcid":"0000-0002-2985-7724","first_name":"Thomas A","full_name":"Henzinger, Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger"}],"article_processing_charge":"No","day":"24","publication_status":"submitted","main_file_link":[{"url":"https://arxiv.org/abs/2205.11991","open_access":"1"}],"abstract":[{"text":"In this work, we address the problem of learning provably stable neural\r\nnetwork policies for stochastic control systems. While recent work has\r\ndemonstrated the feasibility of certifying given policies using martingale\r\ntheory, the problem of how to learn such policies is little explored. Here, we\r\nstudy the effectiveness of jointly learning a policy together with a martingale\r\ncertificate that proves its stability using a single learning algorithm. We\r\nobserve that the joint optimization problem becomes easily stuck in local\r\nminima when starting from a randomly initialized policy. Our results suggest\r\nthat some form of pre-training of the policy is required for the joint\r\noptimization to repair and verify the policy successfully.","lang":"eng"}],"department":[{"_id":"KrCh"},{"_id":"ToHe"}],"external_id":{"arxiv":["2205.11991"]},"month":"05","arxiv":1,"related_material":{"record":[{"id":"14539","relation":"dissertation_contains","status":"public"}]},"year":"2022","date_published":"2022-05-24T00:00:00Z","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","citation":{"ista":"Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv, <a href=\"https://doi.org/10.48550/arXiv.2205.11991\">10.48550/arXiv.2205.11991</a>.","chicago":"Zikelic, Dorde, Mathias Lechner, Krishnendu Chatterjee, and Thomas A Henzinger. “Learning Stabilizing Policies in Stochastic Control Systems.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2205.11991\">https://doi.org/10.48550/arXiv.2205.11991</a>.","mla":"Zikelic, Dorde, et al. “Learning Stabilizing Policies in Stochastic Control Systems.” <i>ArXiv</i>, doi:<a href=\"https://doi.org/10.48550/arXiv.2205.11991\">10.48550/arXiv.2205.11991</a>.","apa":"Zikelic, D., Lechner, M., Chatterjee, K., &#38; Henzinger, T. A. (n.d.). Learning stabilizing policies in stochastic control systems. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2205.11991\">https://doi.org/10.48550/arXiv.2205.11991</a>","ama":"Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2205.11991\">10.48550/arXiv.2205.11991</a>","short":"D. Zikelic, M. Lechner, K. Chatterjee, T.A. Henzinger, ArXiv (n.d.).","ieee":"D. Zikelic, M. Lechner, K. Chatterjee, and T. A. Henzinger, “Learning stabilizing policies in stochastic control systems,” <i>arXiv</i>. ."},"ec_funded":1,"project":[{"call_identifier":"H2020","grant_number":"101020093","name":"Vigilant Algorithmic Monitoring of Software","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"},{"call_identifier":"H2020","grant_number":"863818","name":"Formal Methods for Stochastic Models: Algorithms and Applications","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E"},{"name":"International IST Doctoral Program","grant_number":"665385","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"}],"publication":"arXiv","status":"public","language":[{"iso":"eng"}],"oa":1},{"ddc":["570"],"tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","image":"/images/cc_0.png","short":"CC0 (1.0)"},"abstract":[{"lang":"eng","text":"Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies."}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.gtht76hmz"}],"day":"02","article_processing_charge":"No","doi":"10.5061/DRYAD.GTHT76HMZ","author":[{"first_name":"Etienne","full_name":"Orliac, Etienne","last_name":"Orliac"},{"last_name":"Trejo Banos","full_name":"Trejo Banos, Daniel","first_name":"Daniel"},{"first_name":"Sven","last_name":"Ojavee","full_name":"Ojavee, Sven"},{"full_name":"Läll, Kristi","last_name":"Läll","first_name":"Kristi"},{"last_name":"Mägi","full_name":"Mägi, Reedik","first_name":"Reedik"},{"first_name":"Peter","full_name":"Visscher, Peter","last_name":"Visscher"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"}],"oa_version":"Published Version","publisher":"Dryad","title":"Improving genome-wide association discovery and genomic prediction accuracy in biobank data","_id":"13064","date_updated":"2023-08-03T12:40:37Z","date_created":"2023-05-23T16:28:13Z","type":"research_data_reference","oa":1,"status":"public","citation":{"mla":"Orliac, Etienne, et al. <i>Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>.","apa":"Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P., &#38; Robinson, M. R. (2022). Improving genome-wide association discovery and genomic prediction accuracy in biobank data. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>","ista":"Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR. 2022. Improving genome-wide association discovery and genomic prediction accuracy in biobank data, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>.","chicago":"Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi, Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>.","short":"E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R. Robinson, (2022).","ieee":"E. Orliac <i>et al.</i>, “Improving genome-wide association discovery and genomic prediction accuracy in biobank data.” Dryad, 2022.","ama":"Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association discovery and genomic prediction accuracy in biobank data. 2022. doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-09-02T00:00:00Z","year":"2022","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"11733"}]},"month":"09","department":[{"_id":"MaRo"}]},{"main_file_link":[{"url":"https://doi.org/10.5061/dryad.m905qfv4b","open_access":"1"}],"ddc":["570"],"tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","image":"/images/cc_0.png","short":"CC0 (1.0)"},"abstract":[{"text":"Chromosomal inversions have been shown to play a major role in local adaptation by suppressing recombination between alternative arrangements and maintaining beneficial allele combinations. However, so far, their importance relative to the remaining genome remains largely unknown. Understanding the genetic architecture of adaptation requires better estimates of how loci of different effect sizes contribute to phenotypic variation. Here, we used three Swedish islands where the marine snail Littorina saxatilis has repeatedly evolved into two distinct ecotypes along a habitat transition. We estimated the contribution of inversion polymorphisms to phenotypic divergence while controlling for polygenic effects in the remaining genome using a quantitative genetics framework. We confirmed the importance of inversions but showed that contributions of loci outside inversions are of similar magnitude, with variable proportions dependent on the trait and the population. Some inversions showed consistent effects across all sites, whereas others exhibited site-specific effects, indicating that the genomic basis for replicated phenotypic divergence is only partly shared. The contributions of sexual dimorphism as well as environmental factors to phenotypic variation were significant but minor compared to inversions and polygenic background. Overall, this integrated approach provides insight into the multiple mechanisms contributing to parallel phenotypic divergence.","lang":"eng"}],"date_created":"2023-05-23T16:33:12Z","type":"research_data_reference","_id":"13066","date_updated":"2023-08-04T09:42:10Z","title":"Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution","oa_version":"Published Version","publisher":"Dryad","article_processing_charge":"No","day":"28","author":[{"full_name":"Koch, Eva","last_name":"Koch","first_name":"Eva"},{"full_name":"Ravinet, Mark","last_name":"Ravinet","first_name":"Mark"},{"last_name":"Westram","id":"3C147470-F248-11E8-B48F-1D18A9856A87","full_name":"Westram, Anja M","first_name":"Anja M","orcid":"0000-0003-1050-4969"},{"last_name":"Jonannesson","full_name":"Jonannesson, Kerstin","first_name":"Kerstin"},{"last_name":"Butlin","full_name":"Butlin, Roger","first_name":"Roger"}],"doi":"10.5061/DRYAD.M905QFV4B","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-07-28T00:00:00Z","citation":{"ama":"Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. 2022. doi:<a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>","short":"E. Koch, M. Ravinet, A.M. Westram, K. Jonannesson, R. Butlin, (2022).","ieee":"E. Koch, M. Ravinet, A. M. Westram, K. Jonannesson, and R. Butlin, “Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution.” Dryad, 2022.","chicago":"Koch, Eva, Mark Ravinet, Anja M Westram, Kerstin Jonannesson, and Roger Butlin. “Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution.” Dryad, 2022. <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">https://doi.org/10.5061/DRYAD.M905QFV4B</a>.","ista":"Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. 2022. Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>.","mla":"Koch, Eva, et al. <i>Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>.","apa":"Koch, E., Ravinet, M., Westram, A. M., Jonannesson, K., &#38; Butlin, R. (2022). Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">https://doi.org/10.5061/DRYAD.M905QFV4B</a>"},"status":"public","oa":1,"department":[{"_id":"NiBa"}],"related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"12247"}]},"month":"07","year":"2022"},{"ddc":["510"],"abstract":[{"lang":"eng","text":"The source code for replicating experiments presented in the paper.\r\n\r\nThe implementation of the designed priority schedulers can be found in Galois-2.2.1/include/Galois/WorkList/:\r\nStealingMultiQueue.h is the StealingMultiQueue.\r\nMQOptimized/ contains MQ Optimized variants.\r\n\r\nWe provide images that contain all the dependencies and datasets. Images can be pulled from npostnikova/mq-based-schedulers repository, or downloaded from Zenodo. See readme for more detail."}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5813846"}],"oa_version":"Published Version","title":"Multi-queues can be state-of-the-art priority schedulers","publisher":"Zenodo","day":"03","article_processing_charge":"No","author":[{"first_name":"Anastasiia","last_name":"Postnikova","full_name":"Postnikova, Anastasiia"},{"id":"2F4DB10C-F248-11E8-B48F-1D18A9856A87","full_name":"Koval, Nikita","last_name":"Koval","first_name":"Nikita"},{"first_name":"Giorgi","last_name":"Nadiradze","full_name":"Nadiradze, Giorgi","id":"3279A00C-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X"}],"doi":"10.5281/ZENODO.5733408","date_created":"2023-05-23T17:05:40Z","type":"research_data_reference","_id":"13076","date_updated":"2023-08-03T06:48:34Z","status":"public","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-01-03T00:00:00Z","citation":{"apa":"Postnikova, A., Koval, N., Nadiradze, G., &#38; Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5733408\">https://doi.org/10.5281/ZENODO.5733408</a>","mla":"Postnikova, Anastasiia, et al. <i>Multi-Queues Can Be State-of-the-Art Priority Schedulers</i>. Zenodo, 2022, doi:<a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>.","chicago":"Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo, 2022. <a href=\"https://doi.org/10.5281/ZENODO.5733408\">https://doi.org/10.5281/ZENODO.5733408</a>.","ista":"Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be state-of-the-art priority schedulers, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>.","ieee":"A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.","short":"A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).","ama":"Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. 2022. doi:<a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>"},"related_material":{"link":[{"relation":"software","url":"https://github.com/npostnikova/mq-based-schedulers/tree/v1.1"}],"record":[{"id":"11180","status":"public","relation":"used_in_publication"}]},"month":"01","year":"2022","department":[{"_id":"DaAl"}]},{"year":"2022","ec_funded":1,"date_published":"2022-12-01T00:00:00Z","acknowledgement":"The authors would like to thank members of the Vogels lab and Manohar lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X and Leverhulme Grant RPG-2018-310.","status":"public","publication":"Proceedings of Machine Learning Research","project":[{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","call_identifier":"H2020"}],"_id":"13239","date_updated":"2023-07-18T06:36:28Z","type":"conference","article_processing_charge":"No","publisher":"ML Research Press","quality_controlled":"1","page":"518-531","ddc":["000"],"department":[{"_id":"TiVo"}],"file":[{"file_id":"13243","creator":"dernst","date_updated":"2023-07-18T06:32:38Z","file_size":585135,"date_created":"2023-07-18T06:32:38Z","checksum":"7530a93ef42e10b4db1e5e4b69796e93","relation":"main_file","content_type":"application/pdf","access_level":"open_access","file_name":"2022_PMLR_vanderPlas.pdf","success":1}],"month":"12","citation":{"short":"T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 518–531.","ieee":"T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning enables neural networks to learn complex working memory tasks,” in <i>Proceedings of Machine Learning Research</i>, 2022, vol. 199, pp. 518–531.","ama":"Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural networks to learn complex working memory tasks. In: <i>Proceedings of Machine Learning Research</i>. Vol 199. ML Research Press; 2022:518-531.","mla":"Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” <i>Proceedings of Machine Learning Research</i>, vol. 199, ML Research Press, 2022, pp. 518–31.","apa":"Van Der Plas, T. L., Vogels, T. P., &#38; Manohar, S. G. (2022). Predictive learning enables neural networks to learn complex working memory tasks. In <i>Proceedings of Machine Learning Research</i> (Vol. 199, pp. 518–531). ML Research Press.","ista":"Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables neural networks to learn complex working memory tasks. Proceedings of Machine Learning Research. vol. 199, 518–531.","chicago":"Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In <i>Proceedings of Machine Learning Research</i>, 199:518–31. ML Research Press, 2022."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"language":[{"iso":"eng"}],"volume":199,"date_created":"2023-07-16T22:01:12Z","scopus_import":"1","day":"01","author":[{"first_name":"Thijs L.","full_name":"Van Der Plas, Thijs L.","last_name":"Van Der Plas"},{"first_name":"Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P"},{"last_name":"Manohar","full_name":"Manohar, Sanjay G.","first_name":"Sanjay G."}],"title":"Predictive learning enables neural networks to learn complex working memory tasks","oa_version":"Published Version","file_date_updated":"2023-07-18T06:32:38Z","publication_identifier":{"eissn":["2640-3498"]},"publication_status":"published","has_accepted_license":"1","intvolume":"       199","abstract":[{"text":"Brains are thought to engage in predictive learning - learning to predict upcoming stimuli - to construct an internal model of their environment. This is especially notable for spatial navigation, as first described by Tolman’s latent learning tasks. However, predictive learning has also been observed in sensory cortex, in settings unrelated to spatial navigation. Apart from normative frameworks such as active inference or efficient coding, what could be the utility of learning to predict the patterns of occurrence of correlated stimuli? Here we show that prediction, and thereby the construction of an internal model of sequential stimuli, can bootstrap the learning process of a working memory task in a recurrent neural network. We implemented predictive learning alongside working memory match-tasks, and networks emerged to solve the prediction task first by encoding information across time to predict upcoming stimuli, and then eavesdropped on this solution to solve the matching task. Eavesdropping was most beneficial when neural resources were limited. Hence, predictive learning acts as a general neural mechanism to learn to store sensory information that can later be essential for working memory tasks.","lang":"eng"}]},{"publication":"Frontiers in Fungal Biology","status":"public","acknowledgement":"The research leading to these results received funding from the European Research Council under the European Union’s Seventh Framework Programme ERC-2013-STG (grant agreement: 335691), the Austrian Science Fund (I 3033-B22), the Austrian Academy of Sciences, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy EXC-2070-390732324 (PhenoRob) and DFG grant (DJ 64/5-1).\r\nWe would like to thank the GMI/IMBA/IMP core facilities for their excellent technical support. We would like to acknowledge Dr. Sinéad A. O’Sullivan from DZNE, University of Bonn for providing anti-GFP antibodies. The authors are thankful to the Excellence University of Bonn for providing infrastructure and instrumentation facilities at the INRES-Plant Pathology department.","date_published":"2022-10-19T00:00:00Z","year":"2022","ddc":["579"],"quality_controlled":"1","doi":"10.3389/ffunb.2022.1029114","article_processing_charge":"Yes","publisher":"Frontiers Media","date_updated":"2024-03-06T14:01:57Z","_id":"13240","type":"journal_article","oa":1,"language":[{"iso":"eng"}],"citation":{"ama":"Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. <i>Frontiers in Fungal Biology</i>. 2022;3. doi:<a href=\"https://doi.org/10.3389/ffunb.2022.1029114\">10.3389/ffunb.2022.1029114</a>","short":"K.D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, A. Djamei, Frontiers in Fungal Biology 3 (2022).","ieee":"K. D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, and A. Djamei, “Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis,” <i>Frontiers in Fungal Biology</i>, vol. 3. Frontiers Media, 2022.","chicago":"Ingole, Kishor D., Nithya Nagarajan, Simon Uhse, Caterina Giannini, and Armin Djamei. “Tetracycline-Controlled (TetON) Gene Expression System for the Smut Fungus Ustilago Maydis.” <i>Frontiers in Fungal Biology</i>. Frontiers Media, 2022. <a href=\"https://doi.org/10.3389/ffunb.2022.1029114\">https://doi.org/10.3389/ffunb.2022.1029114</a>.","ista":"Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. 2022. Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers in Fungal Biology. 3, 1029114.","mla":"Ingole, Kishor D., et al. “Tetracycline-Controlled (TetON) Gene Expression System for the Smut Fungus Ustilago Maydis.” <i>Frontiers in Fungal Biology</i>, vol. 3, 1029114, Frontiers Media, 2022, doi:<a href=\"https://doi.org/10.3389/ffunb.2022.1029114\">10.3389/ffunb.2022.1029114</a>.","apa":"Ingole, K. D., Nagarajan, N., Uhse, S., Giannini, C., &#38; Djamei, A. (2022). Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. <i>Frontiers in Fungal Biology</i>. Frontiers Media. <a href=\"https://doi.org/10.3389/ffunb.2022.1029114\">https://doi.org/10.3389/ffunb.2022.1029114</a>"},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","month":"10","department":[{"_id":"JiFr"}],"article_number":"1029114","file":[{"file_id":"13242","date_created":"2023-07-17T11:46:34Z","file_size":27966699,"creator":"dernst","date_updated":"2023-07-17T11:46:34Z","relation":"main_file","checksum":"2254e0119c0749d6f7237084fefcece6","success":1,"file_name":"2023_FrontiersFungalBio_Ingole.pdf","access_level":"open_access","content_type":"application/pdf"}],"has_accepted_license":"1","intvolume":"         3","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"text":"Ustilago maydis is a biotrophic phytopathogenic fungus that causes corn smut disease. As a well-established model system, U. maydis is genetically fully accessible with large omics datasets available and subject to various biological questions ranging from DNA-repair, RNA-transport, and protein secretion to disease biology. For many genetic approaches, tight control of transgene regulation is important. Here we established an optimised version of the Tetracycline-ON (TetON) system for U. maydis. We demonstrate the Tetracycline concentration-dependent expression of fluorescent protein transgenes and the system’s suitability for the induced expression of the toxic protein BCL2 Associated X-1 (Bax1). The Golden Gate compatible vector system contains a native minimal promoter from the mating factor a-1 encoding gene, mfa with ten copies of the tet-regulated operator (tetO) and a codon optimised Tet-repressor (tetR*) which is translationally fused to the native transcriptional corepressor Mql1 (UMAG_05501). The metabolism-independent transcriptional regulator system is functional both, in liquid culture as well as on solid media in the presence of the inducer and can become a useful tool for toxin-antitoxin studies, identification of antifungal proteins, and to study functions of toxic gene products in Ustilago maydis.","lang":"eng"}],"publication_identifier":{"eissn":["2673-6128"]},"publication_status":"published","file_date_updated":"2023-07-17T11:46:34Z","author":[{"first_name":"Kishor D.","last_name":"Ingole","full_name":"Ingole, Kishor D."},{"full_name":"Nagarajan, Nithya","last_name":"Nagarajan","first_name":"Nithya"},{"full_name":"Uhse, Simon","last_name":"Uhse","first_name":"Simon"},{"first_name":"Caterina","last_name":"Giannini","id":"e3fdddd5-f6e0-11ea-865d-ca99ee6367f4","full_name":"Giannini, Caterina"},{"first_name":"Armin","last_name":"Djamei","full_name":"Djamei, Armin"}],"scopus_import":"1","day":"19","oa_version":"Published Version","title":"Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis","volume":3,"article_type":"original","date_created":"2023-07-16T22:01:12Z"},{"date_published":"2022-12-01T00:00:00Z","acknowledgement":"This paper is a shortened, workshop version of Konstantinov and Lampert (2021),\r\nhttps://arxiv.org/abs/2102.06004. For further results, including an analysis of algorithms achieving the lower bounds from this paper, we refer to the full version.","publication":"Proceedings of Machine Learning Research","status":"public","related_material":{"record":[{"relation":"extended_version","status":"public","id":"10802"}]},"external_id":{"arxiv":["2102.06004"]},"year":"2022","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2102.06004"}],"page":"59-83","type":"conference","_id":"13241","date_updated":"2023-09-26T10:44:37Z","publisher":"ML Research Press","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83.","chicago":"Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” In <i>Proceedings of Machine Learning Research</i>, 171:59–83. ML Research Press, 2022.","mla":"Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” <i>Proceedings of Machine Learning Research</i>, vol. 171, ML Research Press, 2022, pp. 59–83.","apa":"Konstantinov, N. H., &#38; Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In <i>Proceedings of Machine Learning Research</i> (Vol. 171, pp. 59–83). ML Research Press.","ama":"Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning from corrupted data. In: <i>Proceedings of Machine Learning Research</i>. Vol 171. ML Research Press; 2022:59-83.","short":"N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83.","ieee":"N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in <i>Proceedings of Machine Learning Research</i>, 2022, vol. 171, pp. 59–83."},"language":[{"iso":"eng"}],"oa":1,"department":[{"_id":"ChLa"}],"arxiv":1,"month":"12","publication_identifier":{"eissn":["2640-3498"]},"publication_status":"published","intvolume":"       171","abstract":[{"lang":"eng","text":"Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. Many approaches for training fair models from data have been developed and an implicit assumption about such algorithms is that they are able to recover a fair model, despite potential historical biases in the data. In this work we show a number of impossibility results that indicate that there is no learning algorithm that can recover a fair model when a proportion of the dataset is subject to arbitrary manipulations. Specifically, we prove that there are situations in which an adversary can force any learner to return a biased classifier, with or without degrading accuracy, and that the strength of this bias increases for learning problems with underrepresented protected groups in the data. Our results emphasize on the importance of studying further data corruption models of various strength and of establishing stricter data collection practices for fairness-aware learning."}],"date_created":"2023-07-16T22:01:13Z","volume":171,"oa_version":"Preprint","title":"On the impossibility of fairness-aware learning from corrupted data","scopus_import":"1","day":"01","author":[{"first_name":"Nikola H","full_name":"Konstantinov, Nikola H","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","last_name":"Konstantinov"},{"orcid":"0000-0001-8622-7887","first_name":"Christoph","last_name":"Lampert","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}]},{"intvolume":"       151","abstract":[{"lang":"eng","text":" We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully increasing the batch size over the course of the algorithm’s execution, which leads to computing full gradients. In contrast, the proposed method, equipped with a stochastic average gradient (SAG) estimator, requires only one sample per iteration. Nevertheless, it guarantees fast convergence rates on par with more sophisticated variance reduction techniques. In applications we put special emphasis on problems with a large number of separable constraints. Such problems are prevalent among semidefinite programming (SDP) formulations arising in machine learning and theoretical computer science. We provide numerical experiments on matrix completion, unsupervised clustering, and sparsest-cut SDPs. "}],"publication_status":"published","publication_identifier":{"issn":["2640-3498"]},"scopus_import":"1","day":"01","author":[{"first_name":"Gideon","full_name":"Dresdner, Gideon","last_name":"Dresdner"},{"first_name":"Maria-Luiza","last_name":"Vladarean","full_name":"Vladarean, Maria-Luiza"},{"last_name":"Rätsch","full_name":"Rätsch, Gunnar","first_name":"Gunnar"},{"first_name":"Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"last_name":"Cevher","full_name":"Cevher, Volkan","first_name":"Volkan"},{"full_name":"Yurtsever, Alp","last_name":"Yurtsever","first_name":"Alp"}],"title":" Faster one-sample stochastic conditional gradient method for composite convex minimization","oa_version":"Preprint","volume":151,"date_created":"2023-08-21T09:27:43Z","oa":1,"language":[{"iso":"eng"}],"citation":{"chicago":"Dresdner, Gideon, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, and Alp Yurtsever. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” In <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i>, 151:8439–57. ML Research Press, 2022.","ista":"Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. 2022.  Faster one-sample stochastic conditional gradient method for composite convex minimization. Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 151, 8439–8457.","apa":"Dresdner, G., Vladarean, M.-L., Rätsch, G., Locatello, F., Cevher, V., &#38; Yurtsever, A. (2022).  Faster one-sample stochastic conditional gradient method for composite convex minimization. In <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i> (Vol. 151, pp. 8439–8457). Virtual: ML Research Press.","mla":"Dresdner, Gideon, et al. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i>, vol. 151, ML Research Press, 2022, pp. 8439–57.","ama":"Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A.  Faster one-sample stochastic conditional gradient method for composite convex minimization. In: <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i>. Vol 151. ML Research Press; 2022:8439-8457.","ieee":"G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, and A. Yurtsever, “ Faster one-sample stochastic conditional gradient method for composite convex minimization,” in <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i>, Virtual, 2022, vol. 151, pp. 8439–8457.","short":"G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, A. Yurtsever, in:, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2022, pp. 8439–8457."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"04","arxiv":1,"department":[{"_id":"FrLo"}],"page":"8439-8457","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2202.13212"}],"quality_controlled":"1","article_processing_charge":"No","alternative_title":["PMLR"],"publisher":"ML Research Press","_id":"14093","date_updated":"2023-09-06T10:28:17Z","type":"conference","publication":"Proceedings of the 25th International Conference on Artificial Intelligence and Statistics","extern":"1","status":"public","conference":{"name":"AISTATS: Conference on Artificial Intelligence and Statistics","start_date":"2022-03-28","end_date":"2022-03-30","location":"Virtual"},"date_published":"2022-04-01T00:00:00Z","year":"2022","external_id":{"arxiv":["2202.13212"]}},{"year":"2022","external_id":{"arxiv":["2204.04440"]},"date_published":"2022-12-15T00:00:00Z","conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2022-11-28","end_date":"2022-12-09","location":"New Orleans, LA, United States"},"publication":"36th Conference on Neural Information Processing Systems","status":"public","extern":"1","date_updated":"2023-09-06T10:29:42Z","_id":"14106","type":"conference","alternative_title":["Advances in Neural Information Processing Systems"],"article_processing_charge":"No","publisher":"Neural Information Processing Systems Foundation","main_file_link":[{"url":"https://arxiv.org/abs/2204.04440","open_access":"1"}],"quality_controlled":"1","page":"16548-16562","department":[{"_id":"FrLo"}],"arxiv":1,"month":"12","citation":{"ista":"Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. 2022. Are two heads the same as one? Identifying disparate treatment in fair neural networks. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35, 16548–16562.","chicago":"Lohaus, Michael, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, and Chris Russell. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” In <i>36th Conference on Neural Information Processing Systems</i>, 35:16548–62. Neural Information Processing Systems Foundation, 2022.","apa":"Lohaus, M., Kleindessner, M., Kenthapadi, K., Locatello, F., &#38; Russell, C. (2022). Are two heads the same as one? Identifying disparate treatment in fair neural networks. In <i>36th Conference on Neural Information Processing Systems</i> (Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information Processing Systems Foundation.","mla":"Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” <i>36th Conference on Neural Information Processing Systems</i>, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 16548–62.","ama":"Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads the same as one? Identifying disparate treatment in fair neural networks. In: <i>36th Conference on Neural Information Processing Systems</i>. Vol 35. Neural Information Processing Systems Foundation; 2022:16548-16562.","ieee":"M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are two heads the same as one? Identifying disparate treatment in fair neural networks,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2022, vol. 35, pp. 16548–16562.","short":"M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 16548–16562."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"language":[{"iso":"eng"}],"volume":35,"date_created":"2023-08-21T12:12:42Z","author":[{"first_name":"Michael","full_name":"Lohaus, Michael","last_name":"Lohaus"},{"first_name":"Matthäus","full_name":"Kleindessner, Matthäus","last_name":"Kleindessner"},{"last_name":"Kenthapadi","full_name":"Kenthapadi, Krishnaram","first_name":"Krishnaram"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco"},{"first_name":"Chris","last_name":"Russell","full_name":"Russell, Chris"}],"scopus_import":"1","day":"15","oa_version":"Preprint","title":"Are two heads the same as one? Identifying disparate treatment in fair neural networks","publication_status":"published","publication_identifier":{"isbn":["9781713871088"]},"intvolume":"        35","abstract":[{"text":"We show that deep networks trained to satisfy demographic parity often do so\r\nthrough a form of race or gender awareness, and that the more we force a network\r\nto be fair, the more accurately we can recover race or gender from the internal state\r\nof the network. Based on this observation, we investigate an alternative fairness\r\napproach: we add a second classification head to the network to explicitly predict\r\nthe protected attribute (such as race or gender) alongside the original task. After\r\ntraining the two-headed network, we enforce demographic parity by merging the\r\ntwo heads, creating a network with the same architecture as the original network.\r\nWe establish a close relationship between existing approaches and our approach\r\nby showing (1) that the decisions of a fair classifier are well-approximated by our\r\napproach, and (2) that an unfair and optimally accurate classifier can be recovered\r\nfrom a fair classifier and our second head predicting the protected attribute. We use\r\nour explicit formulation to argue that the existing fairness approaches, just as ours,\r\ndemonstrate disparate treatment and that they are likely to be unlawful in a wide\r\nrange of scenarios under US law.","lang":"eng"}]},{"citation":{"apa":"Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022). Self-supervised amodal video object segmentation. In <i>36th Conference on Neural Information Processing Systems</i>. New Orleans, LA, United States. <a href=\"https://doi.org/10.48550/arXiv.2210.12733\">https://doi.org/10.48550/arXiv.2210.12733</a>","mla":"Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.” <i>36th Conference on Neural Information Processing Systems</i>, 2022, doi:<a href=\"https://doi.org/10.48550/arXiv.2210.12733\">10.48550/arXiv.2210.12733</a>.","ista":"Yao J, Hong Y, Wang C, Xiao T, He T, Locatello F, Wipf D, Fu Y, Zhang Z. 2022. Self-supervised amodal video object segmentation. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.","chicago":"Yao, Jian, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, and Zheng Zhang. “Self-Supervised Amodal Video Object Segmentation.” In <i>36th Conference on Neural Information Processing Systems</i>, 2022. <a href=\"https://doi.org/10.48550/arXiv.2210.12733\">https://doi.org/10.48550/arXiv.2210.12733</a>.","ieee":"J. Yao <i>et al.</i>, “Self-supervised amodal video object segmentation,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2022.","short":"J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z. Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022.","ama":"Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation. In: <i>36th Conference on Neural Information Processing Systems</i>. ; 2022. doi:<a href=\"https://doi.org/10.48550/arXiv.2210.12733\">10.48550/arXiv.2210.12733</a>"},"conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2022-12-01","start_date":"2022-11-28","location":"New Orleans, LA, United States"},"date_published":"2022-10-23T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"publication":"36th Conference on Neural Information Processing Systems","language":[{"iso":"eng"}],"status":"public","extern":"1","department":[{"_id":"FrLo"}],"year":"2022","external_id":{"arxiv":["2210.12733"]},"month":"10","arxiv":1,"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2210.12733","open_access":"1"}],"publication_status":"published","abstract":[{"text":"Amodal perception requires inferring the full shape of an object that is partially occluded. This task is particularly challenging on two levels: (1) it requires more information than what is contained in the instant retina or imaging sensor, (2) it is difficult to obtain enough well-annotated amodal labels for supervision. To this end, this paper develops a new framework of\r\nSelf-supervised amodal Video object segmentation (SaVos). Our method efficiently leverages the visual information of video temporal sequences to infer the amodal mask of objects. The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.\r\nAccordingly, we derive a novel self-supervised learning paradigm that efficiently utilizes the visible object parts as the supervision to guide the training on videos. In addition to learning type prior to complete masks for known types, SaVos also learns the spatiotemporal prior, which is also useful for the amodal task and could generalize to unseen types. The proposed\r\nframework achieves the state-of-the-art performance on the synthetic amodal segmentation benchmark FISHBOWL and the real world benchmark KINS-Video-Car. Further, it lends itself well to being transferred to novel distributions using test-time adaptation, outperforming existing models even after the transfer to a new distribution.","lang":"eng"}],"date_updated":"2023-09-11T09:34:17Z","_id":"14107","type":"conference","date_created":"2023-08-21T12:13:25Z","author":[{"first_name":"Jian","full_name":"Yao, Jian","last_name":"Yao"},{"first_name":"Yuxin","full_name":"Hong, Yuxin","last_name":"Hong"},{"first_name":"Chiyu","full_name":"Wang, Chiyu","last_name":"Wang"},{"full_name":"Xiao, Tianjun","last_name":"Xiao","first_name":"Tianjun"},{"first_name":"Tong","last_name":"He","full_name":"He, Tong"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco"},{"full_name":"Wipf, David","last_name":"Wipf","first_name":"David"},{"first_name":"Yanwei","full_name":"Fu, Yanwei","last_name":"Fu"},{"full_name":"Zhang, Zheng","last_name":"Zhang","first_name":"Zheng"}],"doi":"10.48550/arXiv.2210.12733","article_processing_charge":"No","day":"23","title":"Self-supervised amodal video object segmentation","oa_version":"Preprint"},{"date_created":"2023-08-21T12:18:00Z","oa_version":"Preprint","title":"Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers","author":[{"first_name":"Dominik","last_name":"Zietlow","full_name":"Zietlow, Dominik"},{"first_name":"Michael","full_name":"Lohaus, Michael","last_name":"Lohaus"},{"last_name":"Balakrishnan","full_name":"Balakrishnan, Guha","first_name":"Guha"},{"last_name":"Kleindessner","full_name":"Kleindessner, Matthaus","first_name":"Matthaus"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco","orcid":"0000-0002-4850-0683"},{"first_name":"Bernhard","last_name":"Scholkopf","full_name":"Scholkopf, Bernhard"},{"first_name":"Chris","last_name":"Russell","full_name":"Russell, Chris"}],"day":"01","scopus_import":"1","publication_status":"published","publication_identifier":{"isbn":["9781665469470"],"issn":["1063-6919"],"eissn":["2575-7075"]},"abstract":[{"text":"Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups.","lang":"eng"}],"department":[{"_id":"FrLo"}],"arxiv":1,"month":"07","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In: <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>. Institute of Electrical and Electronics Engineers; 2022:10400-10411. doi:<a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">10.1109/cvpr52688.2022.01016</a>","short":"D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B. Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.","ieee":"D. Zietlow <i>et al.</i>, “Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers,” in <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, New Orleans, LA, United States, 2022, pp. 10400–10411.","chicago":"Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner, Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 10400–411. Institute of Electrical and Electronics Engineers, 2022. <a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">https://doi.org/10.1109/cvpr52688.2022.01016</a>.","ista":"Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.","mla":"Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–11, doi:<a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">10.1109/cvpr52688.2022.01016</a>.","apa":"Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F., Scholkopf, B., &#38; Russell, C. (2022). Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i> (pp. 10400–10411). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">https://doi.org/10.1109/cvpr52688.2022.01016</a>"},"language":[{"iso":"eng"}],"oa":1,"type":"conference","date_updated":"2023-09-11T09:19:14Z","_id":"14114","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/cvpr52688.2022.01016","article_processing_charge":"No","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2203.04913"}],"page":"10400-10411","external_id":{"arxiv":["2203.04913"]},"year":"2022","date_published":"2022-07-01T00:00:00Z","conference":{"start_date":"2022-06-18","end_date":"2022-06-24","name":"CVPR: Conference on Computer Vision and Pattern Recognition","location":"New Orleans, LA, United States"},"status":"public","extern":"1","publication":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition"}]