--- _id: '7791' 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. 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. article_processing_charge: Yes (via OA deal) article_type: original arxiv: 1 author: - first_name: Arseniy full_name: Akopyan, Arseniy id: 430D2C90-F248-11E8-B48F-1D18A9856A87 last_name: Akopyan orcid: 0000-0002-2548-617X - first_name: Roman full_name: Karasev, Roman last_name: Karasev citation: ama: Akopyan A, Karasev R. When different norms lead to same billiard trajectories? European Journal of Mathematics. 2022;8(4):1309-1312. doi:10.1007/s40879-020-00405-0 apa: Akopyan, A., & Karasev, R. (2022). When different norms lead to same billiard trajectories? European Journal of Mathematics. Springer Nature. https://doi.org/10.1007/s40879-020-00405-0 chicago: Akopyan, Arseniy, and Roman Karasev. “When Different Norms Lead to Same Billiard Trajectories?” European Journal of Mathematics. Springer Nature, 2022. https://doi.org/10.1007/s40879-020-00405-0. ieee: A. Akopyan and R. Karasev, “When different norms lead to same billiard trajectories?,” European Journal of Mathematics, vol. 8, no. 4. Springer Nature, pp. 1309–1312, 2022. ista: Akopyan A, Karasev R. 2022. When different norms lead to same billiard trajectories? European Journal of Mathematics. 8(4), 1309–1312. mla: Akopyan, Arseniy, and Roman Karasev. “When Different Norms Lead to Same Billiard Trajectories?” European Journal of Mathematics, vol. 8, no. 4, Springer Nature, 2022, pp. 1309–12, doi:10.1007/s40879-020-00405-0. short: A. Akopyan, R. Karasev, European Journal of Mathematics 8 (2022) 1309–1312. date_created: 2020-05-03T22:00:48Z date_published: 2022-12-01T00:00:00Z date_updated: 2024-02-22T15:58:42Z day: '01' ddc: - '510' department: - _id: HeEd doi: 10.1007/s40879-020-00405-0 ec_funded: 1 external_id: arxiv: - '1912.12685' file: - access_level: open_access checksum: f53e71fd03744075adcd0b8fc1b8423d content_type: application/pdf creator: dernst date_created: 2020-05-04T10:33:42Z date_updated: 2020-07-14T12:48:03Z file_id: '7796' file_name: 2020_EuropMathematics_Akopyan.pdf file_size: 263926 relation: main_file file_date_updated: 2020-07-14T12:48:03Z has_accepted_license: '1' intvolume: ' 8' issue: '4' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '12' oa: 1 oa_version: Published Version page: 1309 - 1312 project: - _id: 266A2E9E-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '788183' name: Alpha Shape Theory Extended - _id: B67AFEDC-15C9-11EA-A837-991A96BB2854 name: IST Austria Open Access Fund publication: European Journal of Mathematics publication_identifier: eissn: - 2199-6768 issn: - 2199-675X publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: When different norms lead to same billiard trajectories? tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 8 year: '2022' ... --- _id: '8125' abstract: - lang: eng 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.SIGNIFICANCEMemory 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. article_processing_charge: No author: - first_name: William F. full_name: Podlaski, William F. last_name: Podlaski orcid: 0000-0001-6619-7502 - first_name: Everton J. full_name: Agnes, Everton J. last_name: Agnes orcid: 0000-0001-7184-7311 - first_name: Tim P full_name: Vogels, Tim P id: CB6FF8D2-008F-11EA-8E08-2637E6697425 last_name: Vogels orcid: 0000-0003-3295-6181 citation: ama: Podlaski WF, Agnes EJ, Vogels TP. High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. bioRxiv. 2022. doi:10.1101/2020.01.08.898528 apa: Podlaski, W. F., Agnes, E. J., & Vogels, T. P. (2022). High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.01.08.898528 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.” BioRxiv. Cold Spring Harbor Laboratory, 2022. https://doi.org/10.1101/2020.01.08.898528. 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,” bioRxiv. Cold Spring Harbor Laboratory, 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, 10.1101/2020.01.08.898528. mla: Podlaski, William F., et al. “High Capacity and Dynamic Accessibility in Associative Memory Networks with Context-Dependent Neuronal and Synaptic Gating.” BioRxiv, Cold Spring Harbor Laboratory, 2022, doi:10.1101/2020.01.08.898528. short: W.F. Podlaski, E.J. Agnes, T.P. Vogels, BioRxiv (2022). date_created: 2020-07-16T12:24:28Z date_published: 2022-12-21T00:00:00Z date_updated: 2024-03-06T12:03:59Z day: '21' department: - _id: TiVo doi: 10.1101/2020.01.08.898528 language: - iso: eng locked: '1' main_file_link: - open_access: '1' url: 'https://doi.org/10.1101/2020.01.08.898528 ' month: '12' oa: 1 oa_version: Preprint publication: bioRxiv publication_status: published publisher: Cold Spring Harbor Laboratory status: public title: High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '7577' abstract: - lang: eng 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. 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). article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Olaniyi S. full_name: Iyiola, Olaniyi S. last_name: Iyiola citation: ama: Shehu Y, Iyiola OS. Weak convergence for variational inequalities with inertial-type method. Applicable Analysis. 2022;101(1):192-216. doi:10.1080/00036811.2020.1736287 apa: Shehu, Y., & Iyiola, O. S. (2022). Weak convergence for variational inequalities with inertial-type method. Applicable Analysis. Taylor & Francis. https://doi.org/10.1080/00036811.2020.1736287 chicago: Shehu, Yekini, and Olaniyi S. Iyiola. “Weak Convergence for Variational Inequalities with Inertial-Type Method.” Applicable Analysis. Taylor & Francis, 2022. https://doi.org/10.1080/00036811.2020.1736287. ieee: Y. Shehu and O. S. Iyiola, “Weak convergence for variational inequalities with inertial-type method,” Applicable Analysis, vol. 101, no. 1. Taylor & Francis, pp. 192–216, 2022. ista: Shehu Y, Iyiola OS. 2022. Weak convergence for variational inequalities with inertial-type method. Applicable Analysis. 101(1), 192–216. mla: Shehu, Yekini, and Olaniyi S. Iyiola. “Weak Convergence for Variational Inequalities with Inertial-Type Method.” Applicable Analysis, vol. 101, no. 1, Taylor & Francis, 2022, pp. 192–216, doi:10.1080/00036811.2020.1736287. short: Y. Shehu, O.S. Iyiola, Applicable Analysis 101 (2022) 192–216. date_created: 2020-03-09T07:06:52Z date_published: 2022-01-01T00:00:00Z date_updated: 2024-03-05T14:01:52Z day: '01' ddc: - '510' - '515' - '518' department: - _id: VlKo doi: 10.1080/00036811.2020.1736287 ec_funded: 1 external_id: arxiv: - '2101.08057' isi: - '000518364100001' file: - access_level: open_access checksum: 869efe8cb09505dfa6012f67d20db63d content_type: application/pdf creator: dernst date_created: 2020-10-12T10:42:54Z date_updated: 2021-03-16T23:30:06Z embargo: 2021-03-15 file_id: '8648' file_name: 2020_ApplicAnalysis_Shehu.pdf file_size: 4282586 relation: main_file file_date_updated: 2021-03-16T23:30:06Z has_accepted_license: '1' intvolume: ' 101' isi: 1 issue: '1' language: - iso: eng month: '01' oa: 1 oa_version: Submitted Version page: 192-216 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Applicable Analysis publication_identifier: eissn: - 1563-504X issn: - 0003-6811 publication_status: published publisher: Taylor & Francis quality_controlled: '1' scopus_import: '1' status: public title: Weak convergence for variational inequalities with inertial-type method type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 101 year: '2022' ... --- _id: '14355' abstract: - lang: eng 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.' article_processing_charge: No article_type: original author: - first_name: Elisa full_name: Cali, Elisa last_name: Cali - first_name: Sheng-Jia full_name: Lin, Sheng-Jia last_name: Lin - first_name: Clarissa full_name: Rocca, Clarissa last_name: Rocca - first_name: Yavuz full_name: Sahin, Yavuz last_name: Sahin - first_name: Aisha full_name: Al Shamsi, Aisha last_name: Al Shamsi - first_name: Salima full_name: El Chehadeh, Salima last_name: El Chehadeh - first_name: Myriam full_name: Chaabouni, Myriam last_name: Chaabouni - first_name: Kshitij full_name: Mankad, Kshitij last_name: Mankad - first_name: Evangelia full_name: Galanaki, Evangelia last_name: Galanaki - first_name: Stephanie full_name: Efthymiou, Stephanie last_name: Efthymiou - first_name: Sniya full_name: Sudhakar, Sniya last_name: Sudhakar - first_name: Alkyoni full_name: Athanasiou-Fragkouli, Alkyoni last_name: Athanasiou-Fragkouli - first_name: Tamer full_name: Celik, Tamer last_name: Celik - first_name: Nejat full_name: Narli, Nejat last_name: Narli - first_name: Sebastiano full_name: Bianca, Sebastiano last_name: Bianca - first_name: David full_name: Murphy, David last_name: Murphy - first_name: Francisco Martins De Carvalho full_name: Moreira, Francisco Martins De Carvalho last_name: Moreira - first_name: Andrea full_name: Accogli, Andrea last_name: Accogli - first_name: Cassidy full_name: Petree, Cassidy last_name: Petree - first_name: Kevin full_name: Huang, Kevin id: 3b3d2888-1ff6-11ee-9fa6-8f209ca91fe3 last_name: Huang orcid: 0000-0002-2512-7812 - first_name: Kamel full_name: Monastiri, Kamel last_name: Monastiri - first_name: Masoud full_name: Edizadeh, Masoud last_name: Edizadeh - first_name: Rosaria full_name: Nardello, Rosaria last_name: Nardello - first_name: Marzia full_name: Ognibene, Marzia last_name: Ognibene - first_name: Patrizia full_name: De Marco, Patrizia last_name: De Marco - first_name: Martino full_name: Ruggieri, Martino last_name: Ruggieri - first_name: Federico full_name: Zara, Federico last_name: Zara - first_name: Pasquale full_name: Striano, Pasquale last_name: Striano - first_name: Yavuz full_name: Sahin, Yavuz last_name: Sahin - first_name: Lihadh full_name: Al-Gazali, Lihadh last_name: Al-Gazali - first_name: Marie Therese Abi full_name: Warde, Marie Therese Abi last_name: Warde - first_name: Benedicte full_name: Gerard, Benedicte last_name: Gerard - first_name: Giovanni full_name: Zifarelli, Giovanni last_name: Zifarelli - first_name: Christian full_name: Beetz, Christian last_name: Beetz - first_name: Sara full_name: Fortuna, Sara last_name: Fortuna - first_name: Miguel full_name: Soler, Miguel last_name: Soler - first_name: Enza Maria full_name: Valente, Enza Maria last_name: Valente - first_name: Gaurav full_name: Varshney, Gaurav last_name: Varshney - 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 citation: ama: Cali E, Lin S-J, Rocca C, et al. A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease. Genetics in Medicine. 2022;24(10):2194-2203. doi:10.1016/j.gim.2022.07.013 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. Genetics in Medicine. Elsevier. https://doi.org/10.1016/j.gim.2022.07.013 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.” Genetics in Medicine. Elsevier, 2022. https://doi.org/10.1016/j.gim.2022.07.013. ieee: E. Cali et al., “A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease,” Genetics in Medicine, vol. 24, no. 10. Elsevier, pp. 2194–2203, 2022. 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.” Genetics in Medicine, vol. 24, no. 10, Elsevier, 2022, pp. 2194–203, doi:10.1016/j.gim.2022.07.013. 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. date_created: 2023-09-20T20:57:18Z date_published: 2022-10-01T00:00:00Z date_updated: 2023-09-25T08:57:07Z day: '01' ddc: - '570' department: - _id: GradSch doi: 10.1016/j.gim.2022.07.013 extern: '1' file: - access_level: open_access checksum: 8117175a89129eb5022d81ffe7625f9f content_type: application/pdf creator: dernst date_created: 2023-09-25T08:56:06Z date_updated: 2023-09-25T08:56:06Z file_id: '14371' file_name: 2022_GeneticsMedicine_Calin.pdf file_size: 1434037 relation: main_file success: 1 file_date_updated: 2023-09-25T08:56:06Z has_accepted_license: '1' intvolume: ' 24' issue: '10' keyword: - Human mediator complex - MED11 - MEDopathies language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 2194-2203 publication: Genetics in Medicine publication_identifier: issn: - 1098-3600 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 24 year: '2022' ... --- _id: '14381' 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. article_processing_charge: No article_type: original author: - first_name: Uli full_name: Wagner, Uli id: 36690CA2-F248-11E8-B48F-1D18A9856A87 last_name: Wagner orcid: 0000-0002-1494-0568 citation: ama: Wagner U. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). Bulletin de la Societe Mathematique de France. 2022;438:281-294. doi:10.24033/ast.1188 apa: Wagner, U. (2022). High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). Bulletin de La Societe Mathematique de France. Societe Mathematique de France. https://doi.org/10.24033/ast.1188 chicago: Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and Others).” Bulletin de La Societe Mathematique de France. Societe Mathematique de France, 2022. https://doi.org/10.24033/ast.1188. ieee: U. Wagner, “High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others),” Bulletin de la Societe Mathematique de France, vol. 438. Societe Mathematique de France, pp. 281–294, 2022. ista: Wagner U. 2022. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others). Bulletin de la Societe Mathematique de France. 438, 281–294. mla: Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and Others).” Bulletin de La Societe Mathematique de France, vol. 438, Societe Mathematique de France, 2022, pp. 281–94, doi:10.24033/ast.1188. short: U. Wagner, Bulletin de La Societe Mathematique de France 438 (2022) 281–294. date_created: 2023-10-01T22:01:14Z date_published: 2022-01-01T00:00:00Z date_updated: 2023-10-03T08:04:03Z day: '01' department: - _id: UlWa doi: 10.24033/ast.1188 intvolume: ' 438' language: - iso: eng month: '01' oa_version: None page: 281-294 publication: Bulletin de la Societe Mathematique de France publication_identifier: eissn: - 2102-622X issn: - 0037-9484 publication_status: published publisher: Societe Mathematique de France quality_controlled: '1' scopus_import: '1' status: public title: High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 438 year: '2022' ... --- _id: '14437' abstract: - lang: eng 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. article_processing_charge: No article_type: letter_note author: - first_name: Hendrik full_name: Utzat, Hendrik last_name: Utzat - first_name: Maria full_name: Ibáñez, Maria id: 43C61214-F248-11E8-B48F-1D18A9856A87 last_name: Ibáñez orcid: 0000-0001-5013-2843 citation: ama: Utzat H, Ibáñez M. Molecular engineering enables bright blue LEDs. Nature. 2022;612(7941):638-639. doi:10.1038/d41586-022-04447-0 apa: Utzat, H., & Ibáñez, M. (2022). Molecular engineering enables bright blue LEDs. Nature. Springer Nature. https://doi.org/10.1038/d41586-022-04447-0 chicago: Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue LEDs.” Nature. Springer Nature, 2022. https://doi.org/10.1038/d41586-022-04447-0. ieee: H. Utzat and M. Ibáñez, “Molecular engineering enables bright blue LEDs,” Nature, vol. 612, no. 7941. Springer Nature, pp. 638–639, 2022. ista: Utzat H, Ibáñez M. 2022. Molecular engineering enables bright blue LEDs. Nature. 612(7941), 638–639. mla: Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue LEDs.” Nature, vol. 612, no. 7941, Springer Nature, 2022, pp. 638–39, doi:10.1038/d41586-022-04447-0. short: H. Utzat, M. Ibáñez, Nature 612 (2022) 638–639. date_created: 2023-10-17T11:14:43Z date_published: 2022-12-21T00:00:00Z date_updated: 2023-10-18T06:26:30Z day: '21' department: - _id: MaIb doi: 10.1038/d41586-022-04447-0 external_id: pmid: - '36543947' intvolume: ' 612' issue: '7941' keyword: - Multidisciplinary language: - iso: eng month: '12' oa_version: None page: 638-639 pmid: 1 publication: Nature publication_identifier: eissn: - 1476-4687 issn: - 0028-0836 publication_status: published publisher: Springer Nature quality_controlled: '1' status: public title: Molecular engineering enables bright blue LEDs type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 612 year: '2022' ... --- _id: '14520' abstract: - lang: eng 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.' article_processing_charge: No author: - first_name: Martin full_name: Zemlicka, Martin id: 2DCF8DE6-F248-11E8-B48F-1D18A9856A87 last_name: Zemlicka - first_name: Elena full_name: Redchenko, Elena id: 2C21D6E8-F248-11E8-B48F-1D18A9856A87 last_name: Redchenko - first_name: Matilda full_name: Peruzzo, Matilda id: 3F920B30-F248-11E8-B48F-1D18A9856A87 last_name: Peruzzo orcid: 0000-0002-3415-4628 - first_name: Farid full_name: Hassani, Farid id: 2AED110C-F248-11E8-B48F-1D18A9856A87 last_name: Hassani orcid: 0000-0001-6937-5773 - first_name: Andrea full_name: Trioni, Andrea id: 42F71B44-F248-11E8-B48F-1D18A9856A87 last_name: Trioni - first_name: Shabir full_name: Barzanjeh, Shabir id: 2D25E1F6-F248-11E8-B48F-1D18A9856A87 last_name: Barzanjeh orcid: 0000-0003-0415-1423 - first_name: Johannes M full_name: Fink, Johannes M id: 4B591CBA-F248-11E8-B48F-1D18A9856A87 last_name: Fink orcid: 0000-0001-8112-028X citation: ama: 'Zemlicka M, Redchenko E, Peruzzo M, et al. Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses. 2022. doi:10.5281/ZENODO.8408897' apa: 'Zemlicka, M., Redchenko, E., Peruzzo, M., Hassani, F., Trioni, A., Barzanjeh, S., & Fink, J. M. (2022). Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses. Zenodo. https://doi.org/10.5281/ZENODO.8408897' 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. https://doi.org/10.5281/ZENODO.8408897.' ieee: 'M. Zemlicka et al., “Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses.” Zenodo, 2022.' 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, 10.5281/ZENODO.8408897.' mla: 'Zemlicka, Martin, et al. Compact Vacuum Gap Transmon Qubits: Selective and Sensitive Probes for Superconductor Surface Losses. Zenodo, 2022, doi:10.5281/ZENODO.8408897.' short: M. Zemlicka, E. Redchenko, M. Peruzzo, F. Hassani, A. Trioni, S. Barzanjeh, J.M. Fink, (2022). date_created: 2023-11-13T08:09:10Z date_published: 2022-06-28T00:00:00Z date_updated: 2024-09-10T12:23:57Z day: '28' ddc: - '530' department: - _id: JoFi doi: 10.5281/ZENODO.8408897 has_accepted_license: '1' license: https://creativecommons.org/publicdomain/zero/1.0/ main_file_link: - open_access: '1' url: https://doi.org/10.5281/ZENODO.8408897 month: '06' oa: 1 oa_version: Published Version publisher: Zenodo related_material: record: - id: '14517' relation: used_in_publication status: public status: public title: 'Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses' tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14597' abstract: - lang: eng 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." article_processing_charge: No arxiv: 1 author: - first_name: Julian L full_name: Fischer, Julian L id: 2C12A0B0-F248-11E8-B48F-1D18A9856A87 last_name: Fischer orcid: 0000-0002-0479-558X - first_name: Alice full_name: Marveggio, Alice id: 25647992-AA84-11E9-9D75-8427E6697425 last_name: Marveggio citation: ama: Fischer JL, Marveggio A. Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. arXiv. doi:10.48550/ARXIV.2203.17143 apa: Fischer, J. L., & Marveggio, A. (n.d.). Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. arXiv. https://doi.org/10.48550/ARXIV.2203.17143 chicago: Fischer, Julian L, and Alice Marveggio. “Quantitative Convergence of the Vectorial Allen-Cahn Equation towards Multiphase Mean Curvature Flow.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2203.17143. ieee: J. L. Fischer and A. Marveggio, “Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow,” arXiv. . ista: Fischer JL, Marveggio A. Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. arXiv, 10.48550/ARXIV.2203.17143. mla: Fischer, Julian L., and Alice Marveggio. “Quantitative Convergence of the Vectorial Allen-Cahn Equation towards Multiphase Mean Curvature Flow.” ArXiv, doi:10.48550/ARXIV.2203.17143. short: J.L. Fischer, A. Marveggio, ArXiv (n.d.). date_created: 2023-11-23T09:30:02Z date_published: 2022-03-31T00:00:00Z date_updated: 2023-11-30T13:25:02Z day: '31' department: - _id: JuFi doi: 10.48550/ARXIV.2203.17143 ec_funded: 1 external_id: arxiv: - '2203.17143' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2203.17143 month: '03' oa: 1 oa_version: Preprint project: - _id: 0aa76401-070f-11eb-9043-b5bb049fa26d call_identifier: H2020 grant_number: '948819' name: Bridging Scales in Random Materials publication: arXiv publication_status: submitted related_material: record: - id: '14587' relation: dissertation_contains status: public status: public title: Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '14600' 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. article_processing_charge: No arxiv: 1 author: - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X citation: ama: Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. arXiv. doi:10.48550/ARXIV.2210.05308 apa: Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (n.d.). Learning control policies for stochastic systems with reach-avoid guarantees. arXiv. https://doi.org/10.48550/ARXIV.2210.05308 chicago: Zikelic, Dorde, Mathias Lechner, Thomas A Henzinger, and Krishnendu Chatterjee. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2210.05308. ieee: D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” arXiv. . ista: Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. arXiv, 10.48550/ARXIV.2210.05308. mla: Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” ArXiv, doi:10.48550/ARXIV.2210.05308. short: D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, ArXiv (n.d.). date_created: 2023-11-24T13:10:09Z date_published: 2022-11-29T00:00:00Z date_updated: 2025-07-14T09:10:02Z day: '29' department: - _id: KrCh - _id: ToHe doi: 10.48550/ARXIV.2210.05308 ec_funded: 1 external_id: arxiv: - '2210.05308' language: - iso: eng license: https://creativecommons.org/licenses/by-sa/4.0/ main_file_link: - open_access: '1' url: https://arxiv.org/abs/2210.05308 month: '11' oa: 1 oa_version: Preprint 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 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: arXiv publication_status: submitted related_material: record: - id: '14539' relation: dissertation_contains status: public - id: '14830' relation: later_version status: public status: public title: Learning control policies for stochastic systems with reach-avoid guarantees tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '14601' abstract: - lang: eng 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." article_processing_charge: No arxiv: 1 author: - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv. doi:10.48550/arXiv.2205.11991 apa: Zikelic, D., Lechner, M., Chatterjee, K., & Henzinger, T. A. (n.d.). Learning stabilizing policies in stochastic control systems. arXiv. https://doi.org/10.48550/arXiv.2205.11991 chicago: Zikelic, Dorde, Mathias Lechner, Krishnendu Chatterjee, and Thomas A Henzinger. “Learning Stabilizing Policies in Stochastic Control Systems.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2205.11991. ieee: D. Zikelic, M. Lechner, K. Chatterjee, and T. A. Henzinger, “Learning stabilizing policies in stochastic control systems,” arXiv. . ista: Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv, 10.48550/arXiv.2205.11991. mla: Zikelic, Dorde, et al. “Learning Stabilizing Policies in Stochastic Control Systems.” ArXiv, doi:10.48550/arXiv.2205.11991. short: D. Zikelic, M. Lechner, K. Chatterjee, T.A. Henzinger, ArXiv (n.d.). date_created: 2023-11-24T13:22:30Z date_published: 2022-05-24T00:00:00Z date_updated: 2025-07-14T09:10:00Z day: '24' department: - _id: KrCh - _id: ToHe doi: 10.48550/arXiv.2205.11991 ec_funded: 1 external_id: arxiv: - '2205.11991' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2205.11991 month: '05' oa: 1 oa_version: Preprint project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: arXiv publication_status: submitted related_material: record: - id: '14539' relation: dissertation_contains status: public status: public title: Learning stabilizing policies in stochastic control systems type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '13064' 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. article_processing_charge: No author: - first_name: Etienne full_name: Orliac, Etienne last_name: Orliac - first_name: Daniel full_name: Trejo Banos, Daniel last_name: Trejo Banos - first_name: Sven full_name: Ojavee, Sven last_name: Ojavee - first_name: Kristi full_name: Läll, Kristi last_name: Läll - first_name: Reedik full_name: Mägi, Reedik last_name: Mägi - first_name: Peter full_name: Visscher, Peter last_name: Visscher - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association discovery and genomic prediction accuracy in biobank data. 2022. doi:10.5061/DRYAD.GTHT76HMZ apa: Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P., & Robinson, M. R. (2022). Improving genome-wide association discovery and genomic prediction accuracy in biobank data. Dryad. https://doi.org/10.5061/DRYAD.GTHT76HMZ 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. https://doi.org/10.5061/DRYAD.GTHT76HMZ. ieee: E. Orliac et al., “Improving genome-wide association discovery and genomic prediction accuracy in biobank data.” Dryad, 2022. 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, 10.5061/DRYAD.GTHT76HMZ. mla: Orliac, Etienne, et al. Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data. Dryad, 2022, doi:10.5061/DRYAD.GTHT76HMZ. short: E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R. Robinson, (2022). date_created: 2023-05-23T16:28:13Z date_published: 2022-09-02T00:00:00Z date_updated: 2023-08-03T12:40:37Z day: '02' ddc: - '570' department: - _id: MaRo doi: 10.5061/DRYAD.GTHT76HMZ main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.gtht76hmz month: '09' oa: 1 oa_version: Published Version publisher: Dryad related_material: record: - id: '11733' relation: used_in_publication status: public status: public title: Improving genome-wide association discovery and genomic prediction accuracy in biobank data tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '13066' abstract: - lang: eng 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. article_processing_charge: No author: - first_name: Eva full_name: Koch, Eva last_name: Koch - first_name: Mark full_name: Ravinet, Mark last_name: Ravinet - first_name: Anja M full_name: Westram, Anja M id: 3C147470-F248-11E8-B48F-1D18A9856A87 last_name: Westram orcid: 0000-0003-1050-4969 - first_name: Kerstin full_name: Jonannesson, Kerstin last_name: Jonannesson - first_name: Roger full_name: Butlin, Roger last_name: Butlin 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:10.5061/DRYAD.M905QFV4B' apa: 'Koch, E., Ravinet, M., Westram, A. M., Jonannesson, K., & Butlin, R. (2022). Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. Dryad. https://doi.org/10.5061/DRYAD.M905QFV4B' 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. https://doi.org/10.5061/DRYAD.M905QFV4B.' 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.' 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, 10.5061/DRYAD.M905QFV4B.' mla: 'Koch, Eva, et al. Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution. Dryad, 2022, doi:10.5061/DRYAD.M905QFV4B.' short: E. Koch, M. Ravinet, A.M. Westram, K. Jonannesson, R. Butlin, (2022). date_created: 2023-05-23T16:33:12Z date_published: 2022-07-28T00:00:00Z date_updated: 2023-08-04T09:42:10Z day: '28' ddc: - '570' department: - _id: NiBa doi: 10.5061/DRYAD.M905QFV4B main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.m905qfv4b month: '07' oa: 1 oa_version: Published Version publisher: Dryad related_material: record: - id: '12247' relation: used_in_publication status: public status: public title: 'Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution' tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '13076' 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." article_processing_charge: No author: - first_name: Anastasiia full_name: Postnikova, Anastasiia last_name: Postnikova - first_name: Nikita full_name: Koval, Nikita id: 2F4DB10C-F248-11E8-B48F-1D18A9856A87 last_name: Koval - first_name: Giorgi full_name: Nadiradze, Giorgi id: 3279A00C-F248-11E8-B48F-1D18A9856A87 last_name: Nadiradze - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. 2022. doi:10.5281/ZENODO.5733408 apa: Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408 chicago: Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo, 2022. https://doi.org/10.5281/ZENODO.5733408. ieee: A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022. ista: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be state-of-the-art priority schedulers, Zenodo, 10.5281/ZENODO.5733408. mla: Postnikova, Anastasiia, et al. Multi-Queues Can Be State-of-the-Art Priority Schedulers. Zenodo, 2022, doi:10.5281/ZENODO.5733408. short: A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022). date_created: 2023-05-23T17:05:40Z date_published: 2022-01-03T00:00:00Z date_updated: 2023-08-03T06:48:34Z day: '03' ddc: - '510' department: - _id: DaAl doi: 10.5281/ZENODO.5733408 main_file_link: - open_access: '1' url: https://doi.org/10.5281/zenodo.5813846 month: '01' oa: 1 oa_version: Published Version publisher: Zenodo related_material: link: - relation: software url: https://github.com/npostnikova/mq-based-schedulers/tree/v1.1 record: - id: '11180' relation: used_in_publication status: public status: public title: Multi-queues can be state-of-the-art priority schedulers type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '13239' abstract: - lang: eng 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. 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." article_processing_charge: No author: - first_name: Thijs L. full_name: Van Der Plas, Thijs L. last_name: Van Der Plas - first_name: Tim P full_name: Vogels, Tim P id: CB6FF8D2-008F-11EA-8E08-2637E6697425 last_name: Vogels orcid: 0000-0003-3295-6181 - first_name: Sanjay G. full_name: Manohar, Sanjay G. last_name: Manohar citation: ama: 'Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural networks to learn complex working memory tasks. In: Proceedings of Machine Learning Research. Vol 199. ML Research Press; 2022:518-531.' apa: Van Der Plas, T. L., Vogels, T. P., & Manohar, S. G. (2022). Predictive learning enables neural networks to learn complex working memory tasks. In Proceedings of Machine Learning Research (Vol. 199, pp. 518–531). ML Research Press. 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 Proceedings of Machine Learning Research, 199:518–31. ML Research Press, 2022. 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 Proceedings of Machine Learning Research, 2022, vol. 199, pp. 518–531. 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. mla: Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” Proceedings of Machine Learning Research, vol. 199, ML Research Press, 2022, pp. 518–31. 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. date_created: 2023-07-16T22:01:12Z date_published: 2022-12-01T00:00:00Z date_updated: 2023-07-18T06:36:28Z day: '01' ddc: - '000' department: - _id: TiVo ec_funded: 1 file: - access_level: open_access checksum: 7530a93ef42e10b4db1e5e4b69796e93 content_type: application/pdf creator: dernst date_created: 2023-07-18T06:32:38Z date_updated: 2023-07-18T06:32:38Z file_id: '13243' file_name: 2022_PMLR_vanderPlas.pdf file_size: 585135 relation: main_file success: 1 file_date_updated: 2023-07-18T06:32:38Z has_accepted_license: '1' intvolume: ' 199' language: - iso: eng month: '12' oa: 1 oa_version: Published Version page: 518-531 project: - _id: 0aacfa84-070f-11eb-9043-d7eb2c709234 call_identifier: H2020 grant_number: '819603' name: Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning. publication: Proceedings of Machine Learning Research publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Predictive learning enables neural networks to learn complex working memory tasks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 199 year: '2022' ... --- _id: '13240' abstract: - lang: eng 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. 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." article_number: '1029114' article_processing_charge: Yes article_type: original author: - first_name: Kishor D. full_name: Ingole, Kishor D. last_name: Ingole - first_name: Nithya full_name: Nagarajan, Nithya last_name: Nagarajan - first_name: Simon full_name: Uhse, Simon last_name: Uhse - first_name: Caterina full_name: Giannini, Caterina id: e3fdddd5-f6e0-11ea-865d-ca99ee6367f4 last_name: Giannini - first_name: Armin full_name: Djamei, Armin last_name: Djamei citation: ama: Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers in Fungal Biology. 2022;3. doi:10.3389/ffunb.2022.1029114 apa: Ingole, K. D., 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. Frontiers Media. https://doi.org/10.3389/ffunb.2022.1029114 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.” Frontiers in Fungal Biology. Frontiers Media, 2022. https://doi.org/10.3389/ffunb.2022.1029114. 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,” Frontiers in Fungal Biology, vol. 3. Frontiers Media, 2022. 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.” Frontiers in Fungal Biology, vol. 3, 1029114, Frontiers Media, 2022, doi:10.3389/ffunb.2022.1029114. short: K.D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, A. Djamei, Frontiers in Fungal Biology 3 (2022). date_created: 2023-07-16T22:01:12Z date_published: 2022-10-19T00:00:00Z date_updated: 2024-03-06T14:01:57Z day: '19' ddc: - '579' department: - _id: JiFr doi: 10.3389/ffunb.2022.1029114 file: - access_level: open_access checksum: 2254e0119c0749d6f7237084fefcece6 content_type: application/pdf creator: dernst date_created: 2023-07-17T11:46:34Z date_updated: 2023-07-17T11:46:34Z file_id: '13242' file_name: 2023_FrontiersFungalBio_Ingole.pdf file_size: 27966699 relation: main_file success: 1 file_date_updated: 2023-07-17T11:46:34Z has_accepted_license: '1' intvolume: ' 3' language: - iso: eng month: '10' oa: 1 oa_version: Published Version publication: Frontiers in Fungal Biology publication_identifier: eissn: - 2673-6128 publication_status: published publisher: Frontiers Media quality_controlled: '1' scopus_import: '1' status: public title: Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 3 year: '2022' ... --- _id: '13241' 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. 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." article_processing_charge: No arxiv: 1 author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning from corrupted data. In: Proceedings of Machine Learning Research. Vol 171. ML Research Press; 2022:59-83.' apa: Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press. chicago: Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” In Proceedings of Machine Learning Research, 171:59–83. ML Research Press, 2022. ieee: N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in Proceedings of Machine Learning Research, 2022, vol. 171, pp. 59–83. 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. mla: Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” Proceedings of Machine Learning Research, vol. 171, ML Research Press, 2022, pp. 59–83. short: N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83. date_created: 2023-07-16T22:01:13Z date_published: 2022-12-01T00:00:00Z date_updated: 2023-09-26T10:44:37Z day: '01' department: - _id: ChLa external_id: arxiv: - '2102.06004' intvolume: ' 171' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2102.06004 month: '12' oa: 1 oa_version: Preprint page: 59-83 publication: Proceedings of Machine Learning Research publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '10802' relation: extended_version status: public scopus_import: '1' status: public title: On the impossibility of fairness-aware learning from corrupted data type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 171 year: '2022' ... --- _id: '14093' 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. ' alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Gideon full_name: Dresdner, Gideon last_name: Dresdner - first_name: Maria-Luiza full_name: Vladarean, Maria-Luiza last_name: Vladarean - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Volkan full_name: Cevher, Volkan last_name: Cevher - first_name: Alp full_name: Yurtsever, Alp last_name: Yurtsever citation: 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: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. Vol 151. ML Research Press; 2022:8439-8457.' apa: '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. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (Vol. 151, pp. 8439–8457). Virtual: ML Research Press.' 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 Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, 151:8439–57. ML Research Press, 2022. 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 Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, Virtual, 2022, vol. 151, pp. 8439–8457. 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.' mla: Dresdner, Gideon, et al. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, vol. 151, ML Research Press, 2022, pp. 8439–57. 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. conference: end_date: 2022-03-30 location: Virtual name: 'AISTATS: Conference on Artificial Intelligence and Statistics' start_date: 2022-03-28 date_created: 2023-08-21T09:27:43Z date_published: 2022-04-01T00:00:00Z date_updated: 2023-09-06T10:28:17Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2202.13212' intvolume: ' 151' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2202.13212 month: '04' oa: 1 oa_version: Preprint page: 8439-8457 publication: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics publication_identifier: issn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: ' Faster one-sample stochastic conditional gradient method for composite convex minimization' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 151 year: '2022' ... --- _id: '14106' abstract: - lang: eng 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." alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Michael full_name: Lohaus, Michael last_name: Lohaus - first_name: Matthäus full_name: Kleindessner, Matthäus last_name: Kleindessner - first_name: Krishnaram full_name: Kenthapadi, Krishnaram last_name: Kenthapadi - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Chris full_name: Russell, Chris last_name: Russell citation: 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: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:16548-16562.' apa: '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. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information Processing Systems Foundation.' 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 36th Conference on Neural Information Processing Systems, 35:16548–62. Neural Information Processing Systems Foundation, 2022. 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 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 16548–16562. 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.' mla: Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 16548–62. 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. conference: end_date: 2022-12-09 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-21T12:12:42Z date_published: 2022-12-15T00:00:00Z date_updated: 2023-09-06T10:29:42Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '2204.04440' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2204.04440 month: '12' oa: 1 oa_version: Preprint page: 16548-16562 publication: 36th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713871088' publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Are two heads the same as one? Identifying disparate treatment in fair neural networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ... --- _id: '14107' abstract: - lang: eng 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." article_processing_charge: No arxiv: 1 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 - first_name: Tianjun full_name: Xiao, Tianjun last_name: Xiao - first_name: Tong full_name: He, Tong last_name: He - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: David full_name: Wipf, David last_name: Wipf - first_name: Yanwei full_name: Fu, Yanwei last_name: Fu - first_name: Zheng full_name: Zhang, Zheng last_name: Zhang citation: ama: 'Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation. In: 36th Conference on Neural Information Processing Systems. ; 2022. doi:10.48550/arXiv.2210.12733' apa: Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022). Self-supervised amodal video object segmentation. In 36th Conference on Neural Information Processing Systems. New Orleans, LA, United States. https://doi.org/10.48550/arXiv.2210.12733 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 36th Conference on Neural Information Processing Systems, 2022. https://doi.org/10.48550/arXiv.2210.12733. ieee: J. Yao et al., “Self-supervised amodal video object segmentation,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022. 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.' mla: Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.” 36th Conference on Neural Information Processing Systems, 2022, doi:10.48550/arXiv.2210.12733. 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. conference: end_date: 2022-12-01 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-21T12:13:25Z date_published: 2022-10-23T00:00:00Z date_updated: 2023-09-11T09:34:17Z day: '23' department: - _id: FrLo doi: 10.48550/arXiv.2210.12733 extern: '1' external_id: arxiv: - '2210.12733' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2210.12733 month: '10' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: published status: public title: Self-supervised amodal video object segmentation type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14114' abstract: - lang: eng 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. article_processing_charge: No arxiv: 1 author: - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - first_name: Michael full_name: Lohaus, Michael last_name: Lohaus - first_name: Guha full_name: Balakrishnan, Guha last_name: Balakrishnan - first_name: Matthaus full_name: Kleindessner, Matthaus last_name: Kleindessner - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bernhard full_name: Scholkopf, Bernhard last_name: Scholkopf - first_name: Chris full_name: Russell, Chris last_name: Russell citation: ama: 'Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016' apa: '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. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10400–10411). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01016' 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 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10400–411. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01016.' ieee: 'D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.' 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.” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–11, doi:10.1109/cvpr52688.2022.01016.' 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. conference: end_date: 2022-06-24 location: New Orleans, LA, United States name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2022-06-18 date_created: 2023-08-21T12:18:00Z date_published: 2022-07-01T00:00:00Z date_updated: 2023-09-11T09:19:14Z day: '01' department: - _id: FrLo doi: 10.1109/cvpr52688.2022.01016 extern: '1' external_id: arxiv: - '2203.04913' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2203.04913 month: '07' oa: 1 oa_version: Preprint page: 10400-10411 publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition publication_identifier: eissn: - 2575-7075 isbn: - '9781665469470' issn: - 1063-6919 publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' scopus_import: '1' status: public title: 'Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ...