--- _id: '14098' abstract: - lang: eng text: Magnetic fields can drastically change predictions of evolutionary models of massive stars via mass-loss quenching, magnetic braking, and efficient angular momentum transport, which we aim to quantify in this work. We use the MESA software instrument to compute an extensive main-sequence grid of stellar structure and evolution models, as well as isochrones, accounting for the effects attributed to a surface fossil magnetic field. The grid is densely populated in initial mass (3–60 M⊙), surface equatorial magnetic field strength (0–50 kG), and metallicity (representative of the Solar neighbourhood and the Magellanic Clouds). We use two magnetic braking and two chemical mixing schemes and compare the model predictions for slowly rotating, nitrogen-enriched (‘Group 2’) stars with observations in the Large Magellanic Cloud. We quantify a range of initial field strengths that allow for producing Group 2 stars and find that typical values (up to a few kG) lead to solutions. Between the subgrids, we find notable departures in surface abundances and evolutionary paths. In our magnetic models, chemical mixing is always less efficient compared to non-magnetic models due to the rapid spin-down. We identify that quasi-chemically homogeneous main sequence evolution by efficient mixing could be prevented by fossil magnetic fields. We recommend comparing this grid of evolutionary models with spectropolarimetric and spectroscopic observations with the goals of (i) revisiting the derived stellar parameters of known magnetic stars, and (ii) observationally constraining the uncertain magnetic braking and chemical mixing schemes. article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Z. full_name: Keszthelyi, Z. last_name: Keszthelyi - first_name: A. de full_name: Koter, A. de last_name: Koter - first_name: Ylva Louise Linsdotter full_name: Götberg, Ylva Louise Linsdotter id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d last_name: Götberg orcid: 0000-0002-6960-6911 - first_name: G. full_name: Meynet, G. last_name: Meynet - first_name: S. A. full_name: Brands, S. A. last_name: Brands - first_name: V. full_name: Petit, V. last_name: Petit - first_name: M. full_name: Carrington, M. last_name: Carrington - first_name: A. David-Uraz full_name: A. David-Uraz, A. David-Uraz last_name: A. David-Uraz - first_name: S. T. full_name: Geen, S. T. last_name: Geen - first_name: C. full_name: Georgy, C. last_name: Georgy - first_name: R. full_name: Hirschi, R. last_name: Hirschi - first_name: J. full_name: Puls, J. last_name: Puls - first_name: K. J. full_name: Ramalatswa, K. J. last_name: Ramalatswa - first_name: M. E. full_name: Shultz, M. E. last_name: Shultz - first_name: A. ud-Doula full_name: A. ud-Doula, A. ud-Doula last_name: A. ud-Doula citation: ama: 'Keszthelyi Z, Koter A de, Götberg YLL, et al. The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities. Monthly Notices of the Royal Astronomical Society. 2022;517(2):2028-2055. doi:10.1093/mnras/stac2598' apa: 'Keszthelyi, Z., Koter, A. de, Götberg, Y. L. L., Meynet, G., Brands, S. A., Petit, V., … A. ud-Doula, A. ud-Doula. (2022). The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities. Monthly Notices of the Royal Astronomical Society. Oxford Academic. https://doi.org/10.1093/mnras/stac2598' chicago: 'Keszthelyi, Z., A. de Koter, Ylva Louise Linsdotter Götberg, G. Meynet, S. A. Brands, V. Petit, M. Carrington, et al. “The Effects of Surface Fossil Magnetic Fields on Massive Star Evolution: IV. Grids of Models at Solar, LMC, and SMC Metallicities.” Monthly Notices of the Royal Astronomical Society. Oxford Academic, 2022. https://doi.org/10.1093/mnras/stac2598.' ieee: 'Z. Keszthelyi et al., “The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities,” Monthly Notices of the Royal Astronomical Society, vol. 517, no. 2. Oxford Academic, pp. 2028–2055, 2022.' ista: 'Keszthelyi Z, Koter A de, Götberg YLL, Meynet G, Brands SA, Petit V, Carrington M, A. David-Uraz AD-U, Geen ST, Georgy C, Hirschi R, Puls J, Ramalatswa KJ, Shultz ME, A. ud-Doula A ud-Doula. 2022. The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities. Monthly Notices of the Royal Astronomical Society. 517(2), 2028–2055.' mla: 'Keszthelyi, Z., et al. “The Effects of Surface Fossil Magnetic Fields on Massive Star Evolution: IV. Grids of Models at Solar, LMC, and SMC Metallicities.” Monthly Notices of the Royal Astronomical Society, vol. 517, no. 2, Oxford Academic, 2022, pp. 2028–55, doi:10.1093/mnras/stac2598.' short: Z. Keszthelyi, A. de Koter, Y.L.L. Götberg, G. Meynet, S.A. Brands, V. Petit, M. Carrington, A.D.-U. A. David-Uraz, S.T. Geen, C. Georgy, R. Hirschi, J. Puls, K.J. Ramalatswa, M.E. Shultz, A. ud-Doula A. ud-Doula, Monthly Notices of the Royal Astronomical Society 517 (2022) 2028–2055. date_created: 2023-08-21T10:11:21Z date_published: 2022-12-01T00:00:00Z date_updated: 2023-08-22T13:18:34Z day: '01' doi: 10.1093/mnras/stac2598 extern: '1' external_id: arxiv: - '2209.06350' intvolume: ' 517' issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1093/mnras/stac2598 month: '12' oa: 1 oa_version: Published Version page: 2028-2055 publication: Monthly Notices of the Royal Astronomical Society publication_identifier: eissn: - 1365-2966 issn: - 0035-8711 publication_status: published publisher: Oxford Academic quality_controlled: '1' scopus_import: '1' status: public title: 'The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 517 year: '2022' ... --- _id: '14099' abstract: - lang: eng text: Magnetism can greatly impact the evolution of stars. In some stars with OBA spectral types there is direct evidence via the Zeeman effect for stable, large-scale magnetospheres, which lead to the spin-down of the stellar surface and reduced mass loss. So far, a comprehensive grid of stellar structure and evolution models accounting for these effects was lacking. For this reason, we computed and studied models with two magnetic braking and two chemical mixing schemes in three metallicity environments with the MESA software instrument. We find notable differences between the subgrids, which affects the model predictions and thus the detailed characterisation of stars. We are able to quantify the impact of magnetic fields in terms of preventing quasi-chemically homogeneous evolution and producing slowly-rotating, nitrogen-enriched ("Group 2") stars. Our model grid is fully open access and open source. article_number: '2211.07060' article_processing_charge: No arxiv: 1 author: - first_name: Z. full_name: Keszthelyi, Z. last_name: Keszthelyi - first_name: A. de full_name: Koter, A. de last_name: Koter - first_name: Ylva Louise Linsdotter full_name: Götberg, Ylva Louise Linsdotter id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d last_name: Götberg orcid: 0000-0002-6960-6911 - first_name: G. full_name: Meynet, G. last_name: Meynet - first_name: S. A. full_name: Brands, S. A. last_name: Brands - first_name: V. full_name: Petit, V. last_name: Petit - first_name: M. full_name: Carrington, M. last_name: Carrington - first_name: A. David-Uraz full_name: A. David-Uraz, A. David-Uraz last_name: A. David-Uraz - first_name: S. T. full_name: Geen, S. T. last_name: Geen - first_name: C. full_name: Georgy, C. last_name: Georgy - first_name: R. full_name: Hirschi, R. last_name: Hirschi - first_name: J. full_name: Puls, J. last_name: Puls - first_name: K. J. full_name: Ramalatswa, K. J. last_name: Ramalatswa - first_name: M. E. full_name: Shultz, M. E. last_name: Shultz - first_name: A. ud-Doula full_name: A. ud-Doula, A. ud-Doula last_name: A. ud-Doula citation: ama: Keszthelyi Z, Koter A de, Götberg YLL, et al. Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields. arXiv. doi:10.48550/arXiv.2211.07060 apa: Keszthelyi, Z., Koter, A. de, Götberg, Y. L. L., Meynet, G., Brands, S. A., Petit, V., … A. ud-Doula, A. ud-Doula. (n.d.). Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields. arXiv. https://doi.org/10.48550/arXiv.2211.07060 chicago: Keszthelyi, Z., A. de Koter, Ylva Louise Linsdotter Götberg, G. Meynet, S. A. Brands, V. Petit, M. Carrington, et al. “Spin-down and Reduced Mass Loss in Early-Type Stars with Large-Scale Magnetic Fields.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2211.07060. ieee: Z. Keszthelyi et al., “Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields,” arXiv. . ista: Keszthelyi Z, Koter A de, Götberg YLL, Meynet G, Brands SA, Petit V, Carrington M, A. David-Uraz AD-U, Geen ST, Georgy C, Hirschi R, Puls J, Ramalatswa KJ, Shultz ME, A. ud-Doula A ud-Doula. Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields. arXiv, 2211.07060. mla: Keszthelyi, Z., et al. “Spin-down and Reduced Mass Loss in Early-Type Stars with Large-Scale Magnetic Fields.” ArXiv, 2211.07060, doi:10.48550/arXiv.2211.07060. short: Z. Keszthelyi, A. de Koter, Y.L.L. Götberg, G. Meynet, S.A. Brands, V. Petit, M. Carrington, A.D.-U. A. David-Uraz, S.T. Geen, C. Georgy, R. Hirschi, J. Puls, K.J. Ramalatswa, M.E. Shultz, A. ud-Doula A. ud-Doula, ArXiv (n.d.). date_created: 2023-08-21T10:11:37Z date_published: 2022-11-14T00:00:00Z date_updated: 2023-08-22T13:20:15Z day: '14' doi: 10.48550/arXiv.2211.07060 extern: '1' external_id: arxiv: - '2211.07060' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.07060 month: '11' oa: 1 oa_version: Submitted Version publication: arXiv publication_status: submitted status: public title: Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 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: 2024-10-14T12:27:01Z 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' ... --- _id: '14168' abstract: - lang: eng text: "Recent work has seen the development of general purpose neural architectures\r\nthat can be trained to perform tasks across diverse data modalities. General\r\npurpose models typically make few assumptions about the underlying\r\ndata-structure and are known to perform well in the large-data regime. At the\r\nsame time, there has been growing interest in modular neural architectures that\r\nrepresent the data using sparsely interacting modules. These models can be more\r\nrobust out-of-distribution, computationally efficient, and capable of\r\nsample-efficient adaptation to new data. However, they tend to make\r\ndomain-specific assumptions about the data, and present challenges in how\r\nmodule behavior (i.e., parameterization) and connectivity (i.e., their layout)\r\ncan be jointly learned. In this work, we introduce a general purpose, yet\r\nmodular neural architecture called Neural Attentive Circuits (NACs) that\r\njointly learns the parameterization and a sparse connectivity of neural modules\r\nwithout using domain knowledge. NACs are best understood as the combination of\r\ntwo systems that are jointly trained end-to-end: one that determines the module\r\nconfiguration and the other that executes it on an input. We demonstrate\r\nqualitatively that NACs learn diverse and meaningful module configurations on\r\nthe NLVR2 dataset without additional supervision. Quantitatively, we show that\r\nby incorporating modularity in this way, NACs improve upon a strong non-modular\r\nbaseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about\r\n10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that\r\nNACs can achieve an 8x speedup at inference time while losing less than 3%\r\nperformance. Finally, we find NACs to yield competitive results on diverse data\r\nmodalities spanning point-cloud classification, symbolic processing and\r\ntext-classification from ASCII bytes, thereby confirming its general purpose\r\nnature." alternative_title: - ' Advances in Neural Information Processing Systems' article_processing_charge: No arxiv: 1 author: - first_name: Nasim full_name: Rahaman, Nasim last_name: Rahaman - first_name: Martin full_name: Weiss, Martin last_name: Weiss - 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: Pal, Chris last_name: Pal - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Li Erran full_name: Li, Li Erran last_name: Li - first_name: Nicolas full_name: Ballas, Nicolas last_name: Ballas citation: ama: 'Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: 36th Conference on Neural Information Processing Systems. Vol 35. ; 2022.' apa: Rahaman, N., Weiss, M., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B., … Ballas, N. (2022). Neural attentive circuits. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, United States. chicago: Rahaman, Nasim, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, and Nicolas Ballas. “Neural Attentive Circuits.” In 36th Conference on Neural Information Processing Systems, Vol. 35, 2022. ieee: N. Rahaman et al., “Neural attentive circuits,” in 36th Conference on Neural Information Processing Systems, New Orleans, United States, 2022, vol. 35. ista: 'Rahaman N, Weiss M, Locatello F, Pal C, Bengio Y, Schölkopf B, Li LE, Ballas N. 2022. Neural attentive circuits. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35.' mla: Rahaman, Nasim, et al. “Neural Attentive Circuits.” 36th Conference on Neural Information Processing Systems, vol. 35, 2022. short: N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E. Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems, 2022. conference: end_date: 2022-12-01 location: New Orleans, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-29 date_created: 2023-08-22T13:57:27Z date_published: 2022-10-14T00:00:00Z date_updated: 2023-09-11T09:29:09Z day: '14' department: - _id: FrLo extern: '1' external_id: arxiv: - '2210.08031' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2210.08031 month: '10' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: published status: public title: Neural attentive circuits type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ... --- _id: '14170' abstract: - lang: eng text: "The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and performance of downstream tasks in scenes with multiple objects. In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets and evaluate segmentation metrics and downstream object property prediction. In addition, we study generalization and robustness by investigating the settings where either a single object is out of distribution -- e.g., having an unseen color, texture, or shape -- or global properties of the scene are altered -- e.g., by occlusions, cropping, or increasing the number of objects. From our experimental study, we find object-centric representations to be useful for\r\ndownstream tasks and generally robust to most distribution shifts affecting objects. However, when the distribution shift affects the input in a less structured manner, robustness in terms of segmentation and downstream task performance may vary significantly across models and distribution shifts. " alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Samuele full_name: Papa, Samuele last_name: Papa - first_name: Michele De full_name: Vita, Michele De last_name: Vita - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Ole full_name: Winther, Ole last_name: Winther - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. In: Proceedings of the 39th International Conference on Machine Learning. Vol 2022. ML Research Press; :5221-5285.' apa: 'Dittadi, A., Papa, S., Vita, M. D., Schölkopf, B., Winther, O., & Locatello, F. (n.d.). Generalization and robustness implications in object-centric learning. In Proceedings of the 39th International Conference on Machine Learning (Vol. 2022, pp. 5221–5285). Baltimore, MD, United States: ML Research Press.' chicago: Dittadi, Andrea, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello. “Generalization and Robustness Implications in Object-Centric Learning.” In Proceedings of the 39th International Conference on Machine Learning, 2022:5221–85. ML Research Press, n.d. ieee: A. Dittadi, S. Papa, M. D. Vita, B. Schölkopf, O. Winther, and F. Locatello, “Generalization and robustness implications in object-centric learning,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, vol. 2022, pp. 5221–5285. ista: Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 2022, 5221–5285. mla: Dittadi, Andrea, et al. “Generalization and Robustness Implications in Object-Centric Learning.” Proceedings of the 39th International Conference on Machine Learning, vol. 2022, ML Research Press, pp. 5221–85. short: A. Dittadi, S. Papa, M.D. Vita, B. Schölkopf, O. Winther, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, n.d., pp. 5221–5285. conference: end_date: 2022-07-23 location: Baltimore, MD, United States name: International Conference on Machine Learning start_date: 2022-07-17 date_created: 2023-08-22T13:59:55Z date_published: 2022-07-22T00:00:00Z date_updated: 2023-09-11T10:08:14Z day: '22' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.00637' intvolume: ' 2022' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2107.00637 month: '07' oa: 1 oa_version: Preprint page: 5221-5285 publication: Proceedings of the 39th International Conference on Machine Learning publication_status: submitted publisher: ML Research Press quality_controlled: '1' status: public title: Generalization and robustness implications in object-centric learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2022 year: '2022' ... --- _id: '14171' abstract: - lang: eng text: "This paper demonstrates how to recover causal graphs from the score of the\r\ndata distribution in non-linear additive (Gaussian) noise models. Using score\r\nmatching algorithms as a building block, we show how to design a new generation\r\nof scalable causal discovery methods. To showcase our approach, we also propose\r\na new efficient method for approximating the score's Jacobian, enabling to\r\nrecover the causal graph. Empirically, we find that the new algorithm, called\r\nSCORE, is competitive with state-of-the-art causal discovery methods while\r\nbeing significantly faster." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Paul full_name: Rolland, Paul last_name: Rolland - first_name: Volkan full_name: Cevher, Volkan last_name: Cevher - first_name: Matthäus full_name: Kleindessner, Matthäus last_name: Kleindessner - first_name: Chris full_name: Russel, Chris last_name: Russel - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Dominik full_name: Janzing, Dominik last_name: Janzing - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal discovery of nonlinear additive noise  models. In: Proceedings of the 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:18741-18753.' apa: 'Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing, D., & Locatello, F. (2022). Score matching enables causal discovery of nonlinear additive noise  models. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 18741–18753). Baltimore, MD, United States: ML Research Press.' chicago: Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” In Proceedings of the 39th International Conference on Machine Learning, 162:18741–53. ML Research Press, 2022. ieee: P. Rolland et al., “Score matching enables causal discovery of nonlinear additive noise  models,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753. ista: Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello F. 2022. Score matching enables causal discovery of nonlinear additive noise  models. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 162, 18741–18753. mla: Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 18741–53. short: P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 18741–18753. conference: end_date: 2022-07-23 location: Baltimore, MD, United States name: International Conference on Machine Learning start_date: 2022-07-17 date_created: 2023-08-22T14:00:18Z date_published: 2022-07-22T00:00:00Z date_updated: 2023-09-11T10:14:20Z day: '22' department: - _id: FrLo extern: '1' external_id: arxiv: - '2203.04413' intvolume: ' 162' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2203.04413 month: '07' oa: 1 oa_version: Preprint page: 18741-18753 publication: Proceedings of the 39th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: Score matching enables causal discovery of nonlinear additive noise models type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 162 year: '2022' ... --- _id: '14172' abstract: - lang: eng text: "An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly infer the generative factors of variation in simple datasets (dSprites, Shapes3D, MPI3D) from controlled environments, and on our contributed CelebGlow dataset. In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models\r\nthat learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards\r\nmore realistic real-world datasets. Despite their inability to identify the correct mechanism, the models are quite modular as their ability to infer other in-distribution factors remains fairly stable, providing only a single factoris out-of-distribution. These results point to an important yet understudied problem of learning mechanistic models of observations that can facilitate\r\ngeneralization." article_processing_charge: No arxiv: 1 author: - first_name: Lukas full_name: Schott, Lukas last_name: Schott - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Chris full_name: Russell, Chris last_name: Russell - first_name: Matthias full_name: Bethge, Matthias last_name: Bethge - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Wieland full_name: Brendel, Wieland last_name: Brendel citation: ama: 'Schott L, Kügelgen J von, Träuble F, et al. Visual representation learning does not generalize strongly within the  same domain. In: 10th International Conference on Learning Representations. ; 2022.' apa: Schott, L., Kügelgen, J. von, Träuble, F., Gehler, P., Russell, C., Bethge, M., … Brendel, W. (2022). Visual representation learning does not generalize strongly within the  same domain. In 10th International Conference on Learning Representations. Virtual. chicago: Schott, Lukas, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” In 10th International Conference on Learning Representations, 2022. ieee: L. Schott et al., “Visual representation learning does not generalize strongly within the  same domain,” in 10th International Conference on Learning Representations, Virtual, 2022. ista: 'Schott L, Kügelgen J von, Träuble F, Gehler P, Russell C, Bethge M, Schölkopf B, Locatello F, Brendel W. 2022. Visual representation learning does not generalize strongly within the  same domain. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” 10th International Conference on Learning Representations, 2022. short: L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:00:50Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:40:52Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.08221' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2107.08221 month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: Visual representation learning does not generalize strongly within the same domain type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14173' abstract: - lang: eng text: "Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same\r\nexperimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and\r\nfew-shot setting. Our findings confirm that in- and out-of-distribution accuracies tend to increase jointly, but show that their relation is largely dataset-dependent, and in general more nuanced and more complex than posited by previous, smaller scale studies." alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Florian full_name: Wenzel, Florian last_name: Wenzel - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Peter Vincent full_name: Gehler, Peter Vincent last_name: Gehler - first_name: Carl-Johann Simon-Gabriel full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel last_name: Carl-Johann Simon-Gabriel - first_name: Max full_name: Horn, Max last_name: Horn - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - first_name: David full_name: Kernert, David last_name: Kernert - first_name: Chris full_name: Russell, Chris last_name: Russell - first_name: Thomas full_name: Brox, Thomas last_name: Brox - first_name: Bernt full_name: Schiele, Bernt last_name: Schiele - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Wenzel F, Dittadi A, Gehler PV, et al. Assaying out-of-distribution generalization in transfer learning. In: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:7181-7198.' apa: 'Wenzel, F., Dittadi, A., Gehler, P. V., Carl-Johann Simon-Gabriel, C.-J. S.-G., Horn, M., Zietlow, D., … Locatello, F. (2022). Assaying out-of-distribution generalization in transfer learning. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 7181–7198). New Orleans, LA, United States: Neural Information Processing Systems Foundation.' chicago: Wenzel, Florian, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” In 36th Conference on Neural Information Processing Systems, 35:7181–98. Neural Information Processing Systems Foundation, 2022. ieee: F. Wenzel et al., “Assaying out-of-distribution generalization in transfer learning,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 7181–7198. ista: 'Wenzel F, Dittadi A, Gehler PV, Carl-Johann Simon-Gabriel C-JS-G, Horn M, Zietlow D, Kernert D, Russell C, Brox T, Schiele B, Schölkopf B, Locatello F. 2022. Assaying out-of-distribution generalization in transfer learning. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35, 7181–7198.' mla: Wenzel, Florian, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 7181–98. short: F. Wenzel, A. Dittadi, P.V. Gehler, C.-J.S.-G. Carl-Johann Simon-Gabriel, M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf, F. Locatello, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 7181–7198. 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: 2023-08-22T14:01:13Z date_published: 2022-12-15T00:00:00Z date_updated: 2023-09-06T10:34:43Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '2207.09239' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2207.09239 month: '12' oa: 1 oa_version: Preprint page: 7181-7198 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: Assaying out-of-distribution generalization in transfer learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ... --- _id: '14174' abstract: - lang: eng text: "Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facilitate efficient downstream learning and generalization. By training 240 representations and over 10,000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of\r\npretrained VAE-based representations affect the OOD generalization of downstream agents. We observe that many agents are surprisingly robust to realistic distribution shifts, including the challenging sim-to-real case. In addition, we find that the generalization performance of a simple downstream proxy task reliably predicts the generalization performance of our RL agents\r\nunder a wide range of OOD settings. Such proxy tasks can thus be used to select pretrained representations that will lead to agents that generalize." article_processing_charge: No arxiv: 1 author: - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Manuel full_name: Wüthrich, Manuel last_name: Wüthrich - first_name: Felix full_name: Widmaier, Felix last_name: Widmaier - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Ole full_name: Winther, Ole last_name: Winther - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Olivier full_name: Bachem, Olivier last_name: Bachem - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer citation: ama: 'Dittadi A, Träuble F, Wüthrich M, et al. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In: 10th International Conference on Learning Representations. ; 2022.' apa: Dittadi, A., Träuble, F., Wüthrich, M., Widmaier, F., Gehler, P., Winther, O., … Bauer, S. (2022). The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In 10th International Conference on Learning Representations. Virtual. chicago: Dittadi, Andrea, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” In 10th International Conference on Learning Representations, 2022. ieee: A. Dittadi et al., “The role of pretrained representations for the OOD generalization of  reinforcement learning agents,” in 10th International Conference on Learning Representations, Virtual, 2022. ista: 'Dittadi A, Träuble F, Wüthrich M, Widmaier F, Gehler P, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2022. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Dittadi, Andrea, et al. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” 10th International Conference on Learning Representations, 2022. short: A. Dittadi, F. Träuble, M. Wüthrich, F. Widmaier, P. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:02:13Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:48:36Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.05686' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2107.05686' month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: The role of pretrained representations for the OOD generalization of reinforcement learning agents type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14175' abstract: - lang: eng text: "Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions\r\nof different cues to model performance based on a variant of Shapley values. Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social\r\ninteraction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality." article_processing_charge: No arxiv: 1 author: - first_name: Osama full_name: Makansi, Osama last_name: Makansi - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Dominik full_name: Janzing, Dominik last_name: Janzing - first_name: Thomas full_name: Brox, Thomas last_name: Brox - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Makansi O, Kügelgen J von, Locatello F, et al. You mostly walk alone: Analyzing feature attribution in trajectory prediction. In: 10th International Conference on Learning Representations. ; 2022.' apa: 'Makansi, O., Kügelgen, J. von, Locatello, F., Gehler, P., Janzing, D., Brox, T., & Schölkopf, B. (2022). You mostly walk alone: Analyzing feature attribution in trajectory prediction. In 10th International Conference on Learning Representations. Virtual.' chicago: 'Makansi, Osama, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, and Bernhard Schölkopf. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” In 10th International Conference on Learning Representations, 2022.' ieee: 'O. Makansi et al., “You mostly walk alone: Analyzing feature attribution in trajectory prediction,” in 10th International Conference on Learning Representations, Virtual, 2022.' ista: 'Makansi O, Kügelgen J von, Locatello F, Gehler P, Janzing D, Brox T, Schölkopf B. 2022. You mostly walk alone: Analyzing feature attribution in trajectory prediction. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: 'Makansi, Osama, et al. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” 10th International Conference on Learning Representations, 2022.' short: O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox, B. Schölkopf, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:02:34Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:52:20Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2110.05304' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2110.05304 month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: 'You mostly walk alone: Analyzing feature attribution in trajectory prediction' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14215' abstract: - lang: eng text: Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals. article_processing_charge: No arxiv: 1 author: - first_name: Nasim full_name: Rahaman, Nasim last_name: Rahaman - first_name: Martin full_name: Weiss, Martin last_name: Weiss - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Alexandre full_name: Lacoste, Alexandre last_name: Lacoste - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio - first_name: Chris full_name: Pal, Chris last_name: Pal - first_name: Li Erran full_name: Li, Li Erran last_name: Li - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Rahaman N, Weiss M, Träuble F, et al. A general purpose neural architecture for geospatial systems. In: 36th Conference on Neural Information Processing Systems.' apa: Rahaman, N., Weiss, M., Träuble, F., Locatello, F., Lacoste, A., Bengio, Y., … Schölkopf, B. (n.d.). A general purpose neural architecture for geospatial systems. In 36th Conference on Neural Information Processing Systems. New Orleans, LA, United States. chicago: Rahaman, Nasim, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, and Bernhard Schölkopf. “A General Purpose Neural Architecture for Geospatial Systems.” In 36th Conference on Neural Information Processing Systems, n.d. ieee: N. Rahaman et al., “A general purpose neural architecture for geospatial systems,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States. ista: 'Rahaman N, Weiss M, Träuble F, Locatello F, Lacoste A, Bengio Y, Pal C, Li LE, Schölkopf B. A general purpose neural architecture for geospatial systems. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.' mla: Rahaman, Nasim, et al. “A General Purpose Neural Architecture for Geospatial Systems.” 36th Conference on Neural Information Processing Systems. short: N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C. Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing Systems, n.d. 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: 2023-08-22T14:21:47Z date_published: 2022-11-04T00:00:00Z date_updated: 2023-09-13T09:35:59Z day: '04' department: - _id: FrLo extern: '1' external_id: arxiv: - '2211.02348' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.02348 month: '11' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: submitted quality_controlled: '1' status: public title: A general purpose neural architecture for geospatial systems type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14220' abstract: - lang: eng text: Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings and extrapolate this skill zero-shot without any drop in performance when the number of objects changes. We consider the generic task of bringing a specific cube out of a set to a goal position. We find that previous approaches, which primarily leverage attention and graph neural network-based architectures, do not generalize their skills when the number of input objects changes while scaling as K2. We propose an alternative plug-and-play module based on relational inductive biases to overcome these limitations. Besides exceeding performances in their training environment, we show that our approach, which scales linearly in K, allows agents to extrapolate and generalize zero-shot to any new object number. article_number: '2201.13388' article_processing_charge: No arxiv: 1 author: - first_name: Davide full_name: Mambelli, Davide last_name: Mambelli - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv. doi:10.48550/arXiv.2201.13388 apa: Mambelli, D., Träuble, F., Bauer, S., Schölkopf, B., & Locatello, F. (n.d.). Compositional multi-object reinforcement learning with linear relation networks. arXiv. https://doi.org/10.48550/arXiv.2201.13388 chicago: Mambelli, Davide, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, and Francesco Locatello. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2201.13388. ieee: D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, and F. Locatello, “Compositional multi-object reinforcement learning with linear relation networks,” arXiv. . ista: Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv, 2201.13388. mla: Mambelli, Davide, et al. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” ArXiv, 2201.13388, doi:10.48550/arXiv.2201.13388. short: D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.). date_created: 2023-08-22T14:23:16Z date_published: 2022-01-31T00:00:00Z date_updated: 2024-10-14T12:27:39Z day: '31' department: - _id: FrLo doi: 10.48550/arXiv.2201.13388 extern: '1' external_id: arxiv: - '2201.13388' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2201.13388 month: '01' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Compositional multi-object reinforcement learning with linear relation networks type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14236' abstract: - lang: eng text: We show an $(1+\epsilon)$-approximation algorithm for maintaining maximum $s$-$t$ flow under $m$ edge insertions in $m^{1/2+o(1)} \epsilon^{-1/2}$ amortized update time for directed, unweighted graphs. This constitutes the first sublinear dynamic maximum flow algorithm in general sparse graphs with arbitrarily good approximation guarantee. article_number: '2211.09606' article_processing_charge: No arxiv: 1 author: - first_name: Gramoz full_name: Goranci, Gramoz last_name: Goranci - first_name: Monika H full_name: Henzinger, Monika H id: 540c9bbd-f2de-11ec-812d-d04a5be85630 last_name: Henzinger orcid: 0000-0002-5008-6530 citation: ama: Goranci G, Henzinger M. Incremental approximate maximum flow in m1/2+o(1) update time. arXiv. doi:10.48550/arXiv.2211.09606 apa: Goranci, G., & Henzinger, M. (n.d.). Incremental approximate maximum flow in m1/2+o(1) update time. arXiv. https://doi.org/10.48550/arXiv.2211.09606 chicago: Goranci, Gramoz, and Monika Henzinger. “Incremental Approximate Maximum Flow in M1/2+o(1) Update Time.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2211.09606. ieee: G. Goranci and M. Henzinger, “Incremental approximate maximum flow in m1/2+o(1) update time,” arXiv. . ista: Goranci G, Henzinger M. Incremental approximate maximum flow in m1/2+o(1) update time. arXiv, 2211.09606. mla: Goranci, Gramoz, and Monika Henzinger. “Incremental Approximate Maximum Flow in M1/2+o(1) Update Time.” ArXiv, 2211.09606, doi:10.48550/arXiv.2211.09606. short: G. Goranci, M. Henzinger, ArXiv (n.d.). date_created: 2023-08-25T15:04:29Z date_published: 2022-11-17T00:00:00Z date_updated: 2024-11-06T12:01:45Z day: '17' doi: 10.48550/arXiv.2211.09606 extern: '1' external_id: arxiv: - '2211.09606' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.09606 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Incremental approximate maximum flow in m1/2+o(1) update time type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14248' abstract: - lang: eng text: "Recent work by Forsgård indicates that not every convex lattice polygon arises as the characteristic polygon of an affine dimer or, equivalently, an admissible oriented line arrangement on the torus in general position. We begin the classication of convex lattice polygons arising as characteristic polygons of affine dimers. We present several general constructions of new affine dimers from old, and an algorithm for finding affine dimers with prescribed polygon.\r\n\r\nWith these tools we prove that all lattice triangles, generalised parallelograms, and polygons of genus at most two admit an affine dimer." article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Daniel full_name: Holmes, Daniel id: 3a443b4c-080d-11ed-979a-feb062bdcee0 last_name: Holmes citation: ama: Holmes D. Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. 2022;5:24-51. apa: Holmes, D. (2022). Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. California State University. chicago: Holmes, Daniel. “Affine Dimers from Characteristic Polygons.” PUMP Journal of Undergraduate Research. California State University, 2022. ieee: D. Holmes, “Affine dimers from characteristic polygons,” PUMP Journal of Undergraduate Research, vol. 5. California State University, pp. 24–51, 2022. ista: Holmes D. 2022. Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. 5, 24–51. mla: Holmes, Daniel. “Affine Dimers from Characteristic Polygons.” PUMP Journal of Undergraduate Research, vol. 5, California State University, 2022, pp. 24–51. short: D. Holmes, PUMP Journal of Undergraduate Research 5 (2022) 24–51. corr_author: '1' date_created: 2023-08-29T13:08:09Z date_published: 2022-02-13T00:00:00Z date_updated: 2024-10-09T21:06:47Z day: '13' extern: '1' external_id: arxiv: - '2110.01703' intvolume: ' 5' keyword: - dimer model - hyperplane arrangement - torus - lattice polygon language: - iso: eng main_file_link: - open_access: '1' url: https://journals.calstate.edu/pump/article/view/2711 month: '02' oa: 1 oa_version: Published Version page: 24-51 publication: PUMP Journal of Undergraduate Research publication_identifier: issn: - 2576-3725 publication_status: published publisher: California State University quality_controlled: '1' status: public title: Affine dimers from characteristic polygons type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 5 year: '2022' ... --- _id: '14282' abstract: - lang: eng text: Asymmetric multiprotein complexes that undergo subunit exchange play central roles in biology but present a challenge for design because the components must not only contain interfaces that enable reversible association but also be stable and well behaved in isolation. We use implicit negative design to generate β sheet–mediated heterodimers that can be assembled into a wide variety of complexes. The designs are stable, folded, and soluble in isolation and rapidly assemble upon mixing, and crystal structures are close to the computational models. We construct linearly arranged hetero-oligomers with up to six different components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub and demonstrate that such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems. article_number: abj7662 article_processing_charge: No article_type: original author: - first_name: Danny D. full_name: Sahtoe, Danny D. last_name: Sahtoe - first_name: Florian M full_name: Praetorius, Florian M id: dfec9381-4341-11ee-8fd8-faa02bba7d62 last_name: Praetorius - first_name: Alexis full_name: Courbet, Alexis last_name: Courbet - first_name: Yang full_name: Hsia, Yang last_name: Hsia - first_name: Basile I. M. full_name: Wicky, Basile I. M. last_name: Wicky - first_name: Natasha I. full_name: Edman, Natasha I. last_name: Edman - first_name: Lauren M. full_name: Miller, Lauren M. last_name: Miller - first_name: Bart J. R. full_name: Timmermans, Bart J. R. last_name: Timmermans - first_name: Justin full_name: Decarreau, Justin last_name: Decarreau - first_name: Hana M. full_name: Morris, Hana M. last_name: Morris - first_name: Alex full_name: Kang, Alex last_name: Kang - first_name: Asim K. full_name: Bera, Asim K. last_name: Bera - first_name: David full_name: Baker, David last_name: Baker citation: ama: Sahtoe DD, Praetorius FM, Courbet A, et al. Reconfigurable asymmetric protein assemblies through implicit negative design. Science. 2022;375(6578). doi:10.1126/science.abj7662 apa: Sahtoe, D. D., Praetorius, F. M., Courbet, A., Hsia, Y., Wicky, B. I. M., Edman, N. I., … Baker, D. (2022). Reconfigurable asymmetric protein assemblies through implicit negative design. Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.abj7662 chicago: Sahtoe, Danny D., Florian M Praetorius, Alexis Courbet, Yang Hsia, Basile I. M. Wicky, Natasha I. Edman, Lauren M. Miller, et al. “Reconfigurable Asymmetric Protein Assemblies through Implicit Negative Design.” Science. American Association for the Advancement of Science, 2022. https://doi.org/10.1126/science.abj7662. ieee: D. D. Sahtoe et al., “Reconfigurable asymmetric protein assemblies through implicit negative design,” Science, vol. 375, no. 6578. American Association for the Advancement of Science, 2022. ista: Sahtoe DD, Praetorius FM, Courbet A, Hsia Y, Wicky BIM, Edman NI, Miller LM, Timmermans BJR, Decarreau J, Morris HM, Kang A, Bera AK, Baker D. 2022. Reconfigurable asymmetric protein assemblies through implicit negative design. Science. 375(6578), abj7662. mla: Sahtoe, Danny D., et al. “Reconfigurable Asymmetric Protein Assemblies through Implicit Negative Design.” Science, vol. 375, no. 6578, abj7662, American Association for the Advancement of Science, 2022, doi:10.1126/science.abj7662. short: D.D. Sahtoe, F.M. Praetorius, A. Courbet, Y. Hsia, B.I.M. Wicky, N.I. Edman, L.M. Miller, B.J.R. Timmermans, J. Decarreau, H.M. Morris, A. Kang, A.K. Bera, D. Baker, Science 375 (2022). date_created: 2023-09-06T12:05:42Z date_published: 2022-01-21T00:00:00Z date_updated: 2023-11-07T12:39:56Z day: '21' doi: 10.1126/science.abj7662 extern: '1' external_id: pmid: - '35050655' intvolume: ' 375' issue: '6578' language: - iso: eng month: '01' oa_version: None pmid: 1 publication: Science publication_identifier: eissn: - 1095-9203 issn: - 0036-8075 publication_status: published publisher: American Association for the Advancement of Science quality_controlled: '1' scopus_import: '1' status: public title: Reconfigurable asymmetric protein assemblies through implicit negative design type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 375 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: '14356' abstract: - lang: eng text: Aminoacyl-tRNA synthetases (ARSs) are essential enzymes for faithful assignment of amino acids to their cognate tRNA. Variants in ARS genes are frequently associated with clinically heterogeneous phenotypes in humans and follow both autosomal dominant or recessive inheritance patterns in many instances. Variants in tryptophanyl-tRNA synthetase 1 (WARS1) cause autosomal dominantly inherited distal hereditary motor neuropathy and Charcot-Marie-Tooth disease. Presently, only one family with biallelic WARS1 variants has been described. We present three affected individuals from two families with biallelic variants (p.Met1? and p.(Asp419Asn)) in WARS1, showing varying severities of developmental delay and intellectual disability. Hearing impairment and microcephaly, as well as abnormalities of the brain, skeletal system, movement/gait, and behavior were variable features. Phenotyping of knocked down wars-1 in a Caenorhabditis elegans model showed depletion is associated with defects in germ cell development. A wars1 knockout vertebrate model recapitulates the human clinical phenotypes, confirms variant pathogenicity, and uncovers evidence implicating the p.Met1? variant as potentially impacting an exon critical for normal hearing. Together, our findings provide consolidating evidence for biallelic disruption of WARS1 as causal for an autosomal recessive neurodevelopmental syndrome and present a vertebrate model that recapitulates key phenotypes observed in patients. article_processing_charge: No article_type: original author: - first_name: Sheng-Jia full_name: Lin, Sheng-Jia last_name: Lin - first_name: Barbara full_name: Vona, Barbara last_name: Vona - first_name: Hillary M. full_name: Porter, Hillary M. last_name: Porter - first_name: Mahmoud full_name: Izadi, Mahmoud last_name: Izadi - first_name: Kevin full_name: Huang, Kevin id: 3b3d2888-1ff6-11ee-9fa6-8f209ca91fe3 last_name: Huang orcid: 0000-0002-2512-7812 - first_name: Yves full_name: Lacassie, Yves last_name: Lacassie - first_name: Jill A. full_name: Rosenfeld, Jill A. last_name: Rosenfeld - first_name: Saadullah full_name: Khan, Saadullah last_name: Khan - first_name: Cassidy full_name: Petree, Cassidy last_name: Petree - first_name: Tayyiba A. full_name: Ali, Tayyiba A. last_name: Ali - first_name: Nazif full_name: Muhammad, Nazif last_name: Muhammad - first_name: Sher A. full_name: Khan, Sher A. last_name: Khan - first_name: Noor full_name: Muhammad, Noor last_name: Muhammad - first_name: Pengfei full_name: Liu, Pengfei last_name: Liu - first_name: Marie-Louise full_name: Haymon, Marie-Louise last_name: Haymon - first_name: Franz full_name: Rueschendorf, Franz last_name: Rueschendorf - first_name: Il-Keun full_name: Kong, Il-Keun last_name: Kong - first_name: Linda full_name: Schnapp, Linda last_name: Schnapp - first_name: Natasha full_name: Shur, Natasha last_name: Shur - first_name: Lynn full_name: Chorich, Lynn last_name: Chorich - first_name: Lawrence full_name: Layman, Lawrence last_name: Layman - first_name: Thomas full_name: Haaf, Thomas last_name: Haaf - first_name: Ehsan full_name: Pourkarimi, Ehsan last_name: Pourkarimi - first_name: Hyung-Goo full_name: Kim, Hyung-Goo last_name: Kim - first_name: Gaurav K. full_name: Varshney, Gaurav K. last_name: Varshney citation: ama: Lin S-J, Vona B, Porter HM, et al. Biallelic variants in WARS1 cause a highly variable neurodevelopmental syndrome and implicate a critical exon for normal auditory function. Human Mutation. 2022;43(10):1472-1489. doi:10.1002/humu.24435 apa: Lin, S.-J., Vona, B., Porter, H. M., Izadi, M., Huang, K., Lacassie, Y., … Varshney, G. K. (2022). Biallelic variants in WARS1 cause a highly variable neurodevelopmental syndrome and implicate a critical exon for normal auditory function. Human Mutation. Wiley. https://doi.org/10.1002/humu.24435 chicago: Lin, Sheng-Jia, Barbara Vona, Hillary M. Porter, Mahmoud Izadi, Kevin Huang, Yves Lacassie, Jill A. Rosenfeld, et al. “Biallelic Variants in WARS1 Cause a Highly Variable Neurodevelopmental Syndrome and Implicate a Critical Exon for Normal Auditory Function.” Human Mutation. Wiley, 2022. https://doi.org/10.1002/humu.24435. ieee: S.-J. Lin et al., “Biallelic variants in WARS1 cause a highly variable neurodevelopmental syndrome and implicate a critical exon for normal auditory function,” Human Mutation, vol. 43, no. 10. Wiley, pp. 1472–1489, 2022. ista: Lin S-J, Vona B, Porter HM, Izadi M, Huang K, Lacassie Y, Rosenfeld JA, Khan S, Petree C, Ali TA, Muhammad N, Khan SA, Muhammad N, Liu P, Haymon M-L, Rueschendorf F, Kong I-K, Schnapp L, Shur N, Chorich L, Layman L, Haaf T, Pourkarimi E, Kim H-G, Varshney GK. 2022. Biallelic variants in WARS1 cause a highly variable neurodevelopmental syndrome and implicate a critical exon for normal auditory function. Human Mutation. 43(10), 1472–1489. mla: Lin, Sheng-Jia, et al. “Biallelic Variants in WARS1 Cause a Highly Variable Neurodevelopmental Syndrome and Implicate a Critical Exon for Normal Auditory Function.” Human Mutation, vol. 43, no. 10, Wiley, 2022, pp. 1472–89, doi:10.1002/humu.24435. short: S.-J. Lin, B. Vona, H.M. Porter, M. Izadi, K. Huang, Y. Lacassie, J.A. Rosenfeld, S. Khan, C. Petree, T.A. Ali, N. Muhammad, S.A. Khan, N. Muhammad, P. Liu, M.-L. Haymon, F. Rueschendorf, I.-K. Kong, L. Schnapp, N. Shur, L. Chorich, L. Layman, T. Haaf, E. Pourkarimi, H.-G. Kim, G.K. Varshney, Human Mutation 43 (2022) 1472–1489. date_created: 2023-09-20T20:58:24Z date_published: 2022-10-01T00:00:00Z date_updated: 2023-09-25T08:54:14Z day: '01' ddc: - '570' doi: 10.1002/humu.24435 extern: '1' file: - access_level: open_access checksum: 74b01d4e4084b2f64c30ed32b18ee928 content_type: application/pdf creator: dernst date_created: 2023-09-25T08:52:54Z date_updated: 2023-09-25T08:52:54Z file_id: '14370' file_name: 2022_HumanMutation_Lin.pdf file_size: 12131312 relation: main_file success: 1 file_date_updated: 2023-09-25T08:52:54Z has_accepted_license: '1' intvolume: ' 43' issue: '10' keyword: - autosomal recessive - biallelic variants - C - elegans - translation initiation sites - tryptophanyl-tRNA synthetase 1 (WARS1) - WHEP domain - zebrafish language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 1472-1489 publication: Human Mutation publication_identifier: issn: - 1059-7794 publication_status: published publisher: Wiley quality_controlled: '1' scopus_import: '1' status: public title: Biallelic variants in WARS1 cause a highly variable neurodevelopmental syndrome and implicate a critical exon for normal auditory function 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: 43 year: '2022' ... --- _id: '14357' abstract: - lang: eng text: 'Aminoacylation of transfer RNA (tRNA) is a key step in protein biosynthesis, carried out by highly specific aminoacyl-tRNA synthetases (ARSs). ARSs have been implicated in autosomal dominant and autosomal recessive human disorders. Autosomal dominant variants in tryptophanyl-tRNA synthetase 1 (WARS1) are known to cause distal hereditary motor neuropathy and Charcot-Marie-Tooth disease, but a recessively inherited phenotype is yet to be clearly defined. Seryl-tRNA synthetase 1 (SARS1) has rarely been implicated in an autosomal recessive developmental disorder. Here, we report five individuals with biallelic missense variants in WARS1 or SARS1, who presented with an overlapping phenotype of microcephaly, developmental delay, intellectual disability, and brain anomalies. Structural mapping showed that the SARS1 variant is located directly within the enzyme’s active site, most likely diminishing activity, while the WARS1 variant is located in the N-terminal domain. We further characterize the identified WARS1 variant by showing that it negatively impacts protein abundance and is unable to rescue the phenotype of a CRISPR/Cas9 wars1 knockout zebrafish model. In summary, we describe two overlapping autosomal recessive syndromes caused by variants in WARS1 and SARS1, present functional insights into the pathogenesis of the WARS1-related syndrome and define an emerging disease spectrum: ARS-related developmental disorders with or without microcephaly.' article_processing_charge: No article_type: original author: - first_name: Nina full_name: Boegershausen, Nina last_name: Boegershausen - first_name: Hannah E. full_name: Krawczyk, Hannah E. last_name: Krawczyk - first_name: Rami A. full_name: Jamra, Rami A. last_name: Jamra - first_name: Sheng-Jia full_name: Lin, Sheng-Jia last_name: Lin - first_name: Goekhan full_name: Yigit, Goekhan last_name: Yigit - first_name: Irina full_name: Huening, Irina last_name: Huening - first_name: Anna M. full_name: Polo, Anna M. last_name: Polo - first_name: Barbara full_name: Vona, Barbara last_name: Vona - first_name: Kevin full_name: Huang, Kevin id: 3b3d2888-1ff6-11ee-9fa6-8f209ca91fe3 last_name: Huang orcid: 0000-0002-2512-7812 - first_name: Julia full_name: Schmidt, Julia last_name: Schmidt - first_name: Janine full_name: Altmueller, Janine last_name: Altmueller - first_name: Johannes full_name: Luppe, Johannes last_name: Luppe - first_name: Konrad full_name: Platzer, Konrad last_name: Platzer - first_name: Beate B. full_name: Doergeloh, Beate B. last_name: Doergeloh - first_name: Andreas full_name: Busche, Andreas last_name: Busche - first_name: Saskia full_name: Biskup, Saskia last_name: Biskup - first_name: Marisa full_name: Mendes, I, Marisa last_name: Mendes, I - first_name: Desiree E. C. full_name: Smith, Desiree E. C. last_name: Smith - first_name: Gajja S. full_name: Salomons, Gajja S. last_name: Salomons - first_name: Arne full_name: Zibat, Arne last_name: Zibat - first_name: Eva full_name: Bueltmann, Eva last_name: Bueltmann - first_name: Peter full_name: Nuernberg, Peter last_name: Nuernberg - first_name: Malte full_name: Spielmann, Malte last_name: Spielmann - first_name: Johannes R. full_name: Lemke, Johannes R. last_name: Lemke - first_name: Yun full_name: Li, Yun last_name: Li - first_name: Martin full_name: Zenker, Martin last_name: Zenker - first_name: Gaurav K. full_name: Varshney, Gaurav K. last_name: Varshney - first_name: Hauke S. full_name: Hillen, Hauke S. last_name: Hillen - first_name: Christian P. full_name: Kratz, Christian P. last_name: Kratz - first_name: Bernd full_name: Wollnik, Bernd last_name: Wollnik citation: ama: 'Boegershausen N, Krawczyk HE, Jamra RA, et al. WARS1 and SARS1: Two tRNA synthetases implicated in autosomal recessive microcephaly. Human Mutation. 2022;43(10):1454-1471. doi:10.1002/humu.24430' apa: 'Boegershausen, N., Krawczyk, H. E., Jamra, R. A., Lin, S.-J., Yigit, G., Huening, I., … Wollnik, B. (2022). WARS1 and SARS1: Two tRNA synthetases implicated in autosomal recessive microcephaly. Human Mutation. Wiley. https://doi.org/10.1002/humu.24430' chicago: 'Boegershausen, Nina, Hannah E. Krawczyk, Rami A. Jamra, Sheng-Jia Lin, Goekhan Yigit, Irina Huening, Anna M. Polo, et al. “WARS1 and SARS1: Two TRNA Synthetases Implicated in Autosomal Recessive Microcephaly.” Human Mutation. Wiley, 2022. https://doi.org/10.1002/humu.24430.' ieee: 'N. Boegershausen et al., “WARS1 and SARS1: Two tRNA synthetases implicated in autosomal recessive microcephaly,” Human Mutation, vol. 43, no. 10. Wiley, pp. 1454–1471, 2022.' ista: 'Boegershausen N, Krawczyk HE, Jamra RA, Lin S-J, Yigit G, Huening I, Polo AM, Vona B, Huang K, Schmidt J, Altmueller J, Luppe J, Platzer K, Doergeloh BB, Busche A, Biskup S, Mendes, I M, Smith DEC, Salomons GS, Zibat A, Bueltmann E, Nuernberg P, Spielmann M, Lemke JR, Li Y, Zenker M, Varshney GK, Hillen HS, Kratz CP, Wollnik B. 2022. WARS1 and SARS1: Two tRNA synthetases implicated in autosomal recessive microcephaly. Human Mutation. 43(10), 1454–1471.' mla: 'Boegershausen, Nina, et al. “WARS1 and SARS1: Two TRNA Synthetases Implicated in Autosomal Recessive Microcephaly.” Human Mutation, vol. 43, no. 10, Wiley, 2022, pp. 1454–71, doi:10.1002/humu.24430.' short: N. Boegershausen, H.E. Krawczyk, R.A. Jamra, S.-J. Lin, G. Yigit, I. Huening, A.M. Polo, B. Vona, K. Huang, J. Schmidt, J. Altmueller, J. Luppe, K. Platzer, B.B. Doergeloh, A. Busche, S. Biskup, M. Mendes, I, D.E.C. Smith, G.S. Salomons, A. Zibat, E. Bueltmann, P. Nuernberg, M. Spielmann, J.R. Lemke, Y. Li, M. Zenker, G.K. Varshney, H.S. Hillen, C.P. Kratz, B. Wollnik, Human Mutation 43 (2022) 1454–1471. date_created: 2023-09-20T20:59:33Z date_published: 2022-10-01T00:00:00Z date_updated: 2023-09-25T08:43:06Z day: '01' ddc: - '570' doi: 10.1002/humu.24430 extern: '1' external_id: pmid: - '35790048' file: - access_level: open_access checksum: c31fc91e0445c35b9da83eb911a9b552 content_type: application/pdf creator: dernst date_created: 2023-09-25T08:41:23Z date_updated: 2023-09-25T08:41:23Z file_id: '14367' file_name: 2022_HumanMutation_Boegershausen.pdf file_size: 4863605 relation: main_file success: 1 file_date_updated: 2023-09-25T08:41:23Z has_accepted_license: '1' intvolume: ' 43' issue: '10' keyword: - aminoacylation - aminoacyl-tRNA synthetase - ARS - CRISPR - Cas9 - intellectual disability - microcephaly - SARS1 - tRNA - WARS1 - zebrafish language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 1454-1471 pmid: 1 publication: Human Mutation publication_identifier: issn: - 1059-7794 publication_status: published publisher: Wiley quality_controlled: '1' scopus_import: '1' status: public title: 'WARS1 and SARS1: Two tRNA synthetases implicated in autosomal recessive microcephaly' tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 43 year: '2022' ...