[{"publication_identifier":{"isbn":["9781713871088"]},"language":[{"iso":"eng"}],"year":"2022","arxiv":1,"intvolume":" 35","extern":"1","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.","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.","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.","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.","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.","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."},"quality_controlled":"1","conference":{"start_date":"2022-11-28","end_date":"2022-12-09","location":"New Orleans, LA, United States","name":"NeurIPS: Neural Information Processing Systems"},"oa":1,"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."}],"external_id":{"arxiv":["2204.04440"]},"alternative_title":["Advances in Neural Information Processing Systems"],"page":"16548-16562","day":"15","status":"public","title":"Are two heads the same as one? Identifying disparate treatment in fair neural networks","volume":35,"article_processing_charge":"No","_id":"14106","type":"conference","publication":"36th Conference on Neural Information Processing Systems","date_created":"2023-08-21T12:12:42Z","oa_version":"Preprint","department":[{"_id":"FrLo"}],"author":[{"full_name":"Lohaus, Michael","last_name":"Lohaus","first_name":"Michael"},{"full_name":"Kleindessner, Matthäus","first_name":"Matthäus","last_name":"Kleindessner"},{"full_name":"Kenthapadi, Krishnaram","last_name":"Kenthapadi","first_name":"Krishnaram"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"last_name":"Russell","first_name":"Chris","full_name":"Russell, Chris"}],"date_published":"2022-12-15T00:00:00Z","month":"12","publisher":"Neural Information Processing Systems Foundation","date_updated":"2023-09-06T10:29:42Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","main_file_link":[{"url":"https://arxiv.org/abs/2204.04440","open_access":"1"}],"scopus_import":"1"},{"year":"2022","day":"23","language":[{"iso":"eng"}],"title":"Self-supervised amodal video object segmentation","arxiv":1,"status":"public","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","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.","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."},"doi":"10.48550/arXiv.2210.12733","oa_version":"Preprint","date_created":"2023-08-21T12:13:25Z","_id":"14107","type":"conference","article_processing_charge":"No","extern":"1","publication":"36th Conference on Neural Information Processing Systems","author":[{"last_name":"Yao","first_name":"Jian","full_name":"Yao, Jian"},{"full_name":"Hong, Yuxin","last_name":"Hong","first_name":"Yuxin"},{"full_name":"Wang, Chiyu","last_name":"Wang","first_name":"Chiyu"},{"full_name":"Xiao, Tianjun","first_name":"Tianjun","last_name":"Xiao"},{"last_name":"He","first_name":"Tong","full_name":"He, Tong"},{"last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"first_name":"David","last_name":"Wipf","full_name":"Wipf, David"},{"full_name":"Fu, Yanwei","first_name":"Yanwei","last_name":"Fu"},{"full_name":"Zhang, Zheng","last_name":"Zhang","first_name":"Zheng"}],"conference":{"end_date":"2022-12-01","start_date":"2022-11-28","location":"New Orleans, LA, United States","name":"NeurIPS: Neural Information Processing Systems"},"department":[{"_id":"FrLo"}],"oa":1,"abstract":[{"text":"Amodal perception requires inferring the full shape of an object that is partially occluded. This task is particularly challenging on two levels: (1) it requires more information than what is contained in the instant retina or imaging sensor, (2) it is difficult to obtain enough well-annotated amodal labels for supervision. To this end, this paper develops a new framework of\r\nSelf-supervised amodal Video object segmentation (SaVos). Our method efficiently leverages the visual information of video temporal sequences to infer the amodal mask of objects. The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.\r\nAccordingly, we derive a novel self-supervised learning paradigm that efficiently utilizes the visible object parts as the supervision to guide the training on videos. In addition to learning type prior to complete masks for known types, SaVos also learns the spatiotemporal prior, which is also useful for the amodal task and could generalize to unseen types. The proposed\r\nframework achieves the state-of-the-art performance on the synthetic amodal segmentation benchmark FISHBOWL and the real world benchmark KINS-Video-Car. Further, it lends itself well to being transferred to novel distributions using test-time adaptation, outperforming existing models even after the transfer to a new distribution.","lang":"eng"}],"month":"10","date_published":"2022-10-23T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","date_updated":"2023-09-11T09:34:17Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2210.12733"}],"external_id":{"arxiv":["2210.12733"]}},{"status":"public","title":"Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers","day":"01","department":[{"_id":"FrLo"}],"author":[{"full_name":"Zietlow, Dominik","last_name":"Zietlow","first_name":"Dominik"},{"last_name":"Lohaus","first_name":"Michael","full_name":"Lohaus, Michael"},{"first_name":"Guha","last_name":"Balakrishnan","full_name":"Balakrishnan, Guha"},{"full_name":"Kleindessner, Matthaus","last_name":"Kleindessner","first_name":"Matthaus"},{"orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello"},{"full_name":"Scholkopf, Bernhard","last_name":"Scholkopf","first_name":"Bernhard"},{"last_name":"Russell","first_name":"Chris","full_name":"Russell, Chris"}],"article_processing_charge":"No","_id":"14114","type":"conference","publication":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition","date_created":"2023-08-21T12:18:00Z","oa_version":"Preprint","publisher":"Institute of Electrical and Electronics Engineers","date_updated":"2023-09-11T09:19:14Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","date_published":"2022-07-01T00:00:00Z","month":"07","scopus_import":"1","main_file_link":[{"url":"https://arxiv.org/abs/2203.04913","open_access":"1"}],"arxiv":1,"publication_identifier":{"eissn":["2575-7075"],"isbn":["9781665469470"],"issn":["1063-6919"]},"language":[{"iso":"eng"}],"year":"2022","conference":{"name":"CVPR: Conference on Computer Vision and Pattern Recognition","start_date":"2022-06-18","end_date":"2022-06-24","location":"New Orleans, LA, United States"},"extern":"1","doi":"10.1109/cvpr52688.2022.01016","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","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.","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.","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","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.","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."},"quality_controlled":"1","oa":1,"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."}],"external_id":{"arxiv":["2203.04913"]},"page":"10400-10411"},{"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2210.08031"}],"alternative_title":[" Advances in Neural Information Processing Systems"],"external_id":{"arxiv":["2210.08031"]},"oa":1,"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."}],"month":"10","date_published":"2022-10-14T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","date_updated":"2023-09-11T09:29:09Z","citation":{"ama":"Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: 36th Conference on Neural Information Processing Systems. Vol 35. ; 2022.","mla":"Rahaman, Nasim, et al. “Neural Attentive Circuits.” 36th Conference on Neural Information Processing Systems, vol. 35, 2022.","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.","ieee":"N. Rahaman et al., “Neural attentive circuits,” in 36th Conference on Neural Information Processing Systems, New Orleans, United States, 2022, vol. 35.","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.","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.","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."},"oa_version":"Preprint","date_created":"2023-08-22T13:57:27Z","_id":"14168","type":"conference","article_processing_charge":"No","publication":"36th Conference on Neural Information Processing Systems","extern":"1","author":[{"full_name":"Rahaman, Nasim","first_name":"Nasim","last_name":"Rahaman"},{"full_name":"Weiss, Martin","first_name":"Martin","last_name":"Weiss"},{"last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"first_name":"Chris","last_name":"Pal","full_name":"Pal, Chris"},{"first_name":"Yoshua","last_name":"Bengio","full_name":"Bengio, Yoshua"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"last_name":"Li","first_name":"Li Erran","full_name":"Li, Li Erran"},{"full_name":"Ballas, Nicolas","last_name":"Ballas","first_name":"Nicolas"}],"department":[{"_id":"FrLo"}],"conference":{"name":"NeurIPS: Neural Information Processing Systems","location":"New Orleans, United States","end_date":"2022-12-01","start_date":"2022-11-29"},"day":"14","year":"2022","language":[{"iso":"eng"}],"volume":35,"title":"Neural attentive circuits","arxiv":1,"intvolume":" 35","status":"public"},{"oa":1,"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. "}],"external_id":{"arxiv":["2107.00637"]},"page":"5221-5285","alternative_title":["PMLR"],"language":[{"iso":"eng"}],"year":"2022","arxiv":1,"intvolume":" 2022","extern":"1","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.","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.","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."},"quality_controlled":"1","conference":{"name":"International Conference on Machine Learning","end_date":"2022-07-23","start_date":"2022-07-17","location":"Baltimore, MD, United States"},"date_published":"2022-07-22T00:00:00Z","month":"07","publisher":"ML Research Press","date_updated":"2023-09-11T10:08:14Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"submitted","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2107.00637"}],"day":"22","status":"public","title":"Generalization and robustness implications in object-centric learning","volume":2022,"_id":"14170","type":"conference","article_processing_charge":"No","publication":"Proceedings of the 39th International Conference on Machine Learning","date_created":"2023-08-22T13:59:55Z","oa_version":"Preprint","department":[{"_id":"FrLo"}],"author":[{"last_name":"Dittadi","first_name":"Andrea","full_name":"Dittadi, Andrea"},{"full_name":"Papa, Samuele","first_name":"Samuele","last_name":"Papa"},{"last_name":"Vita","first_name":"Michele De","full_name":"Vita, Michele De"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"full_name":"Winther, Ole","last_name":"Winther","first_name":"Ole"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello"}]},{"day":"22","status":"public","volume":162,"title":"Score matching enables causal discovery of nonlinear additive noise models","publication":"Proceedings of the 39th International Conference on Machine Learning","type":"conference","_id":"14171","article_processing_charge":"No","oa_version":"Preprint","date_created":"2023-08-22T14:00:18Z","department":[{"_id":"FrLo"}],"author":[{"last_name":"Rolland","first_name":"Paul","full_name":"Rolland, Paul"},{"first_name":"Volkan","last_name":"Cevher","full_name":"Cevher, Volkan"},{"full_name":"Kleindessner, Matthäus","first_name":"Matthäus","last_name":"Kleindessner"},{"full_name":"Russel, Chris","last_name":"Russel","first_name":"Chris"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"},{"first_name":"Dominik","last_name":"Janzing","full_name":"Janzing, Dominik"},{"orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello"}],"date_published":"2022-07-22T00:00:00Z","month":"07","date_updated":"2023-09-11T10:14:20Z","publisher":"ML Research Press","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2203.04413"}],"language":[{"iso":"eng"}],"year":"2022","intvolume":" 162","arxiv":1,"extern":"1","quality_controlled":"1","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.","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.","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.","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.","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.","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":{"name":"International Conference on Machine Learning","end_date":"2022-07-23","start_date":"2022-07-17","location":"Baltimore, MD, United States"},"abstract":[{"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.","lang":"eng"}],"oa":1,"external_id":{"arxiv":["2203.04413"]},"page":"18741-18753","alternative_title":["PMLR"]},{"status":"public","arxiv":1,"title":"Visual representation learning does not generalize strongly within the same domain","language":[{"iso":"eng"}],"day":"25","year":"2022","conference":{"location":"Virtual","end_date":"2022-04-29","start_date":"2022-04-25","name":"ICLR: International Conference on Learning Representations"},"department":[{"_id":"FrLo"}],"author":[{"first_name":"Lukas","last_name":"Schott","full_name":"Schott, Lukas"},{"full_name":"Kügelgen, Julius von","first_name":"Julius von","last_name":"Kügelgen"},{"full_name":"Träuble, Frederik","last_name":"Träuble","first_name":"Frederik"},{"full_name":"Gehler, Peter","first_name":"Peter","last_name":"Gehler"},{"full_name":"Russell, Chris","last_name":"Russell","first_name":"Chris"},{"last_name":"Bethge","first_name":"Matthias","full_name":"Bethge, Matthias"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"},{"last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"full_name":"Brendel, Wieland","first_name":"Wieland","last_name":"Brendel"}],"publication":"10th International Conference on Learning Representations","extern":"1","_id":"14172","article_processing_charge":"No","type":"conference","date_created":"2023-08-22T14:00:50Z","oa_version":"Preprint","quality_controlled":"1","citation":{"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.","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.","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.","mla":"Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly within the Same Domain.” 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.","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."},"date_updated":"2023-09-11T09:40:52Z","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-04-25T00:00:00Z","month":"04","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."}],"oa":1,"external_id":{"arxiv":["2107.08221"]},"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2107.08221","open_access":"1"}]},{"department":[{"_id":"FrLo"}],"author":[{"first_name":"Florian","last_name":"Wenzel","full_name":"Wenzel, Florian"},{"first_name":"Andrea","last_name":"Dittadi","full_name":"Dittadi, Andrea"},{"full_name":"Gehler, Peter Vincent","last_name":"Gehler","first_name":"Peter Vincent"},{"first_name":"Carl-Johann Simon-Gabriel","last_name":"Carl-Johann Simon-Gabriel","full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"last_name":"Zietlow","first_name":"Dominik","full_name":"Zietlow, Dominik"},{"first_name":"David","last_name":"Kernert","full_name":"Kernert, David"},{"full_name":"Russell, Chris","last_name":"Russell","first_name":"Chris"},{"first_name":"Thomas","last_name":"Brox","full_name":"Brox, Thomas"},{"full_name":"Schiele, Bernt","first_name":"Bernt","last_name":"Schiele"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"},{"orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"article_processing_charge":"No","_id":"14173","type":"conference","publication":"36th Conference on Neural Information Processing Systems","oa_version":"Preprint","date_created":"2023-08-22T14:01:13Z","status":"public","title":"Assaying out-of-distribution generalization in transfer learning","volume":35,"day":"15","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2207.09239"}],"publisher":"Neural Information Processing Systems Foundation","date_updated":"2023-09-06T10:34:43Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","month":"12","date_published":"2022-12-15T00:00:00Z","conference":{"start_date":"2022-11-28","end_date":"2022-12-09","location":"New Orleans, LA, United States","name":"NeurIPS: Neural Information Processing Systems"},"extern":"1","citation":{"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.","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.","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.","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."},"quality_controlled":"1","arxiv":1,"intvolume":" 35","publication_identifier":{"isbn":["9781713871088"]},"language":[{"iso":"eng"}],"year":"2022","external_id":{"arxiv":["2207.09239"]},"page":"7181-7198","alternative_title":["Advances in Neural Information Processing Systems"],"oa":1,"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."}]},{"type":"conference","_id":"14174","article_processing_charge":"No","extern":"1","publication":"10th International Conference on Learning Representations","citation":{"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.","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.","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.","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.","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.","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.","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."},"quality_controlled":"1","date_created":"2023-08-22T14:02:13Z","oa_version":"Preprint","department":[{"_id":"FrLo"}],"conference":{"name":"ICLR: International Conference on Learning Representations","end_date":"2022-04-29","start_date":"2022-04-25","location":"Virtual"},"author":[{"first_name":"Andrea","last_name":"Dittadi","full_name":"Dittadi, Andrea"},{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"first_name":"Manuel","last_name":"Wüthrich","full_name":"Wüthrich, Manuel"},{"last_name":"Widmaier","first_name":"Felix","full_name":"Widmaier, Felix"},{"last_name":"Gehler","first_name":"Peter","full_name":"Gehler, Peter"},{"full_name":"Winther, Ole","first_name":"Ole","last_name":"Winther"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello"},{"first_name":"Olivier","last_name":"Bachem","full_name":"Bachem, Olivier"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"},{"last_name":"Bauer","first_name":"Stefan","full_name":"Bauer, Stefan"}],"language":[{"iso":"eng"}],"day":"25","year":"2022","arxiv":1,"status":"public","title":"The role of pretrained representations for the OOD generalization of reinforcement learning agents","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2107.05686","open_access":"1"}],"external_id":{"arxiv":["2107.05686"]},"month":"04","date_published":"2022-04-25T00:00:00Z","oa":1,"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."}],"date_updated":"2023-09-11T09:48:36Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published"},{"language":[{"iso":"eng"}],"day":"25","year":"2022","arxiv":1,"status":"public","title":"You mostly walk alone: Analyzing feature attribution in trajectory prediction","_id":"14175","type":"conference","article_processing_charge":"No","publication":"10th International Conference on Learning Representations","extern":"1","citation":{"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.","short":"O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox, B. Schölkopf, 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.","mla":"Makansi, Osama, et al. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” 10th International Conference on Learning Representations, 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.","ieee":"O. Makansi et al., “You mostly walk alone: Analyzing feature attribution in trajectory prediction,” in 10th International Conference on Learning Representations, Virtual, 2022.","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."},"oa_version":"Preprint","date_created":"2023-08-22T14:02:34Z","quality_controlled":"1","department":[{"_id":"FrLo"}],"conference":{"name":"ICLR: International Conference on Learning Representations","location":"Virtual","end_date":"2022-04-29","start_date":"2022-04-25"},"author":[{"last_name":"Makansi","first_name":"Osama","full_name":"Makansi, Osama"},{"full_name":"Kügelgen, Julius von","last_name":"Kügelgen","first_name":"Julius von"},{"last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"full_name":"Gehler, Peter","first_name":"Peter","last_name":"Gehler"},{"full_name":"Janzing, Dominik","first_name":"Dominik","last_name":"Janzing"},{"last_name":"Brox","first_name":"Thomas","full_name":"Brox, Thomas"},{"full_name":"Schölkopf, Bernhard","first_name":"Bernhard","last_name":"Schölkopf"}],"month":"04","date_published":"2022-04-25T00:00:00Z","oa":1,"abstract":[{"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.","lang":"eng"}],"date_updated":"2023-09-11T09:52:20Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2110.05304","open_access":"1"}],"external_id":{"arxiv":["2110.05304"]}},{"abstract":[{"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.","lang":"eng"}],"oa":1,"date_published":"2022-11-04T00:00:00Z","month":"11","publication_status":"submitted","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-09-13T09:35:59Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2211.02348","open_access":"1"}],"external_id":{"arxiv":["2211.02348"]},"day":"04","year":"2022","language":[{"iso":"eng"}],"title":"A general purpose neural architecture for geospatial systems","status":"public","arxiv":1,"quality_controlled":"1","date_created":"2023-08-22T14:21:47Z","oa_version":"Preprint","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.","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.","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.","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.","mla":"Rahaman, Nasim, et al. “A General Purpose Neural Architecture for Geospatial Systems.” 36th Conference on Neural Information Processing Systems.","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."},"publication":"36th Conference on Neural Information Processing Systems","extern":"1","_id":"14215","article_processing_charge":"No","type":"conference","author":[{"full_name":"Rahaman, Nasim","last_name":"Rahaman","first_name":"Nasim"},{"first_name":"Martin","last_name":"Weiss","full_name":"Weiss, Martin"},{"first_name":"Frederik","last_name":"Träuble","full_name":"Träuble, Frederik"},{"last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco"},{"full_name":"Lacoste, Alexandre","first_name":"Alexandre","last_name":"Lacoste"},{"last_name":"Bengio","first_name":"Yoshua","full_name":"Bengio, Yoshua"},{"full_name":"Pal, Chris","last_name":"Pal","first_name":"Chris"},{"full_name":"Li, Li Erran","last_name":"Li","first_name":"Li Erran"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"}],"department":[{"_id":"FrLo"}],"conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2022-12-09","start_date":"2022-11-28","location":"New Orleans, LA, United States"}},{"title":"ASIF: Coupled data turns unimodal models to multimodal without training","article_number":"2210.01738","arxiv":1,"status":"public","year":"2022","day":"04","language":[{"iso":"eng"}],"author":[{"full_name":"Norelli, Antonio","last_name":"Norelli","first_name":"Antonio"},{"full_name":"Fumero, Marco","last_name":"Fumero","first_name":"Marco"},{"first_name":"Valentino","last_name":"Maiorca","full_name":"Maiorca, Valentino"},{"full_name":"Moschella, Luca","last_name":"Moschella","first_name":"Luca"},{"last_name":"Rodolà","first_name":"Emanuele","full_name":"Rodolà, Emanuele"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello"}],"department":[{"_id":"FrLo"}],"doi":"10.48550/arXiv.2210.01738","citation":{"ama":"Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF: Coupled data turns unimodal models to multimodal without training. arXiv. doi:10.48550/arXiv.2210.01738","ieee":"A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, and F. Locatello, “ASIF: Coupled data turns unimodal models to multimodal without training,” arXiv. .","ista":"Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF: Coupled data turns unimodal models to multimodal without training. arXiv, 2210.01738.","mla":"Norelli, Antonio, et al. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.” ArXiv, 2210.01738, doi:10.48550/arXiv.2210.01738.","apa":"Norelli, A., Fumero, M., Maiorca, V., Moschella, L., Rodolà, E., & Locatello, F. (n.d.). ASIF: Coupled data turns unimodal models to multimodal without training. arXiv. https://doi.org/10.48550/arXiv.2210.01738","chicago":"Norelli, Antonio, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, and Francesco Locatello. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2210.01738.","short":"A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, F. Locatello, ArXiv (n.d.)."},"date_created":"2023-08-22T14:22:04Z","oa_version":"Preprint","article_processing_charge":"No","_id":"14216","type":"preprint","publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"submitted","date_updated":"2024-02-12T09:57:14Z","oa":1,"abstract":[{"lang":"eng","text":"CLIP proved that aligning visual and language spaces is key to solving many vision tasks without explicit training, but required to train image and text encoders from scratch on a huge dataset. LiT improved this by only training the text encoder and using a pre-trained vision network. In this paper, we show that a common space can be created without any training at all, using single-domain encoders (trained with or without supervision) and a much smaller amount of image-text pairs. Furthermore, our model has unique properties. Most notably, deploying a new version with updated training samples can be done in a matter of seconds. Additionally, the representations in the common space are easily interpretable as every dimension corresponds to the similarity of the input to a unique entry in the multimodal dataset. Experiments on standard zero-shot visual benchmarks demonstrate the typical transfer ability of image-text models. Overall, our method represents a simple yet surprisingly strong baseline for foundation multi-modal models, raising important questions on their data efficiency and on the role of retrieval in machine learning."}],"month":"10","date_published":"2022-10-04T00:00:00Z","external_id":{"arxiv":["2210.01738"]},"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2210.01738","open_access":"1"}]},{"title":"Compositional multi-object reinforcement learning with linear relation networks","status":"public","arxiv":1,"article_number":"2201.13388","year":"2022","day":"31","language":[{"iso":"eng"}],"author":[{"first_name":"Davide","last_name":"Mambelli","full_name":"Mambelli, Davide"},{"full_name":"Träuble, Frederik","first_name":"Frederik","last_name":"Träuble"},{"first_name":"Stefan","last_name":"Bauer","full_name":"Bauer, Stefan"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello"}],"department":[{"_id":"FrLo"}],"oa_version":"Preprint","date_created":"2023-08-22T14:23:16Z","doi":"10.48550/arXiv.2201.13388","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","mla":"Mambelli, Davide, et al. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” ArXiv, 2201.13388, doi:10.48550/arXiv.2201.13388.","ista":"Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. 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. .","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.","short":"D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.).","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"},"publication":"arXiv","extern":"1","type":"preprint","_id":"14220","article_processing_charge":"No","publication_status":"submitted","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-09-11T11:49:40Z","abstract":[{"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.","lang":"eng"}],"oa":1,"month":"01","date_published":"2022-01-31T00:00:00Z","external_id":{"arxiv":["2201.13388"]},"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2201.13388","open_access":"1"}]},{"external_id":{"arxiv":["2211.09606"]},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2211.09606"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"submitted","date_updated":"2023-09-04T08:31:19Z","oa":1,"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."}],"date_published":"2022-11-17T00:00:00Z","month":"11","author":[{"full_name":"Goranci, Gramoz","last_name":"Goranci","first_name":"Gramoz"},{"full_name":"Henzinger, Monika H","orcid":"0000-0002-5008-6530","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","first_name":"Monika H","last_name":"Henzinger"}],"doi":"10.48550/arXiv.2211.09606","citation":{"ama":"Goranci G, Henzinger MH. Incremental approximate maximum flow in m1/2+o(1) update time. arXiv. doi:10.48550/arXiv.2211.09606","ista":"Goranci G, Henzinger MH. Incremental approximate maximum flow in m1/2+o(1) update time. arXiv, 2211.09606.","ieee":"G. Goranci and M. H. Henzinger, “Incremental approximate maximum flow in m1/2+o(1) update time,” arXiv. .","mla":"Goranci, Gramoz, and Monika H. Henzinger. “Incremental Approximate Maximum Flow in M1/2+o(1) Update Time.” ArXiv, 2211.09606, doi:10.48550/arXiv.2211.09606.","apa":"Goranci, G., & Henzinger, M. H. (n.d.). Incremental approximate maximum flow in m1/2+o(1) update time. arXiv. https://doi.org/10.48550/arXiv.2211.09606","short":"G. Goranci, M.H. Henzinger, ArXiv (n.d.).","chicago":"Goranci, Gramoz, and Monika H Henzinger. “Incremental Approximate Maximum Flow in M1/2+o(1) Update Time.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2211.09606."},"date_created":"2023-08-25T15:04:29Z","oa_version":"Preprint","_id":"14236","type":"preprint","article_processing_charge":"No","publication":"arXiv","extern":"1","title":"Incremental approximate maximum flow in m1/2+o(1) update time","article_number":"2211.09606","status":"public","arxiv":1,"year":"2022","day":"17","language":[{"iso":"eng"}]},{"main_file_link":[{"url":"https://journals.calstate.edu/pump/article/view/2711","open_access":"1"}],"date_updated":"2023-09-05T09:28:32Z","publisher":"California State University","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_type":"original","date_published":"2022-02-13T00:00:00Z","month":"02","author":[{"last_name":"Holmes","first_name":"Daniel","id":"3a443b4c-080d-11ed-979a-feb062bdcee0","full_name":"Holmes, Daniel"}],"publication":"PUMP Journal of Undergraduate Research","_id":"14248","article_processing_charge":"No","type":"journal_article","date_created":"2023-08-29T13:08:09Z","oa_version":"Published Version","status":"public","title":"Affine dimers from characteristic polygons","volume":5,"day":"13","external_id":{"arxiv":["2110.01703"]},"page":"24-51","abstract":[{"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.","lang":"eng"}],"keyword":["dimer model","hyperplane arrangement","torus","lattice polygon"],"oa":1,"extern":"1","quality_controlled":"1","citation":{"mla":"Holmes, Daniel. “Affine Dimers from Characteristic Polygons.” PUMP Journal of Undergraduate Research, vol. 5, California State University, 2022, pp. 24–51.","ista":"Holmes D. 2022. Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. 5, 24–51.","ieee":"D. Holmes, “Affine dimers from characteristic polygons,” PUMP Journal of Undergraduate Research, vol. 5. California State University, pp. 24–51, 2022.","chicago":"Holmes, Daniel. “Affine Dimers from Characteristic Polygons.” PUMP Journal of Undergraduate Research. California State University, 2022.","short":"D. Holmes, PUMP Journal of Undergraduate Research 5 (2022) 24–51.","apa":"Holmes, D. (2022). Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. California State University.","ama":"Holmes D. Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. 2022;5:24-51."},"arxiv":1,"intvolume":" 5","publication_identifier":{"issn":["2576-3725"]},"language":[{"iso":"eng"}],"year":"2022"},{"scopus_import":"1","article_type":"original","month":"10","date_published":"2022-10-01T00:00:00Z","date_updated":"2023-09-25T08:57:07Z","publisher":"Elsevier","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Genetics in Medicine","file_date_updated":"2023-09-25T08:56:06Z","_id":"14355","issue":"10","article_processing_charge":"No","type":"journal_article","date_created":"2023-09-20T20:57:18Z","oa_version":"Published Version","department":[{"_id":"GradSch"}],"author":[{"full_name":"Cali, Elisa","first_name":"Elisa","last_name":"Cali"},{"first_name":"Sheng-Jia","last_name":"Lin","full_name":"Lin, Sheng-Jia"},{"full_name":"Rocca, Clarissa","first_name":"Clarissa","last_name":"Rocca"},{"last_name":"Sahin","first_name":"Yavuz","full_name":"Sahin, Yavuz"},{"first_name":"Aisha","last_name":"Al Shamsi","full_name":"Al Shamsi, Aisha"},{"full_name":"El Chehadeh, Salima","first_name":"Salima","last_name":"El Chehadeh"},{"full_name":"Chaabouni, Myriam","last_name":"Chaabouni","first_name":"Myriam"},{"full_name":"Mankad, Kshitij","last_name":"Mankad","first_name":"Kshitij"},{"full_name":"Galanaki, Evangelia","first_name":"Evangelia","last_name":"Galanaki"},{"first_name":"Stephanie","last_name":"Efthymiou","full_name":"Efthymiou, Stephanie"},{"full_name":"Sudhakar, Sniya","first_name":"Sniya","last_name":"Sudhakar"},{"last_name":"Athanasiou-Fragkouli","first_name":"Alkyoni","full_name":"Athanasiou-Fragkouli, Alkyoni"},{"full_name":"Celik, Tamer","last_name":"Celik","first_name":"Tamer"},{"last_name":"Narli","first_name":"Nejat","full_name":"Narli, Nejat"},{"full_name":"Bianca, Sebastiano","first_name":"Sebastiano","last_name":"Bianca"},{"full_name":"Murphy, David","last_name":"Murphy","first_name":"David"},{"full_name":"Moreira, Francisco Martins De Carvalho","first_name":"Francisco Martins De Carvalho","last_name":"Moreira"},{"full_name":"Accogli, Andrea","first_name":"Andrea","last_name":"Accogli"},{"first_name":"Cassidy","last_name":"Petree","full_name":"Petree, Cassidy"},{"id":"3b3d2888-1ff6-11ee-9fa6-8f209ca91fe3","first_name":"Kevin","last_name":"Huang","full_name":"Huang, Kevin","orcid":"0000-0002-2512-7812"},{"last_name":"Monastiri","first_name":"Kamel","full_name":"Monastiri, Kamel"},{"first_name":"Masoud","last_name":"Edizadeh","full_name":"Edizadeh, Masoud"},{"full_name":"Nardello, Rosaria","last_name":"Nardello","first_name":"Rosaria"},{"first_name":"Marzia","last_name":"Ognibene","full_name":"Ognibene, Marzia"},{"full_name":"De Marco, Patrizia","first_name":"Patrizia","last_name":"De Marco"},{"first_name":"Martino","last_name":"Ruggieri","full_name":"Ruggieri, Martino"},{"full_name":"Zara, Federico","first_name":"Federico","last_name":"Zara"},{"last_name":"Striano","first_name":"Pasquale","full_name":"Striano, Pasquale"},{"full_name":"Sahin, Yavuz","last_name":"Sahin","first_name":"Yavuz"},{"first_name":"Lihadh","last_name":"Al-Gazali","full_name":"Al-Gazali, Lihadh"},{"full_name":"Warde, Marie Therese Abi","last_name":"Warde","first_name":"Marie Therese Abi"},{"first_name":"Benedicte","last_name":"Gerard","full_name":"Gerard, Benedicte"},{"first_name":"Giovanni","last_name":"Zifarelli","full_name":"Zifarelli, Giovanni"},{"last_name":"Beetz","first_name":"Christian","full_name":"Beetz, Christian"},{"full_name":"Fortuna, Sara","first_name":"Sara","last_name":"Fortuna"},{"first_name":"Miguel","last_name":"Soler","full_name":"Soler, Miguel"},{"last_name":"Valente","first_name":"Enza Maria","full_name":"Valente, Enza Maria"},{"full_name":"Varshney, Gaurav","last_name":"Varshney","first_name":"Gaurav"},{"full_name":"Maroofian, Reza","last_name":"Maroofian","first_name":"Reza"},{"full_name":"Salpietro, Vincenzo","first_name":"Vincenzo","last_name":"Salpietro"},{"last_name":"Houlden","first_name":"Henry","full_name":"Houlden, Henry"},{"full_name":"Grp, SYNaPS Study","first_name":"SYNaPS Study","last_name":"Grp"}],"day":"01","status":"public","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"title":"A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease","volume":24,"page":"2194-2203","file":[{"creator":"dernst","file_size":1434037,"checksum":"8117175a89129eb5022d81ffe7625f9f","date_updated":"2023-09-25T08:56:06Z","access_level":"open_access","date_created":"2023-09-25T08:56:06Z","file_id":"14371","file_name":"2022_GeneticsMedicine_Calin.pdf","relation":"main_file","content_type":"application/pdf","success":1}],"keyword":["Human mediator complex","MED11","MEDopathies"],"abstract":[{"text":"Purpose: The mediator (MED) multisubunit-complex modulates the activity of the transcriptional machinery, and genetic defects in different MED subunits (17, 20, 27) have been implicated in neurologic diseases. In this study, we identified a recurrent homozygous variant in MED11 (c.325C>T; p.Arg109Ter) in 7 affected individuals from 5 unrelated families. Methods: To investigate the genetic cause of the disease, exome or genome sequencing were performed in 5 unrelated families identified via different research networks and Matchmaker Exchange. Deep clinical and brain imaging evaluations were performed by clinical pediatric neurologists and neuroradiologists. The functional effect of the candidate variant on both MED11 RNA and protein was assessed using reverse transcriptase polymerase chain reaction and western blotting using fibroblast cell lines derived from 1 affected individual and controls and through computational approaches. Knockouts in zebrafish were generated using clustered regularly interspaced short palindromic repeats/Cas9. Results: The disease was characterized by microcephaly, profound neurodevelopmental impairment, exaggerated startle response, myoclonic seizures, progressive widespread neurodegeneration, and premature death. Functional studies on patient-derived fibroblasts did not show a loss of protein function but rather disruption of the C-terminal of MED11, likely impairing binding to other MED subunits. A zebrafish knockout model recapitulates key clinical phenotypes. Conclusion: Loss of the C-terminal of MED subunit 11 may affect its binding efficiency to other MED subunits, thus implicating the MED-complex stability in brain development and neurodegeneration. (C) 2022 The Authors. Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics.","lang":"eng"}],"oa":1,"ddc":["570"],"extern":"1","quality_controlled":"1","doi":"10.1016/j.gim.2022.07.013","citation":{"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.","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.","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.","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.","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.","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"},"language":[{"iso":"eng"}],"publication_identifier":{"issn":["1098-3600"]},"year":"2022","intvolume":" 24","has_accepted_license":"1"},{"page":"1472-1489","file":[{"creator":"dernst","file_size":12131312,"checksum":"74b01d4e4084b2f64c30ed32b18ee928","date_updated":"2023-09-25T08:52:54Z","access_level":"open_access","date_created":"2023-09-25T08:52:54Z","file_id":"14370","relation":"main_file","file_name":"2022_HumanMutation_Lin.pdf","success":1,"content_type":"application/pdf"}],"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."}],"keyword":["autosomal recessive","biallelic variants","C","elegans","translation initiation sites","tryptophanyl-tRNA synthetase 1 (WARS1)","WHEP domain","zebrafish"],"oa":1,"ddc":["570"],"extern":"1","quality_controlled":"1","doi":"10.1002/humu.24435","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","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.","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.","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.","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","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.","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."},"intvolume":" 43","has_accepted_license":"1","language":[{"iso":"eng"}],"publication_identifier":{"issn":["1059-7794"]},"year":"2022","scopus_import":"1","date_updated":"2023-09-25T08:54:14Z","publisher":"Wiley","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_type":"original","month":"10","date_published":"2022-10-01T00:00:00Z","author":[{"full_name":"Lin, Sheng-Jia","last_name":"Lin","first_name":"Sheng-Jia"},{"full_name":"Vona, Barbara","first_name":"Barbara","last_name":"Vona"},{"full_name":"Porter, Hillary M.","first_name":"Hillary M.","last_name":"Porter"},{"last_name":"Izadi","first_name":"Mahmoud","full_name":"Izadi, Mahmoud"},{"last_name":"Huang","first_name":"Kevin","id":"3b3d2888-1ff6-11ee-9fa6-8f209ca91fe3","orcid":"0000-0002-2512-7812","full_name":"Huang, Kevin"},{"first_name":"Yves","last_name":"Lacassie","full_name":"Lacassie, Yves"},{"last_name":"Rosenfeld","first_name":"Jill A.","full_name":"Rosenfeld, Jill A."},{"first_name":"Saadullah","last_name":"Khan","full_name":"Khan, Saadullah"},{"last_name":"Petree","first_name":"Cassidy","full_name":"Petree, Cassidy"},{"last_name":"Ali","first_name":"Tayyiba A.","full_name":"Ali, Tayyiba A."},{"last_name":"Muhammad","first_name":"Nazif","full_name":"Muhammad, Nazif"},{"first_name":"Sher A.","last_name":"Khan","full_name":"Khan, Sher A."},{"full_name":"Muhammad, Noor","first_name":"Noor","last_name":"Muhammad"},{"last_name":"Liu","first_name":"Pengfei","full_name":"Liu, Pengfei"},{"full_name":"Haymon, Marie-Louise","last_name":"Haymon","first_name":"Marie-Louise"},{"last_name":"Rueschendorf","first_name":"Franz","full_name":"Rueschendorf, Franz"},{"full_name":"Kong, Il-Keun","first_name":"Il-Keun","last_name":"Kong"},{"last_name":"Schnapp","first_name":"Linda","full_name":"Schnapp, Linda"},{"last_name":"Shur","first_name":"Natasha","full_name":"Shur, Natasha"},{"first_name":"Lynn","last_name":"Chorich","full_name":"Chorich, Lynn"},{"last_name":"Layman","first_name":"Lawrence","full_name":"Layman, Lawrence"},{"first_name":"Thomas","last_name":"Haaf","full_name":"Haaf, Thomas"},{"last_name":"Pourkarimi","first_name":"Ehsan","full_name":"Pourkarimi, Ehsan"},{"full_name":"Kim, Hyung-Goo","last_name":"Kim","first_name":"Hyung-Goo"},{"full_name":"Varshney, Gaurav K.","last_name":"Varshney","first_name":"Gaurav K."}],"file_date_updated":"2023-09-25T08:52:54Z","publication":"Human Mutation","_id":"14356","article_processing_charge":"No","issue":"10","type":"journal_article","oa_version":"Published Version","date_created":"2023-09-20T20:58:24Z","status":"public","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"volume":43,"title":"Biallelic variants in WARS1 cause a highly variable neurodevelopmental syndrome and implicate a critical exon for normal auditory function","day":"01"},{"abstract":[{"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.","lang":"eng"}],"keyword":["aminoacylation","aminoacyl-tRNA synthetase","ARS","CRISPR","Cas9","intellectual disability","microcephaly","SARS1","tRNA","WARS1","zebrafish"],"oa":1,"ddc":["570"],"file":[{"creator":"dernst","file_size":4863605,"checksum":"c31fc91e0445c35b9da83eb911a9b552","date_updated":"2023-09-25T08:41:23Z","access_level":"open_access","date_created":"2023-09-25T08:41:23Z","file_id":"14367","file_name":"2022_HumanMutation_Boegershausen.pdf","relation":"main_file","content_type":"application/pdf","success":1}],"page":"1454-1471","external_id":{"pmid":["35790048"]},"has_accepted_license":"1","intvolume":" 43","pmid":1,"year":"2022","publication_identifier":{"issn":["1059-7794"]},"language":[{"iso":"eng"}],"quality_controlled":"1","citation":{"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.","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.","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.","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.","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","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"},"doi":"10.1002/humu.24430","extern":"1","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-09-25T08:43:06Z","publisher":"Wiley","article_type":"original","date_published":"2022-10-01T00:00:00Z","month":"10","scopus_import":"1","tmp":{"image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"volume":43,"title":"WARS1 and SARS1: Two tRNA synthetases implicated in autosomal recessive microcephaly","status":"public","day":"01","author":[{"last_name":"Boegershausen","first_name":"Nina","full_name":"Boegershausen, Nina"},{"full_name":"Krawczyk, Hannah E.","first_name":"Hannah E.","last_name":"Krawczyk"},{"last_name":"Jamra","first_name":"Rami A.","full_name":"Jamra, Rami A."},{"full_name":"Lin, Sheng-Jia","first_name":"Sheng-Jia","last_name":"Lin"},{"full_name":"Yigit, Goekhan","first_name":"Goekhan","last_name":"Yigit"},{"full_name":"Huening, Irina","last_name":"Huening","first_name":"Irina"},{"last_name":"Polo","first_name":"Anna M.","full_name":"Polo, Anna M."},{"full_name":"Vona, Barbara","first_name":"Barbara","last_name":"Vona"},{"first_name":"Kevin","id":"3b3d2888-1ff6-11ee-9fa6-8f209ca91fe3","last_name":"Huang","orcid":"0000-0002-2512-7812","full_name":"Huang, Kevin"},{"full_name":"Schmidt, Julia","first_name":"Julia","last_name":"Schmidt"},{"full_name":"Altmueller, Janine","last_name":"Altmueller","first_name":"Janine"},{"full_name":"Luppe, Johannes","last_name":"Luppe","first_name":"Johannes"},{"last_name":"Platzer","first_name":"Konrad","full_name":"Platzer, Konrad"},{"first_name":"Beate B.","last_name":"Doergeloh","full_name":"Doergeloh, Beate B."},{"first_name":"Andreas","last_name":"Busche","full_name":"Busche, Andreas"},{"first_name":"Saskia","last_name":"Biskup","full_name":"Biskup, Saskia"},{"last_name":"Mendes, I","first_name":"Marisa","full_name":"Mendes, I, Marisa"},{"full_name":"Smith, Desiree E. C.","last_name":"Smith","first_name":"Desiree E. C."},{"last_name":"Salomons","first_name":"Gajja S.","full_name":"Salomons, Gajja S."},{"last_name":"Zibat","first_name":"Arne","full_name":"Zibat, Arne"},{"full_name":"Bueltmann, Eva","last_name":"Bueltmann","first_name":"Eva"},{"last_name":"Nuernberg","first_name":"Peter","full_name":"Nuernberg, Peter"},{"last_name":"Spielmann","first_name":"Malte","full_name":"Spielmann, Malte"},{"last_name":"Lemke","first_name":"Johannes R.","full_name":"Lemke, Johannes R."},{"full_name":"Li, Yun","last_name":"Li","first_name":"Yun"},{"first_name":"Martin","last_name":"Zenker","full_name":"Zenker, Martin"},{"full_name":"Varshney, Gaurav K.","last_name":"Varshney","first_name":"Gaurav K."},{"full_name":"Hillen, Hauke S.","last_name":"Hillen","first_name":"Hauke S."},{"full_name":"Kratz, Christian P.","last_name":"Kratz","first_name":"Christian P."},{"first_name":"Bernd","last_name":"Wollnik","full_name":"Wollnik, Bernd"}],"date_created":"2023-09-20T20:59:33Z","oa_version":"Published Version","file_date_updated":"2023-09-25T08:41:23Z","publication":"Human Mutation","_id":"14357","type":"journal_article","issue":"10","article_processing_charge":"No"},{"license":"https://creativecommons.org/publicdomain/zero/1.0/","main_file_link":[{"url":"https://doi.org/10.5281/ZENODO.8408897","open_access":"1"}],"date_updated":"2024-09-10T12:23:57Z","publisher":"Zenodo","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"06","date_published":"2022-06-28T00:00:00Z","abstract":[{"lang":"eng","text":"This dataset comprises all data shown in the figures of the submitted article \"Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses\" at arxiv.org/abs/2206.14104. Additional raw data are available from the corresponding author on reasonable request."}],"ddc":["530"],"oa":1,"department":[{"_id":"JoFi"}],"author":[{"full_name":"Zemlicka, Martin","last_name":"Zemlicka","first_name":"Martin","id":"2DCF8DE6-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Redchenko, Elena","last_name":"Redchenko","first_name":"Elena","id":"2C21D6E8-F248-11E8-B48F-1D18A9856A87"},{"id":"3F920B30-F248-11E8-B48F-1D18A9856A87","first_name":"Matilda","last_name":"Peruzzo","orcid":"0000-0002-3415-4628","full_name":"Peruzzo, Matilda"},{"first_name":"Farid","id":"2AED110C-F248-11E8-B48F-1D18A9856A87","last_name":"Hassani","full_name":"Hassani, Farid","orcid":"0000-0001-6937-5773"},{"full_name":"Trioni, Andrea","last_name":"Trioni","id":"42F71B44-F248-11E8-B48F-1D18A9856A87","first_name":"Andrea"},{"first_name":"Shabir","id":"2D25E1F6-F248-11E8-B48F-1D18A9856A87","last_name":"Barzanjeh","full_name":"Barzanjeh, Shabir","orcid":"0000-0003-0415-1423"},{"full_name":"Fink, Johannes M","orcid":"0000-0001-8112-028X","last_name":"Fink","id":"4B591CBA-F248-11E8-B48F-1D18A9856A87","first_name":"Johannes M"}],"related_material":{"record":[{"id":"14517","status":"public","relation":"used_in_publication"}]},"type":"research_data_reference","_id":"14520","article_processing_charge":"No","oa_version":"Published Version","date_created":"2023-11-13T08:09:10Z","citation":{"ama":"Zemlicka M, Redchenko E, Peruzzo M, et al. Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses. 2022. doi:10.5281/ZENODO.8408897","short":"M. Zemlicka, E. Redchenko, M. Peruzzo, F. Hassani, A. Trioni, S. Barzanjeh, J.M. Fink, (2022).","chicago":"Zemlicka, Martin, Elena Redchenko, Matilda Peruzzo, Farid Hassani, Andrea Trioni, Shabir Barzanjeh, and Johannes M Fink. “Compact Vacuum Gap Transmon Qubits: Selective and Sensitive Probes for Superconductor Surface Losses.” Zenodo, 2022. https://doi.org/10.5281/ZENODO.8408897.","apa":"Zemlicka, M., Redchenko, E., Peruzzo, M., Hassani, F., Trioni, A., Barzanjeh, S., & Fink, J. M. (2022). Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses. Zenodo. https://doi.org/10.5281/ZENODO.8408897","mla":"Zemlicka, Martin, et al. Compact Vacuum Gap Transmon Qubits: Selective and Sensitive Probes for Superconductor Surface Losses. Zenodo, 2022, doi:10.5281/ZENODO.8408897.","ieee":"M. Zemlicka et al., “Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses.” Zenodo, 2022.","ista":"Zemlicka M, Redchenko E, Peruzzo M, Hassani F, Trioni A, Barzanjeh S, Fink JM. 2022. Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses, Zenodo, 10.5281/ZENODO.8408897."},"doi":"10.5281/ZENODO.8408897","status":"public","title":"Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses","tmp":{"image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)"},"has_accepted_license":"1","year":"2022","day":"28"},{"author":[{"full_name":"Fischer, Julian L","orcid":"0000-0002-0479-558X","id":"2C12A0B0-F248-11E8-B48F-1D18A9856A87","first_name":"Julian L","last_name":"Fischer"},{"full_name":"Marveggio, Alice","first_name":"Alice","id":"25647992-AA84-11E9-9D75-8427E6697425","last_name":"Marveggio"}],"related_material":{"record":[{"relation":"dissertation_contains","id":"14587","status":"public"}]},"department":[{"_id":"JuFi"}],"ec_funded":1,"oa_version":"Preprint","date_created":"2023-11-23T09:30:02Z","doi":"10.48550/ARXIV.2203.17143","project":[{"call_identifier":"H2020","grant_number":"948819","name":"Bridging Scales in Random Materials","_id":"0aa76401-070f-11eb-9043-b5bb049fa26d"}],"citation":{"ama":"Fischer JL, Marveggio A. Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. arXiv. doi:10.48550/ARXIV.2203.17143","ista":"Fischer JL, Marveggio A. Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. arXiv, 10.48550/ARXIV.2203.17143.","ieee":"J. L. Fischer and A. Marveggio, “Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow,” arXiv. .","mla":"Fischer, Julian L., and Alice Marveggio. “Quantitative Convergence of the Vectorial Allen-Cahn Equation towards Multiphase Mean Curvature Flow.” ArXiv, doi:10.48550/ARXIV.2203.17143.","apa":"Fischer, J. L., & Marveggio, A. (n.d.). Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow. arXiv. https://doi.org/10.48550/ARXIV.2203.17143","short":"J.L. Fischer, A. Marveggio, ArXiv (n.d.).","chicago":"Fischer, Julian L, and Alice Marveggio. “Quantitative Convergence of the Vectorial Allen-Cahn Equation towards Multiphase Mean Curvature Flow.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2203.17143."},"publication":"arXiv","type":"preprint","_id":"14597","article_processing_charge":"No","title":"Quantitative convergence of the vectorial Allen-Cahn equation towards multiphase mean curvature flow","arxiv":1,"status":"public","year":"2022","day":"31","language":[{"iso":"eng"}],"external_id":{"arxiv":["2203.17143"]},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2203.17143"}],"publication_status":"submitted","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_updated":"2023-11-30T13:25:02Z","abstract":[{"text":"Phase-field models such as the Allen-Cahn equation may give rise to the formation and evolution of geometric shapes, a phenomenon that may be analyzed rigorously in suitable scaling regimes. In its sharp-interface limit, the vectorial Allen-Cahn equation with a potential with N≥3 distinct minima has been conjectured to describe the evolution of branched interfaces by multiphase mean curvature flow.\r\nIn the present work, we give a rigorous proof for this statement in two and three ambient dimensions and for a suitable class of potentials: As long as a strong solution to multiphase mean curvature flow exists, solutions to the vectorial Allen-Cahn equation with well-prepared initial data converge towards multiphase mean curvature flow in the limit of vanishing interface width parameter ε↘0. We even establish the rate of convergence O(ε1/2).\r\nOur approach is based on the gradient flow structure of the Allen-Cahn equation and its limiting motion: Building on the recent concept of \"gradient flow calibrations\" for multiphase mean curvature flow, we introduce a notion of relative entropy for the vectorial Allen-Cahn equation with multi-well potential. This enables us to overcome the limitations of other approaches, e.g. avoiding the need for a stability analysis of the Allen-Cahn operator or additional convergence hypotheses for the energy at positive times.","lang":"eng"}],"oa":1,"date_published":"2022-03-31T00:00:00Z","month":"03"}]