[{"author":[{"last_name":"Zhu","full_name":"Zhu, Zhenyu","first_name":"Zhenyu"},{"first_name":"Fanghui","last_name":"Liu","full_name":"Liu, Fanghui"},{"last_name":"Chrysos","full_name":"Chrysos, Grigorios G","first_name":"Grigorios G"},{"orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"},{"full_name":"Cevher, Volkan","last_name":"Cevher","first_name":"Volkan"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2305.19377"}],"volume":202,"date_created":"2023-08-22T14:18:18Z","extern":"1","intvolume":"       202","_id":"14208","status":"public","oa_version":"Preprint","oa":1,"month":"05","page":"43105-43128","year":"2023","language":[{"iso":"eng"}],"article_processing_charge":"No","department":[{"_id":"FrLo"}],"publisher":"ML Research Press","day":"30","quality_controlled":"1","abstract":[{"lang":"eng","text":"This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly) zero-training error under the lazy training regime. For this purpose, we unify three interrelated concepts of overparameterization, benign overfitting, and the Lipschitz constant of DNNs. Our results indicate that interpolating with smoother functions leads to better generalization. Furthermore, we investigate the special case where interpolating smooth ground-truth functions is performed by DNNs under the Neural Tangent Kernel (NTK) regime for generalization. Our result demonstrates that the generalization error converges to a constant order that only depends on label noise and initialization noise, which theoretically verifies benign overfitting. Our analysis provides a tight lower bound on the normalized margin under non-smooth activation functions, as well as the minimum eigenvalue of NTK under high-dimensional settings, which has its own interest in learning theory."}],"publication_status":"published","title":"Benign overfitting in deep neural networks under lazy training","date_updated":"2023-09-13T08:46:46Z","date_published":"2023-05-30T00:00:00Z","external_id":{"arxiv":["2305.19377"]},"arxiv":1,"publication":"Proceedings of the 40th International Conference on Machine Learning","citation":{"short":"Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 43105–43128.","apa":"Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., &#38; Cevher, V. (2023). Benign overfitting in deep neural networks under lazy training. In <i>Proceedings of the 40th International Conference on Machine Learning</i> (Vol. 202, pp. 43105–43128). Honolulu, Hawaii, United States: ML Research Press.","chicago":"Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.” In <i>Proceedings of the 40th International Conference on Machine Learning</i>, 202:43105–28. ML Research Press, 2023.","mla":"Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy Training.” <i>Proceedings of the 40th International Conference on Machine Learning</i>, vol. 202, ML Research Press, 2023, pp. 43105–28.","ista":"Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. 2023. Benign overfitting in deep neural networks under lazy training. Proceedings of the 40th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 202, 43105–43128.","ieee":"Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting in deep neural networks under lazy training,” in <i>Proceedings of the 40th International Conference on Machine Learning</i>, Honolulu, Hawaii, United States, 2023, vol. 202, pp. 43105–43128.","ama":"Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep neural networks under lazy training. In: <i>Proceedings of the 40th International Conference on Machine Learning</i>. Vol 202. ML Research Press; 2023:43105-43128."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"conference","conference":{"start_date":"2023-07-23","location":"Honolulu, Hawaii, United States","end_date":"2023-07-29","name":"International Conference on Machine Learning"},"alternative_title":["PMLR"]},{"date_updated":"2023-09-13T08:51:56Z","doi":"10.48550/arXiv.2304.10253","extern":"1","publication_status":"submitted","title":"A data augmentation perspective on diffusion models and retrieval","external_id":{"arxiv":["2304.10253"]},"date_published":"2023-04-20T00:00:00Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2304.10253","open_access":"1"}],"abstract":[{"text":"Diffusion models excel at generating photorealistic images from text-queries. Naturally, many approaches have been proposed to use these generative abilities to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large noisily supervised, but nonetheless, annotated datasets. It is an open question whether the generalization capabilities of diffusion models beyond using the additional data of the pre-training process for augmentation lead to improved downstream performance. We perform a systematic evaluation of existing methods to generate images from diffusion models and study new extensions to assess their benefit for data augmentation. While we find that personalizing diffusion models towards the target data outperforms simpler prompting strategies, we also show that using the training data of the diffusion model alone, via a simple nearest neighbor retrieval procedure, leads to even stronger downstream performance. Overall, our study probes the limitations of diffusion models for data augmentation but also highlights its potential in generating new training data to improve performance on simple downstream vision tasks.","lang":"eng"}],"author":[{"first_name":"Max F.","last_name":"Burg","full_name":"Burg, Max F."},{"last_name":"Wenzel","full_name":"Wenzel, Florian","first_name":"Florian"},{"full_name":"Zietlow, Dominik","last_name":"Zietlow","first_name":"Dominik"},{"last_name":"Horn","full_name":"Horn, Max","first_name":"Max"},{"first_name":"Osama","last_name":"Makansi","full_name":"Makansi, Osama"},{"last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"},{"full_name":"Russell, Chris","last_name":"Russell","first_name":"Chris"}],"date_created":"2023-08-22T14:18:43Z","oa":1,"month":"04","status":"public","oa_version":"Preprint","_id":"14209","arxiv":1,"article_number":"2304.10253","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., &#38; Russell, C. (n.d.). A data augmentation perspective on diffusion models and retrieval. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2304.10253\">https://doi.org/10.48550/arXiv.2304.10253</a>","chicago":"Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2304.10253\">https://doi.org/10.48550/arXiv.2304.10253</a>.","short":"M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.).","ieee":"M. F. Burg <i>et al.</i>, “A data augmentation perspective on diffusion models and retrieval,” <i>arXiv</i>. .","ama":"Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2304.10253\">10.48550/arXiv.2304.10253</a>","mla":"Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” <i>ArXiv</i>, 2304.10253, doi:<a href=\"https://doi.org/10.48550/arXiv.2304.10253\">10.48550/arXiv.2304.10253</a>.","ista":"Burg MF, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. A data augmentation perspective on diffusion models and retrieval. arXiv, 2304.10253."},"language":[{"iso":"eng"}],"year":"2023","publication":"arXiv","day":"20","type":"preprint","article_processing_charge":"No","department":[{"_id":"FrLo"}]},{"external_id":{"arxiv":["2304.07939"]},"date_published":"2023-04-17T00:00:00Z","publication_status":"submitted","doi":"10.48550/arXiv.2304.07939","title":"Leveraging sparse and shared feature activations for disentangled representation learning","date_updated":"2024-10-09T21:06:54Z","date_created":"2023-08-22T14:19:03Z","author":[{"last_name":"Fumero","full_name":"Fumero, Marco","first_name":"Marco"},{"first_name":"Florian","full_name":"Wenzel, Florian","last_name":"Wenzel"},{"full_name":"Zancato, Luca","last_name":"Zancato","first_name":"Luca"},{"first_name":"Alessandro","full_name":"Achille, Alessandro","last_name":"Achille"},{"first_name":"Emanuele","last_name":"Rodolà","full_name":"Rodolà, Emanuele"},{"first_name":"Stefano","last_name":"Soatto","full_name":"Soatto, Stefano"},{"last_name":"Schölkopf","full_name":"Schölkopf, Bernhard","first_name":"Bernhard"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"}],"abstract":[{"lang":"eng","text":"Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation. Assuming each supervised task only depends on an unknown subset of the factors of variation, we disentangle the feature space of a supervised multi-task model, with features activating sparsely across different tasks and information being shared as appropriate. Importantly, we never directly observe the factors of variations but establish that access to multiple tasks is sufficient for identifiability under sufficiency and minimality assumptions. We validate our approach on six real world distribution shift benchmarks, and different data modalities (images, text), demonstrating how disentangled representations can be transferred to real settings."}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2304.07939"}],"month":"04","corr_author":"1","oa":1,"article_number":"2304.07939","arxiv":1,"_id":"14210","oa_version":"Preprint","status":"public","year":"2023","language":[{"iso":"eng"}],"citation":{"chicago":"Fumero, Marco, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, and Francesco Locatello. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2304.07939\">https://doi.org/10.48550/arXiv.2304.07939</a>.","apa":"Fumero, M., Wenzel, F., Zancato, L., Achille, A., Rodolà, E., Soatto, S., … Locatello, F. (n.d.). Leveraging sparse and shared feature activations for disentangled representation learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2304.07939\">https://doi.org/10.48550/arXiv.2304.07939</a>","short":"M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, ArXiv (n.d.).","ieee":"M. Fumero <i>et al.</i>, “Leveraging sparse and shared feature activations for disentangled representation learning,” <i>arXiv</i>. .","ama":"Fumero M, Wenzel F, Zancato L, et al. Leveraging sparse and shared feature activations for disentangled representation learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2304.07939\">10.48550/arXiv.2304.07939</a>","mla":"Fumero, Marco, et al. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.” <i>ArXiv</i>, 2304.07939, doi:<a href=\"https://doi.org/10.48550/arXiv.2304.07939\">10.48550/arXiv.2304.07939</a>.","ista":"Fumero M, Wenzel F, Zancato L, Achille A, Rodolà E, Soatto S, Schölkopf B, Locatello F. Leveraging sparse and shared feature activations for disentangled representation learning. arXiv, 2304.07939."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"arXiv","day":"17","department":[{"_id":"FrLo"}],"article_processing_charge":"No","type":"preprint"},{"arxiv":1,"_id":"14211","status":"public","oa_version":"Preprint","oa":1,"month":"04","author":[{"first_name":"Francesco","last_name":"Montagna","full_name":"Montagna, Francesco"},{"full_name":"Noceti, Nicoletta","last_name":"Noceti","first_name":"Nicoletta"},{"full_name":"Rosasco, Lorenzo","last_name":"Rosasco","first_name":"Lorenzo"},{"full_name":"Zhang, Kun","last_name":"Zhang","first_name":"Kun"},{"orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"main_file_link":[{"url":"https://arxiv.org/abs/2304.03265","open_access":"1"}],"abstract":[{"text":"Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: this is the case for the Gaussian noise assumption on additive non-linear models, which is common to many causal discovery approaches. In this paper we show the shortcomings of inference under this hypothesis, analyzing the risk of edge inversion under violation of Gaussianity of the noise terms. Then, we propose a novel method for inferring the topological ordering of the variables in the causal graph, from data generated according to an additive non-linear model with a generic noise distribution. This leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm with a minimal set of assumptions and state of the art performance, experimentally benchmarked on synthetic data.","lang":"eng"}],"quality_controlled":"1","date_created":"2023-08-22T14:19:21Z","title":"Causal discovery with score matching on additive models with arbitrary noise","publication_status":"published","extern":"1","date_updated":"2024-10-14T12:30:04Z","scopus_import":"1","external_id":{"arxiv":["2304.03265"]},"date_published":"2023-04-01T00:00:00Z","article_processing_charge":"No","type":"conference","conference":{"name":"CLeaR: Conference on Causal Learning and Reasoning","start_date":"2023-04-11","location":"Tübingen, Germany","end_date":"2023-04-14"},"department":[{"_id":"FrLo"}],"day":"01","publication":"2nd Conference on Causal Learning and Reasoning","citation":{"ieee":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery with score matching on additive models with arbitrary noise,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","ama":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with score matching on additive models with arbitrary noise. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","mla":"Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ista":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Causal discovery with score matching on additive models with arbitrary noise. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","apa":"Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &#38; Locatello, F. (2023). Causal discovery with score matching on additive models with arbitrary noise. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","short":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","language":[{"iso":"eng"}]},{"day":"01","conference":{"name":"CLeaR: Conference on Causal Learning and Reasoning","end_date":"2023-04-14","start_date":"2023-04-11","location":"Tübingen, Germany"},"department":[{"_id":"FrLo"}],"article_processing_charge":"No","type":"conference","year":"2023","language":[{"iso":"eng"}],"citation":{"ista":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Scalable causal discovery with score matching. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","mla":"Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ama":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery with score matching. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ieee":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable causal discovery with score matching,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","short":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","apa":"Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &#38; Locatello, F. (2023). Scalable causal discovery with score matching. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"2nd Conference on Causal Learning and Reasoning","month":"04","oa":1,"arxiv":1,"_id":"14212","oa_version":"Preprint","status":"public","external_id":{"arxiv":["2304.03382"]},"date_published":"2023-04-01T00:00:00Z","title":"Scalable causal discovery with score matching","publication_status":"published","extern":"1","date_updated":"2024-10-14T12:30:15Z","scopus_import":"1","date_created":"2023-08-22T14:19:40Z","author":[{"last_name":"Montagna","full_name":"Montagna, Francesco","first_name":"Francesco"},{"full_name":"Noceti, Nicoletta","last_name":"Noceti","first_name":"Nicoletta"},{"first_name":"Lorenzo","full_name":"Rosasco, Lorenzo","last_name":"Rosasco"},{"first_name":"Kun","last_name":"Zhang","full_name":"Zhang, Kun"},{"full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"}],"quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2304.03382"}],"abstract":[{"text":"This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function ∇logp(X), we extend the work of Rolland et al. (2022) that only recovers the topological order from the score and requires an expensive pruning step removing spurious edges among those admitted by the ordering. Our analysis leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces the complexity of the pruning by a factor proportional to the graph size. In practice, DAS achieves competitive accuracy with current state-of-the-art while being over an order of magnitude faster. Overall, our approach enables principled and scalable causal discovery, significantly lowering the compute bar.","lang":"eng"}]},{"type":"conference","article_processing_charge":"No","conference":{"end_date":"2023-04-14","location":"Tübingen, Germany","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning"},"department":[{"_id":"FrLo"}],"day":"12","publication":"2nd Conference on Causal Learning and Reasoning","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"short":"Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","apa":"Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &#38; Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric causal representation learning. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","chicago":"Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ista":"Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023. Causal triplet: An open challenge for intervention-centric causal representation learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","mla":"Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ieee":"Y. Liu <i>et al.</i>, “Causal triplet: An open challenge for intervention-centric causal representation learning,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","ama":"Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023."},"language":[{"iso":"eng"}],"year":"2023","status":"public","oa_version":"Preprint","arxiv":1,"_id":"14214","oa":1,"month":"04","quality_controlled":"1","abstract":[{"text":"Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In this paper, we present Causal Triplet, a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual setting, where only certain object-level variables allow for counterfactual observations whereas others do not; (ii) an interventional downstream task with an emphasis on out-of-distribution robustness from the independent causal mechanisms principle. Through extensive experiments, we find that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts. However, recent causal representation learning methods still struggle to identify such latent structures, indicating substantial challenges and opportunities for future work.","lang":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2301.05169"}],"author":[{"full_name":"Liu, Yuejiang","last_name":"Liu","first_name":"Yuejiang"},{"last_name":"Alahi","full_name":"Alahi, Alexandre","first_name":"Alexandre"},{"full_name":"Russell, Chris","last_name":"Russell","first_name":"Chris"},{"full_name":"Horn, Max","last_name":"Horn","first_name":"Max"},{"full_name":"Zietlow, Dominik","last_name":"Zietlow","first_name":"Dominik"},{"last_name":"Schölkopf","full_name":"Schölkopf, Bernhard","first_name":"Bernhard"},{"orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"date_created":"2023-08-22T14:20:18Z","date_updated":"2024-10-14T12:30:42Z","extern":"1","publication_status":"published","title":"Causal triplet: An open challenge for intervention-centric causal representation learning","external_id":{"arxiv":["2301.05169"]},"date_published":"2023-04-12T00:00:00Z"},{"type":"conference","acknowledgement":"AN, MF, and FL partially worked on ASIF when they were at Amazon Web Services in Tübingen,\r\nGermany. This paper is financially supported by the PRIN 2020 project no.2020TA3K9N (LEGO.AI), PNRR MUR project PE0000013-FAIR, and ERC Grant no.802554 (SPECGEO).","conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2023-12-14","start_date":"2023-12-12","location":"New Orleans, LA, United States"},"file":[{"file_size":12648978,"file_id":"18994","checksum":"e51c90300b92d7135050da5c9e3a8015","relation":"main_file","content_type":"application/pdf","file_name":"2023_NeurIPS_Fumero.pdf","date_created":"2025-02-04T12:16:13Z","creator":"dernst","access_level":"open_access","date_updated":"2025-02-04T12:16:13Z","success":1}],"alternative_title":["Advances in Neural Information Processing Systems"],"publication":"37th Conference on Neural Information Processing Systems","citation":{"apa":"Norelli, A., Fumero, M., Maiorca, V., Moschella, L., Rodolà, E., &#38; Locatello, F. (2023). ASIF: Coupled data turns unimodal models to multimodal without training. In <i>37th Conference on Neural Information Processing Systems</i> (Vol. 36, pp. 15303–15319). New Orleans, LA, United States: Neural Information Processing Systems Foundation.","chicago":"Norelli, Antonio, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, and Francesco Locatello. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.” In <i>37th Conference on Neural Information Processing Systems</i>, 36:15303–19. Neural Information Processing Systems Foundation, 2023.","short":"A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, F. Locatello, in:, 37th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2023, pp. 15303–15319.","ieee":"A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, and F. Locatello, “ASIF: Coupled data turns unimodal models to multimodal without training,” in <i>37th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2023, vol. 36, pp. 15303–15319.","ama":"Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF: Coupled data turns unimodal models to multimodal without training. In: <i>37th Conference on Neural Information Processing Systems</i>. Vol 36. Neural Information Processing Systems Foundation; 2023:15303-15319.","ista":"Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. 2023. ASIF: Coupled data turns unimodal models to multimodal without training. 37th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 36, 15303–15319.","mla":"Norelli, Antonio, et al. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.” <i>37th Conference on Neural Information Processing Systems</i>, vol. 36, Neural Information Processing Systems Foundation, 2023, pp. 15303–19."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","arxiv":1,"corr_author":"1","publication_identifier":{"isbn":["9781713899921"]},"abstract":[{"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.","lang":"eng"}],"quality_controlled":"1","title":"ASIF: Coupled data turns unimodal models to multimodal without training","publication_status":"published","date_updated":"2025-05-14T11:28:52Z","external_id":{"arxiv":["2210.01738"]},"date_published":"2023-10-04T00:00:00Z","article_processing_charge":"No","has_accepted_license":"1","department":[{"_id":"FrLo"}],"publisher":"Neural Information Processing Systems Foundation","day":"04","page":"15303-15319","OA_type":"green","year":"2023","language":[{"iso":"eng"}],"ddc":["000"],"_id":"14216","file_date_updated":"2025-02-04T12:16:13Z","oa_version":"Preprint","status":"public","oa":1,"month":"10","author":[{"first_name":"Antonio","full_name":"Norelli, Antonio","last_name":"Norelli"},{"first_name":"Marco","full_name":"Fumero, Marco","last_name":"Fumero"},{"first_name":"Valentino","full_name":"Maiorca, Valentino","last_name":"Maiorca"},{"last_name":"Moschella","full_name":"Moschella, Luca","first_name":"Luca"},{"full_name":"Rodolà, Emanuele","last_name":"Rodolà","first_name":"Emanuele"},{"last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"}],"volume":36,"related_material":{"link":[{"url":"https://github.com/noranta4/ASIF","relation":"software"}]},"date_created":"2023-08-22T14:22:04Z","intvolume":"        36"},{"day":"01","conference":{"location":"Kigali, Rwanda","start_date":"2023-05-01","end_date":"2023-05-05","name":"International Conference on Machine Learning Representations"},"department":[{"_id":"FrLo"}],"article_processing_charge":"No","type":"conference","year":"2023","language":[{"iso":"eng"}],"citation":{"chicago":"Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent Space Communication.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","apa":"Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., &#38; Rodolà, E. (2023). Relative representations enable zero-shot latent space communication. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","short":"L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà, in:, The 11th International Conference on Learning Representations, 2023.","ieee":"L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà, “Relative representations enable zero-shot latent space communication,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","ama":"Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative representations enable zero-shot latent space communication. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","ista":"Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. 2023. Relative representations enable zero-shot latent space communication. The 11th International Conference on Learning Representations. International Conference on Machine Learning Representations.","mla":"Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space Communication.” <i>The 11th International Conference on Learning Representations</i>, 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"The 11th International Conference on Learning Representations","month":"05","oa":1,"_id":"14217","arxiv":1,"oa_version":"Preprint","status":"public","external_id":{"arxiv":["2209.15430"]},"date_published":"2023-05-01T00:00:00Z","publication_status":"published","extern":"1","title":"Relative representations enable zero-shot latent space communication","date_updated":"2023-09-13T09:44:26Z","date_created":"2023-08-22T14:22:20Z","author":[{"last_name":"Moschella","full_name":"Moschella, Luca","first_name":"Luca"},{"full_name":"Maiorca, Valentino","last_name":"Maiorca","first_name":"Valentino"},{"first_name":"Marco","last_name":"Fumero","full_name":"Fumero, Marco"},{"first_name":"Antonio","last_name":"Norelli","full_name":"Norelli, Antonio"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683"},{"first_name":"Emanuele","full_name":"Rodolà, Emanuele","last_name":"Rodolà"}],"quality_controlled":"1","abstract":[{"lang":"eng","text":"Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the random weights initialization, training hyperparameters, or other sources of randomness in the training phase may induce incoherent latent spaces that hinder any form of reuse. Nevertheless, we empirically observe that, under the same data and modeling choices, the angles between the encodings within distinct latent spaces do not change. In this work, we propose the latent similarity between each sample and a fixed set of anchors as an alternative data representation, demonstrating that it can enforce the desired invariances without any additional training. We show how neural architectures can leverage these relative representations to guarantee, in practice, invariance to latent isometries and rescalings, effectively enabling latent space communication: from zero-shot model stitching to latent space comparison between diverse settings. We extensively validate the generalization capability of our approach on different datasets, spanning various modalities (images, text, graphs), tasks (e.g., classification, reconstruction) and architectures (e.g., CNNs, GCNs, transformers)."}],"main_file_link":[{"url":"https://arxiv.org/abs/2209.15430","open_access":"1"}]},{"date_published":"2023-05-10T00:00:00Z","external_id":{"arxiv":["2209.14860"]},"publication_status":"published","extern":"1","title":"Bridging the gap to real-world object-centric learning","date_updated":"2024-10-14T12:30:54Z","date_created":"2023-08-22T14:22:41Z","author":[{"first_name":"Maximilian","full_name":"Seitzer, Maximilian","last_name":"Seitzer"},{"full_name":"Horn, Max","last_name":"Horn","first_name":"Max"},{"first_name":"Andrii","last_name":"Zadaianchuk","full_name":"Zadaianchuk, Andrii"},{"first_name":"Dominik","full_name":"Zietlow, Dominik","last_name":"Zietlow"},{"last_name":"Xiao","full_name":"Xiao, Tianjun","first_name":"Tianjun"},{"full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel","last_name":"Carl-Johann Simon-Gabriel","first_name":"Carl-Johann Simon-Gabriel"},{"first_name":"Tong","full_name":"He, Tong","last_name":"He"},{"first_name":"Zheng","last_name":"Zhang","full_name":"Zhang, Zheng"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"},{"first_name":"Thomas","last_name":"Brox","full_name":"Brox, Thomas"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco"}],"quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/2209.14860","open_access":"1"}],"abstract":[{"lang":"eng","text":"Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing image-based object-centric learning models on simulated data and is the first unsupervised object-centric model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows competitive performance compared to more involved pipelines from the computer vision literature."}],"month":"05","oa":1,"_id":"14218","arxiv":1,"status":"public","oa_version":"Preprint","year":"2023","language":[{"iso":"eng"}],"citation":{"short":"M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The 11th International Conference on Learning Representations, 2023.","chicago":"Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging the Gap to Real-World Object-Centric Learning.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","apa":"Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann Simon-Gabriel, C.-J. S.-G., … Locatello, F. (2023). Bridging the gap to real-world object-centric learning. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","ista":"Seitzer M, Horn M, Zadaianchuk A, Zietlow D, Xiao T, Carl-Johann Simon-Gabriel C-JS-G, He T, Zhang Z, Schölkopf B, Brox T, Locatello F. 2023. Bridging the gap to real-world object-centric learning. The 11th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","mla":"Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric Learning.” <i>The 11th International Conference on Learning Representations</i>, 2023.","ama":"Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric learning. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","ieee":"M. Seitzer <i>et al.</i>, “Bridging the gap to real-world object-centric learning,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"The 11th International Conference on Learning Representations","day":"10","conference":{"name":"ICLR: International Conference on Learning Representations","end_date":"2023-05-05","start_date":"2023-05-01","location":"Kigali, Rwanda"},"department":[{"_id":"FrLo"}],"article_processing_charge":"No","type":"conference"},{"citation":{"short":"A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The 11th International Conference on Learning Representations, 2023.","chicago":"Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","apa":"Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., &#38; Brox, T. (2023). Unsupervised semantic segmentation with self-supervised object-centric representations. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","ista":"Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. 2023. Unsupervised semantic segmentation with self-supervised object-centric representations. The 11th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","mla":"Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” <i>The 11th International Conference on Learning Representations</i>, 2023.","ieee":"A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised semantic segmentation with self-supervised object-centric representations,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","ama":"Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic segmentation with self-supervised object-centric representations. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","language":[{"iso":"eng"}],"publication":"The 11th International Conference on Learning Representations","day":"01","article_processing_charge":"No","type":"conference","department":[{"_id":"FrLo"}],"conference":{"name":"ICLR: International Conference on Learning Representations","location":"Kigali, Rwanda","start_date":"2023-05-01","end_date":"2023-05-05"},"extern":"1","title":"Unsupervised semantic segmentation with self-supervised object-centric representations","publication_status":"published","date_updated":"2023-09-13T11:25:43Z","external_id":{"arxiv":["2207.05027"]},"date_published":"2023-05-01T00:00:00Z","author":[{"first_name":"Andrii","last_name":"Zadaianchuk","full_name":"Zadaianchuk, Andrii"},{"last_name":"Kleindessner","full_name":"Kleindessner, Matthaeus","first_name":"Matthaeus"},{"full_name":"Zhu, Yi","last_name":"Zhu","first_name":"Yi"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"first_name":"Thomas","full_name":"Brox, Thomas","last_name":"Brox"}],"quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2207.05027"}],"abstract":[{"text":"In this paper, we show that recent advances in self-supervised feature\r\nlearning enable unsupervised object discovery and semantic segmentation with a\r\nperformance that matches the state of the field on supervised semantic\r\nsegmentation 10 years ago. We propose a methodology based on unsupervised\r\nsaliency masks and self-supervised feature clustering to kickstart object\r\ndiscovery followed by training a semantic segmentation network on pseudo-labels\r\nto bootstrap the system on images with multiple objects. We present results on\r\nPASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we\r\nreport for the first time results on MS COCO for the whole set of 81 classes:\r\nour method discovers 34 categories with more than $20\\%$ IoU, while obtaining\r\nan average IoU of 19.6 for all 81 categories.","lang":"eng"}],"date_created":"2023-08-22T14:22:58Z","oa":1,"month":"05","_id":"14219","arxiv":1,"status":"public","oa_version":"Preprint"},{"day":"15","article_processing_charge":"No","type":"conference","conference":{"location":"Tübingen, Germany","start_date":"2023-04-11","end_date":"2023-04-14","name":"CLeaR: Conference on Causal Learning and Reasoning"},"department":[{"_id":"FrLo"}],"citation":{"ista":"Tangemann M, Schneider S, Kügelgen J von, Locatello F, Gehler P, Brox T, Kümmerer M, Bethge M, Schölkopf B. 2023. Unsupervised object learning via common fate. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning, 2110.06562.","mla":"Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2110.06562, 2023.","ama":"Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning via common fate. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ieee":"M. Tangemann <i>et al.</i>, “Unsupervised object learning via common fate,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","short":"M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T. Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","apa":"Tangemann, M., Schneider, S., Kügelgen, J. von, Locatello, F., Gehler, P., Brox, T., … Schölkopf, B. (2023). Unsupervised object learning via common fate. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","chicago":"Tangemann, Matthias, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, and Bernhard Schölkopf. “Unsupervised Object Learning via Common Fate.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","language":[{"iso":"eng"}],"publication":"2nd Conference on Causal Learning and Reasoning","oa":1,"month":"04","arxiv":1,"_id":"14222","status":"public","oa_version":"Preprint","article_number":"2110.06562","title":"Unsupervised object learning via common fate","publication_status":"published","extern":"1","date_updated":"2023-09-13T11:31:14Z","external_id":{"arxiv":["2110.06562"]},"date_published":"2023-04-15T00:00:00Z","author":[{"first_name":"Matthias","last_name":"Tangemann","full_name":"Tangemann, Matthias"},{"last_name":"Schneider","full_name":"Schneider, Steffen","first_name":"Steffen"},{"first_name":"Julius von","full_name":"Kügelgen, Julius von","last_name":"Kügelgen"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco"},{"full_name":"Gehler, Peter","last_name":"Gehler","first_name":"Peter"},{"full_name":"Brox, Thomas","last_name":"Brox","first_name":"Thomas"},{"last_name":"Kümmerer","full_name":"Kümmerer, Matthias","first_name":"Matthias"},{"first_name":"Matthias","full_name":"Bethge, Matthias","last_name":"Bethge"},{"first_name":"Bernhard","full_name":"Schölkopf, Bernhard","last_name":"Schölkopf"}],"quality_controlled":"1","abstract":[{"lang":"eng","text":"Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, and provide candidate solutions for each of them. Inspired by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks of moving objects via unsupervised motion segmentation. Second, generative models are trained on the masks of the background and the moving objects, respectively. Third, background and foreground models are combined in a conditional \"dead leaves\" scene model to sample novel scene configurations where occlusions and depth layering arise naturally. To evaluate the individual stages, we introduce the Fishbowl dataset positioned between complex real-world scenes and common object-centric benchmarks of simplistic objects. We show that our approach allows learning generative models that generalize beyond the occlusions present in the input videos, and represent scenes in a modular fashion that allows sampling plausible scenes outside the training distribution by permitting, for instance, object numbers or densities not observed in the training set."}],"main_file_link":[{"url":"https://arxiv.org/abs/2110.06562","open_access":"1"}],"date_created":"2023-08-22T14:23:54Z"},{"external_id":{"arxiv":["2308.15247"],"pmid":["37595218"],"isi":["001101784100001"]},"isi":1,"date_published":"2023-08-04T00:00:00Z","title":"Nonadiabatic laser-induced alignment dynamics of molecules on a surface","publication_status":"published","project":[{"grant_number":"801770","name":"Angulon: physics and applications of a new quasiparticle","_id":"2688CF98-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"date_updated":"2025-04-14T07:48:54Z","abstract":[{"text":"We demonstrate that a sodium dimer, Na2(13Σ+u), residing on the surface of a helium nanodroplet, can be set into rotation by a nonresonant 1.0 ps infrared laser pulse. The time-dependent degree of alignment measured, exhibits a periodic, gradually decreasing structure that deviates qualitatively from that expected for gas-phase dimers. Comparison to alignment dynamics calculated from the time-dependent rotational Schrödinger equation shows that the deviation is due to the alignment dependent interaction between the dimer and the droplet surface. This interaction confines the dimer to the tangential plane of the droplet surface at the point where it resides and is the reason that the observed alignment dynamics is also well described by a 2D quantum rotor model.","lang":"eng"}],"quality_controlled":"1","publication_identifier":{"eissn":["1079-7114"],"issn":["0031-9007"]},"arxiv":1,"ec_funded":1,"pmid":1,"citation":{"apa":"Kranabetter, L., Kristensen, H. H., Ghazaryan, A., Schouder, C. A., Chatterley, A. S., Janssen, P., … Stapelfeldt, H. (2023). Nonadiabatic laser-induced alignment dynamics of molecules on a surface. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">https://doi.org/10.1103/PhysRevLett.131.053201</a>","chicago":"Kranabetter, Lorenz, Henrik H. Kristensen, Areg Ghazaryan, Constant A. Schouder, Adam S. Chatterley, Paul Janssen, Frank Jensen, Robert E. Zillich, Mikhail Lemeshko, and Henrik Stapelfeldt. “Nonadiabatic Laser-Induced Alignment Dynamics of Molecules on a Surface.” <i>Physical Review Letters</i>. American Physical Society, 2023. <a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">https://doi.org/10.1103/PhysRevLett.131.053201</a>.","short":"L. Kranabetter, H.H. Kristensen, A. Ghazaryan, C.A. Schouder, A.S. Chatterley, P. Janssen, F. Jensen, R.E. Zillich, M. Lemeshko, H. Stapelfeldt, Physical Review Letters 131 (2023).","ieee":"L. Kranabetter <i>et al.</i>, “Nonadiabatic laser-induced alignment dynamics of molecules on a surface,” <i>Physical Review Letters</i>, vol. 131, no. 5. American Physical Society, 2023.","ama":"Kranabetter L, Kristensen HH, Ghazaryan A, et al. Nonadiabatic laser-induced alignment dynamics of molecules on a surface. <i>Physical Review Letters</i>. 2023;131(5). doi:<a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">10.1103/PhysRevLett.131.053201</a>","mla":"Kranabetter, Lorenz, et al. “Nonadiabatic Laser-Induced Alignment Dynamics of Molecules on a Surface.” <i>Physical Review Letters</i>, vol. 131, no. 5, 053201, American Physical Society, 2023, doi:<a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">10.1103/PhysRevLett.131.053201</a>.","ista":"Kranabetter L, Kristensen HH, Ghazaryan A, Schouder CA, Chatterley AS, Janssen P, Jensen F, Zillich RE, Lemeshko M, Stapelfeldt H. 2023. Nonadiabatic laser-induced alignment dynamics of molecules on a surface. Physical Review Letters. 131(5), 053201."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Physical Review Letters","acknowledgement":"H. S. acknowledges support from The Villum Foundation through a Villum Investigator Grant No. 25886. M. L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). F. J. and R. E. Z. acknowledge support from the Centre for Scientific Computing, Aarhus and the JKU scientific computing administration, Linz, respectively.","type":"journal_article","intvolume":"       131","doi":"10.1103/PhysRevLett.131.053201","scopus_import":"1","volume":131,"date_created":"2023-08-27T22:01:16Z","author":[{"first_name":"Lorenz","full_name":"Kranabetter, Lorenz","last_name":"Kranabetter"},{"last_name":"Kristensen","full_name":"Kristensen, Henrik H.","first_name":"Henrik H."},{"full_name":"Ghazaryan, Areg","last_name":"Ghazaryan","orcid":"0000-0001-9666-3543","id":"4AF46FD6-F248-11E8-B48F-1D18A9856A87","first_name":"Areg"},{"last_name":"Schouder","full_name":"Schouder, Constant A.","first_name":"Constant A."},{"full_name":"Chatterley, Adam S.","last_name":"Chatterley","first_name":"Adam S."},{"first_name":"Paul","full_name":"Janssen, Paul","last_name":"Janssen"},{"last_name":"Jensen","full_name":"Jensen, Frank","first_name":"Frank"},{"last_name":"Zillich","full_name":"Zillich, Robert E.","first_name":"Robert E."},{"full_name":"Lemeshko, Mikhail","last_name":"Lemeshko","orcid":"0000-0002-6990-7802","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","first_name":"Mikhail"},{"last_name":"Stapelfeldt","full_name":"Stapelfeldt, Henrik","first_name":"Henrik"}],"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2308.15247","open_access":"1"}],"month":"08","oa":1,"article_number":"053201","_id":"14238","status":"public","oa_version":"Preprint","year":"2023","language":[{"iso":"eng"}],"day":"04","issue":"5","article_type":"original","publisher":"American Physical Society","department":[{"_id":"MiLe"}],"article_processing_charge":"No"},{"author":[{"full_name":"Mauri, Mirko","last_name":"Mauri","id":"2cf70c34-09c1-11ed-bd8d-c34fac206130","first_name":"Mirko"},{"full_name":"Shinder, Evgeny","last_name":"Shinder","first_name":"Evgeny"}],"volume":11,"date_created":"2023-08-27T22:01:16Z","doi":"10.1017/fms.2023.65","scopus_import":"1","intvolume":"        11","file_date_updated":"2023-09-05T06:43:11Z","_id":"14239","ddc":["510"],"oa_version":"Published Version","status":"public","article_number":"e66","oa":1,"month":"08","license":"https://creativecommons.org/licenses/by/4.0/","year":"2023","language":[{"iso":"eng"}],"article_processing_charge":"Yes","has_accepted_license":"1","department":[{"_id":"TaHa"}],"publisher":"Cambridge University Press","article_type":"original","day":"03","abstract":[{"lang":"eng","text":"Given a resolution of rational singularities  π:X~→X  over a field of characteristic zero, we use a Hodge-theoretic argument to prove that the image of the functor  Rπ∗:Db(X~)→Db(X)\r\n  between bounded derived categories of coherent sheaves generates  Db(X)\r\n  as a triangulated category. This gives a weak version of the Bondal–Orlov localization conjecture [BO02], answering a question from [PS21]. The same result is established more generally for proper (not necessarily birational) morphisms  π:X~→X , with  X~\r\n  smooth, satisfying  Rπ∗(OX~)=OX ."}],"quality_controlled":"1","title":"Homological Bondal-Orlov localization conjecture for rational singularities","project":[{"call_identifier":"H2020","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program","grant_number":"101034413"}],"publication_status":"published","date_updated":"2025-04-14T07:54:52Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_published":"2023-08-03T00:00:00Z","isi":1,"external_id":{"arxiv":["2212.06786"],"isi":["001041926700001"]},"ec_funded":1,"arxiv":1,"corr_author":"1","publication_identifier":{"eissn":["2050-5094"]},"publication":"Forum of Mathematics, Sigma","citation":{"ieee":"M. Mauri and E. Shinder, “Homological Bondal-Orlov localization conjecture for rational singularities,” <i>Forum of Mathematics, Sigma</i>, vol. 11. Cambridge University Press, 2023.","ama":"Mauri M, Shinder E. Homological Bondal-Orlov localization conjecture for rational singularities. <i>Forum of Mathematics, Sigma</i>. 2023;11. doi:<a href=\"https://doi.org/10.1017/fms.2023.65\">10.1017/fms.2023.65</a>","ista":"Mauri M, Shinder E. 2023. Homological Bondal-Orlov localization conjecture for rational singularities. Forum of Mathematics, Sigma. 11, e66.","mla":"Mauri, Mirko, and Evgeny Shinder. “Homological Bondal-Orlov Localization Conjecture for Rational Singularities.” <i>Forum of Mathematics, Sigma</i>, vol. 11, e66, Cambridge University Press, 2023, doi:<a href=\"https://doi.org/10.1017/fms.2023.65\">10.1017/fms.2023.65</a>.","apa":"Mauri, M., &#38; Shinder, E. (2023). Homological Bondal-Orlov localization conjecture for rational singularities. <i>Forum of Mathematics, Sigma</i>. Cambridge University Press. <a href=\"https://doi.org/10.1017/fms.2023.65\">https://doi.org/10.1017/fms.2023.65</a>","chicago":"Mauri, Mirko, and Evgeny Shinder. “Homological Bondal-Orlov Localization Conjecture for Rational Singularities.” <i>Forum of Mathematics, Sigma</i>. Cambridge University Press, 2023. <a href=\"https://doi.org/10.1017/fms.2023.65\">https://doi.org/10.1017/fms.2023.65</a>.","short":"M. Mauri, E. Shinder, Forum of Mathematics, Sigma 11 (2023)."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","acknowledgement":"We thank Agnieszka Bodzenta-Skibińska, Paolo Cascini, Wahei Hara, Sándor Kovács, Alexander Kuznetsov, Mircea Musta  ă, Nebojsa Pavic, Pavel Sechin, and Michael Wemyss for discussions and e-mail correspondence. We also thank the anonymous referee for the helpful comments. M.M. was supported by the Institute of Science and Technology Austria. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101034413. E.S. was partially supported by the EPSRC grant EP/T019379/1 “Derived categories and algebraic K-theory of singularities”, and by the ERC Synergy grant “Modern Aspects of Geometry: Categories, Cycles and Cohomology of Hyperkähler Varieties.”\r\n\r\n","file":[{"access_level":"open_access","creator":"dernst","success":1,"date_updated":"2023-09-05T06:43:11Z","date_created":"2023-09-05T06:43:11Z","file_name":"2023_ForumMathematics_Mauri.pdf","checksum":"c36241750cc5cb06890aec0ecdfee626","content_type":"application/pdf","relation":"main_file","file_id":"14266","file_size":280865}]},{"acknowledgement":"We thank Georg Sperl for helping with early research for this paper, Mickael Ly and Yi-Lu Chen for proofreading, and members of the ISTA Visual Computing Group for general feedback. This project was funded in part by the European Research Council (ERC Consolidator Grant 101045083 CoDiNA).\r\nThe motorboat and sailboat were modeled by Sergei and the palmtrees by YadroGames. The environment map was created by Emil Persson.","type":"journal_article","file":[{"content_type":"video/mp4","relation":"main_file","checksum":"1d178bb2f8011d9f5aedda6427e18c7a","file_size":511572575,"file_id":"14704","success":1,"date_updated":"2023-12-21T12:26:40Z","creator":"sjeschke","access_level":"open_access","date_created":"2023-12-21T12:26:40Z","file_name":"PaperVideo_final.mp4"},{"date_created":"2024-01-02T09:34:27Z","file_name":"2023_ACMToG_Jeschke.pdf","access_level":"open_access","creator":"dernst","success":1,"date_updated":"2024-01-02T09:34:27Z","file_id":"14725","file_size":7469177,"checksum":"a49b2e744d5cd1276bb8b2e0ce6dc638","content_type":"application/pdf","relation":"main_file"}],"publication":"ACM Transactions on Graphics","citation":{"mla":"Jeschke, Stefan, and Chris Wojtan. “Generalizing Shallow Water Simulations with Dispersive Surface Waves.” <i>ACM Transactions on Graphics</i>, vol. 42, no. 4, 83, Association for Computing Machinery, 2023, doi:<a href=\"https://doi.org/10.1145/3592098\">10.1145/3592098</a>.","ista":"Jeschke S, Wojtan C. 2023. Generalizing shallow water simulations with dispersive surface waves. ACM Transactions on Graphics. 42(4), 83.","ieee":"S. Jeschke and C. Wojtan, “Generalizing shallow water simulations with dispersive surface waves,” <i>ACM Transactions on Graphics</i>, vol. 42, no. 4. Association for Computing Machinery, 2023.","ama":"Jeschke S, Wojtan C. Generalizing shallow water simulations with dispersive surface waves. <i>ACM Transactions on Graphics</i>. 2023;42(4). doi:<a href=\"https://doi.org/10.1145/3592098\">10.1145/3592098</a>","short":"S. Jeschke, C. Wojtan, ACM Transactions on Graphics 42 (2023).","chicago":"Jeschke, Stefan, and Chris Wojtan. “Generalizing Shallow Water Simulations with Dispersive Surface Waves.” <i>ACM Transactions on Graphics</i>. Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3592098\">https://doi.org/10.1145/3592098</a>.","apa":"Jeschke, S., &#38; Wojtan, C. (2023). Generalizing shallow water simulations with dispersive surface waves. <i>ACM Transactions on Graphics</i>. Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3592098\">https://doi.org/10.1145/3592098</a>"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","corr_author":"1","publication_identifier":{"issn":["0730-0301"],"eissn":["1557-7368"]},"abstract":[{"lang":"eng","text":"This paper introduces a novel method for simulating large bodies of water as a height field. At the start of each time step, we partition the waves into a bulk flow (which approximately satisfies the assumptions of the shallow water equations) and surface waves (which approximately satisfy the assumptions of Airy wave theory). We then solve the two wave regimes separately using appropriate state-of-the-art techniques, and re-combine the resulting wave velocities at the end of each step. This strategy leads to the first heightfield wave model capable of simulating complex interactions between both deep and shallow water effects, like the waves from a boat wake sloshing up onto a beach, or a dam break producing wave interference patterns and eddies. We also analyze the numerical dispersion created by our method and derive an exact correction factor for waves at a constant water depth, giving us a numerically perfect re-creation of theoretical water wave dispersion patterns."}],"quality_controlled":"1","publication_status":"published","title":"Generalizing shallow water simulations with dispersive surface waves","project":[{"name":"Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena","_id":"34bc2376-11ca-11ed-8bc3-9a3b3961a088","grant_number":"101045083"}],"date_updated":"2025-04-14T08:01:13Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"external_id":{"isi":["001044671300049"]},"isi":1,"date_published":"2023-08-01T00:00:00Z","article_processing_charge":"Yes (in subscription journal)","acknowledged_ssus":[{"_id":"ScienComp"}],"has_accepted_license":"1","department":[{"_id":"ChWo"}],"publisher":"Association for Computing Machinery","article_type":"original","issue":"4","day":"01","year":"2023","language":[{"iso":"eng"}],"ddc":["000"],"_id":"14240","file_date_updated":"2024-01-02T09:34:27Z","oa_version":"Published Version","status":"public","article_number":"83","oa":1,"month":"08","author":[{"full_name":"Jeschke, Stefan","last_name":"Jeschke","id":"44D6411A-F248-11E8-B48F-1D18A9856A87","first_name":"Stefan"},{"last_name":"Wojtan","full_name":"Wojtan, Christopher J","orcid":"0000-0001-6646-5546","id":"3C61F1D2-F248-11E8-B48F-1D18A9856A87","first_name":"Christopher J"}],"volume":42,"date_created":"2023-08-27T22:01:17Z","doi":"10.1145/3592098","scopus_import":"1","intvolume":"        42"},{"conference":{"name":"SIGGRAPH: Computer Graphics and Interactive Techniques Conference","start_date":"2023-08-06","location":"Los Angeles, CA, United States","end_date":"2023-08-10"},"type":"conference","acknowledgement":"The authors would like to thank Yuki Koyama and Takeo Igarashi for early discussions, and Yuta Yaguchi for support in 3D printing. This research is partially supported by the Israel Science Foundation grant number 1390/19.\r\n","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","citation":{"chicago":"Tojo, Kenji, Ariel Shamir, Bernd Bickel, and Nobuyuki Umetani. “Stealth Shaper: Reflectivity Optimization as Surface Stylization.” In <i>SIGGRAPH 2023 Conference Proceedings</i>. Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3588432.3591542\">https://doi.org/10.1145/3588432.3591542</a>.","apa":"Tojo, K., Shamir, A., Bickel, B., &#38; Umetani, N. (2023). Stealth shaper: Reflectivity optimization as surface stylization. In <i>SIGGRAPH 2023 Conference Proceedings</i>. Los Angeles, CA, United States: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3588432.3591542\">https://doi.org/10.1145/3588432.3591542</a>","short":"K. Tojo, A. Shamir, B. Bickel, N. Umetani, in:, SIGGRAPH 2023 Conference Proceedings, Association for Computing Machinery, 2023.","ieee":"K. Tojo, A. Shamir, B. Bickel, and N. Umetani, “Stealth shaper: Reflectivity optimization as surface stylization,” in <i>SIGGRAPH 2023 Conference Proceedings</i>, Los Angeles, CA, United States, 2023.","ama":"Tojo K, Shamir A, Bickel B, Umetani N. Stealth shaper: Reflectivity optimization as surface stylization. In: <i>SIGGRAPH 2023 Conference Proceedings</i>. Association for Computing Machinery; 2023. doi:<a href=\"https://doi.org/10.1145/3588432.3591542\">10.1145/3588432.3591542</a>","ista":"Tojo K, Shamir A, Bickel B, Umetani N. 2023. Stealth shaper: Reflectivity optimization as surface stylization. SIGGRAPH 2023 Conference Proceedings. SIGGRAPH: Computer Graphics and Interactive Techniques Conference, 20.","mla":"Tojo, Kenji, et al. “Stealth Shaper: Reflectivity Optimization as Surface Stylization.” <i>SIGGRAPH 2023 Conference Proceedings</i>, 20, Association for Computing Machinery, 2023, doi:<a href=\"https://doi.org/10.1145/3588432.3591542\">10.1145/3588432.3591542</a>."},"publication":"SIGGRAPH 2023 Conference Proceedings","publication_identifier":{"isbn":["9798400701597"]},"corr_author":"1","arxiv":1,"date_published":"2023-07-23T00:00:00Z","isi":1,"external_id":{"isi":["001117690500020"],"arxiv":["2305.05944"]},"date_updated":"2025-09-09T12:49:15Z","title":"Stealth shaper: Reflectivity optimization as surface stylization","publication_status":"published","abstract":[{"lang":"eng","text":"We present a technique to optimize the reflectivity of a surface while preserving its overall shape. The naïve optimization of the mesh vertices using the gradients of reflectivity simulations results in undesirable distortion. In contrast, our robust formulation optimizes the surface normal as an independent variable that bridges the reflectivity term with differential rendering, and the regularization term with as-rigid-as-possible elastic energy. We further adaptively subdivide the input mesh to improve the convergence. Consequently, our method can minimize the retroreflectivity of a wide range of input shapes, resulting in sharply creased shapes ubiquitous among stealth aircraft and Sci-Fi vehicles. Furthermore, by changing the reward for the direction of the outgoing light directions, our method can be applied to other reflectivity design tasks, such as the optimization of architectural walls to concentrate light in a specific region. We have tested the proposed method using light-transport simulations and real-world 3D-printed objects."}],"quality_controlled":"1","day":"23","publisher":"Association for Computing Machinery","department":[{"_id":"BeBi"}],"article_processing_charge":"No","language":[{"iso":"eng"}],"year":"2023","month":"07","oa":1,"article_number":"20","oa_version":"Preprint","status":"public","_id":"14241","scopus_import":"1","doi":"10.1145/3588432.3591542","date_created":"2023-08-27T22:01:17Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2305.05944"}],"author":[{"last_name":"Tojo","full_name":"Tojo, Kenji","first_name":"Kenji"},{"first_name":"Ariel","last_name":"Shamir","full_name":"Shamir, Ariel"},{"orcid":"0000-0001-6511-9385","full_name":"Bickel, Bernd","last_name":"Bickel","first_name":"Bernd","id":"49876194-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Nobuyuki","full_name":"Umetani, Nobuyuki","last_name":"Umetani"}]},{"scopus_import":"1","doi":"10.1609/aaai.v37i12.26747","intvolume":"        37","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2211.16187"}],"author":[{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","first_name":"Mathias","full_name":"Lechner, Mathias","last_name":"Lechner"},{"first_name":"Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4681-1699","last_name":"Zikelic","full_name":"Zikelic, Dorde"},{"id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu","last_name":"Chatterjee","full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X"},{"first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","last_name":"Henzinger","full_name":"Henzinger, Thomas A"},{"last_name":"Rus","full_name":"Rus, Daniela","first_name":"Daniela"}],"date_created":"2023-08-27T22:01:17Z","volume":37,"oa":1,"month":"06","oa_version":"Preprint","status":"public","_id":"14242","language":[{"iso":"eng"}],"year":"2023","page":"14964-14973","publisher":"Association for the Advancement of Artificial Intelligence","issue":"12","day":"26","article_processing_charge":"No","department":[{"_id":"ToHe"},{"_id":"KrCh"}],"date_updated":"2025-03-31T16:01:08Z","title":"Quantization-aware interval bound propagation for training certifiably robust quantized neural networks","publication_status":"published","project":[{"call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093"},{"grant_number":"863818","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications","call_identifier":"H2020"},{"grant_number":"665385","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"external_id":{"arxiv":["2211.16187"]},"date_published":"2023-06-26T00:00:00Z","abstract":[{"lang":"eng","text":"We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly adopted in industry. Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization, and certification of the quantized representation is necessary to guarantee robustness. In this work, we present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs. Inspired by advances in robust learning of non-quantized networks, our training algorithm computes the gradient of an abstract representation of the actual network. Unlike existing approaches, our method can handle the discrete semantics of QNNs. Based on QA-IBP, we also develop a complete verification procedure for verifying the adversarial robustness of QNNs, which is guaranteed to terminate and produce a correct answer. Compared to existing approaches, the key advantage of our verification procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate experimentally that our approach significantly outperforms existing methods and establish the new state-of-the-art for training and certifying the robustness of QNNs."}],"quality_controlled":"1","publication_identifier":{"isbn":["9781577358800"]},"arxiv":1,"ec_funded":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Lechner, Mathias, et al. “Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks.” <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>, vol. 37, no. 12, Association for the Advancement of Artificial Intelligence, 2023, pp. 14964–73, doi:<a href=\"https://doi.org/10.1609/aaai.v37i12.26747\">10.1609/aaai.v37i12.26747</a>.","ista":"Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. 2023. Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 37, 14964–14973.","ama":"Lechner M, Zikelic D, Chatterjee K, Henzinger TA, Rus D. Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. In: <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>. Vol 37. Association for the Advancement of Artificial Intelligence; 2023:14964-14973. doi:<a href=\"https://doi.org/10.1609/aaai.v37i12.26747\">10.1609/aaai.v37i12.26747</a>","ieee":"M. Lechner, D. Zikelic, K. Chatterjee, T. A. Henzinger, and D. Rus, “Quantization-aware interval bound propagation for training certifiably robust quantized neural networks,” in <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>, Washington, DC, United States, 2023, vol. 37, no. 12, pp. 14964–14973.","short":"M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, D. Rus, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2023, pp. 14964–14973.","apa":"Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., &#38; Rus, D. (2023). Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. In <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i> (Vol. 37, pp. 14964–14973). Washington, DC, United States: Association for the Advancement of Artificial Intelligence. <a href=\"https://doi.org/10.1609/aaai.v37i12.26747\">https://doi.org/10.1609/aaai.v37i12.26747</a>","chicago":"Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, Thomas A Henzinger, and Daniela Rus. “Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks.” In <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>, 37:14964–73. Association for the Advancement of Artificial Intelligence, 2023. <a href=\"https://doi.org/10.1609/aaai.v37i12.26747\">https://doi.org/10.1609/aaai.v37i12.26747</a>."},"publication":"Proceedings of the 37th AAAI Conference on Artificial Intelligence","acknowledgement":"This work was supported in part by the ERC-2020-AdG 101020093, ERC CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. Research was sponsored by the United\r\nStates Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-\r\n1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied,\r\nof the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright\r\nnotation herein. The research was also funded in part by the AI2050 program at Schmidt Futures (Grant G-22-63172) and Capgemini SE.","type":"conference","conference":{"start_date":"2023-02-07","location":"Washington, DC, United States","end_date":"2023-02-14","name":"AAAI: Conference on Artificial Intelligence"}},{"article_processing_charge":"No","department":[{"_id":"ToHe"},{"_id":"KrCh"}],"day":"27","issue":"5","page":"5464-5471","language":[{"iso":"eng"}],"year":"2023","oa_version":"Published Version","status":"public","_id":"14243","oa":1,"month":"06","main_file_link":[{"url":"https://doi.org/10.1609/aaai.v37i5.25679","open_access":"1"}],"author":[{"first_name":"Guy","id":"463C8BC2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-5588-8287","last_name":"Avni","full_name":"Avni, Guy"},{"full_name":"Jecker, Ismael R","last_name":"Jecker","id":"85D7C63E-7D5D-11E9-9C0F-98C4E5697425","first_name":"Ismael R"},{"full_name":"Zikelic, Dorde","last_name":"Zikelic","orcid":"0000-0002-4681-1699","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","first_name":"Dorde"}],"date_created":"2023-08-27T22:01:18Z","volume":37,"scopus_import":"1","doi":"10.1609/aaai.v37i5.25679","intvolume":"        37","type":"conference","acknowledgement":"This research was supported in part by ISF grant no.1679/21, by the ERC CoG 863818 (ForM-SMArt), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.","conference":{"start_date":"2023-02-07","location":"Washington, DC, United States","end_date":"2023-02-14","name":"AAAI: Conference on Artificial Intelligence"},"publication":"Proceedings of the 37th AAAI Conference on Artificial Intelligence","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Avni, Guy, Ismael R Jecker, and Dorde Zikelic. “Bidding Graph Games with Partially-Observable Budgets.” In <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>, 37:5464–71, 2023. <a href=\"https://doi.org/10.1609/aaai.v37i5.25679\">https://doi.org/10.1609/aaai.v37i5.25679</a>.","apa":"Avni, G., Jecker, I. R., &#38; Zikelic, D. (2023). Bidding graph games with partially-observable budgets. In <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i> (Vol. 37, pp. 5464–5471). Washington, DC, United States. <a href=\"https://doi.org/10.1609/aaai.v37i5.25679\">https://doi.org/10.1609/aaai.v37i5.25679</a>","short":"G. Avni, I.R. Jecker, D. Zikelic, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023, pp. 5464–5471.","ieee":"G. Avni, I. R. Jecker, and D. Zikelic, “Bidding graph games with partially-observable budgets,” in <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>, Washington, DC, United States, 2023, vol. 37, no. 5, pp. 5464–5471.","ama":"Avni G, Jecker IR, Zikelic D. Bidding graph games with partially-observable budgets. In: <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>. Vol 37. ; 2023:5464-5471. doi:<a href=\"https://doi.org/10.1609/aaai.v37i5.25679\">10.1609/aaai.v37i5.25679</a>","mla":"Avni, Guy, et al. “Bidding Graph Games with Partially-Observable Budgets.” <i>Proceedings of the 37th AAAI Conference on Artificial Intelligence</i>, vol. 37, no. 5, 2023, pp. 5464–71, doi:<a href=\"https://doi.org/10.1609/aaai.v37i5.25679\">10.1609/aaai.v37i5.25679</a>.","ista":"Avni G, Jecker IR, Zikelic D. 2023. Bidding graph games with partially-observable budgets. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 37, 5464–5471."},"ec_funded":1,"arxiv":1,"publication_identifier":{"isbn":["9781577358800"]},"quality_controlled":"1","abstract":[{"lang":"eng","text":"Two-player zero-sum \"graph games\" are central in logic, verification, and multi-agent systems. The game proceeds by placing a token on a vertex of a graph, and allowing the players to move it to produce an infinite path, which determines the winner or payoff of the game. Traditionally, the players alternate turns in moving the token. In \"bidding games\", however, the players have budgets and in each turn, an auction (bidding) determines which player moves the token. So far, bidding games have only been studied as full-information games. In this work we initiate the study of partial-information bidding games: we study bidding games in which a player's initial budget is drawn from a known probability distribution. We show that while for some bidding mechanisms and objectives, it is straightforward to adapt the results from the full-information setting to the partial-information setting, for others, the analysis is significantly more challenging, requires new techniques, and gives rise to interesting results. Specifically, we study games with \"mean-payoff\" objectives in combination with \"poorman\" bidding. We construct optimal strategies for a partially-informed player who plays against a fully-informed adversary. We show that, somewhat surprisingly, the \"value\" under pure strategies does not necessarily exist in such games."}],"date_updated":"2025-03-31T16:01:08Z","project":[{"grant_number":"863818","call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications"},{"grant_number":"665385","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"publication_status":"published","title":"Bidding graph games with partially-observable budgets","date_published":"2023-06-27T00:00:00Z","external_id":{"arxiv":["2211.13626"]}},{"language":[{"iso":"eng"}],"year":"2023","page":"958-1027","publisher":"Wiley","article_type":"original","day":"01","issue":"4","has_accepted_license":"1","article_processing_charge":"Yes (via OA deal)","department":[{"_id":"TaHa"}],"scopus_import":"1","doi":"10.1112/plms.12555","intvolume":"       127","author":[{"first_name":"Tamás","id":"4A0666D8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-9582-2634","full_name":"Hausel, Tamás","last_name":"Hausel"},{"last_name":"Wong","full_name":"Wong, Michael Lennox","first_name":"Michael Lennox"},{"first_name":"Dimitri","last_name":"Wyss","full_name":"Wyss, Dimitri"}],"date_created":"2023-08-27T22:01:18Z","volume":127,"oa":1,"month":"10","status":"public","oa_version":"Published Version","_id":"14244","ddc":["510"],"file_date_updated":"2024-01-30T12:56:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"Hausel, T., Wong, M. L., &#38; Wyss, D. (2023). Arithmetic and metric aspects of open de Rham spaces. <i>Proceedings of the London Mathematical Society</i>. Wiley. <a href=\"https://doi.org/10.1112/plms.12555\">https://doi.org/10.1112/plms.12555</a>","chicago":"Hausel, Tamás, Michael Lennox Wong, and Dimitri Wyss. “Arithmetic and Metric Aspects of Open de Rham Spaces.” <i>Proceedings of the London Mathematical Society</i>. Wiley, 2023. <a href=\"https://doi.org/10.1112/plms.12555\">https://doi.org/10.1112/plms.12555</a>.","short":"T. Hausel, M.L. Wong, D. Wyss, Proceedings of the London Mathematical Society 127 (2023) 958–1027.","ieee":"T. Hausel, M. L. Wong, and D. Wyss, “Arithmetic and metric aspects of open de Rham spaces,” <i>Proceedings of the London Mathematical Society</i>, vol. 127, no. 4. Wiley, pp. 958–1027, 2023.","ama":"Hausel T, Wong ML, Wyss D. Arithmetic and metric aspects of open de Rham spaces. <i>Proceedings of the London Mathematical Society</i>. 2023;127(4):958-1027. doi:<a href=\"https://doi.org/10.1112/plms.12555\">10.1112/plms.12555</a>","ista":"Hausel T, Wong ML, Wyss D. 2023. Arithmetic and metric aspects of open de Rham spaces. Proceedings of the London Mathematical Society. 127(4), 958–1027.","mla":"Hausel, Tamás, et al. “Arithmetic and Metric Aspects of Open de Rham Spaces.” <i>Proceedings of the London Mathematical Society</i>, vol. 127, no. 4, Wiley, 2023, pp. 958–1027, doi:<a href=\"https://doi.org/10.1112/plms.12555\">10.1112/plms.12555</a>."},"publication":"Proceedings of the London Mathematical Society","file":[{"success":1,"date_updated":"2024-01-30T12:56:00Z","access_level":"open_access","creator":"dernst","date_created":"2024-01-30T12:56:00Z","file_name":"2023_ProcLondonMathSoc_Hausel.pdf","content_type":"application/pdf","relation":"main_file","checksum":"2af4d2d6a8ae42f7d3fba0188e79ae82","file_size":651335,"file_id":"14910"}],"acknowledgement":"We would like to thank Gergely Bérczy, Roger Bielawski, Philip Boalch, Sergey Cherkis, Andrew Dancer, Brent Doran, Eloïse Hamilton, Frances Kirwan, Bernard Leclerc, Emmanuel Letellier, Alessia Mandini, Maxence Mayrand, András Némethi, Szilárd Szabó, and Daisuke Yamakawa for discussions related to the paper. We especially thank the referee for an extensive list of very careful comments. At various stages of this project, the authors were supported by the Advanced Grant “Arithmetic and physics of Higgs moduli spaces” no. 320593 of the European Research Council, by grant no. 153627 and NCCR SwissMAP, both funded by the Swiss National Science Foundation as well as by EPF Lausanne and IST Austria. In the final stages of this project, MLW was supported by SFB/TR 45 “Periods, moduli and arithmetic of algebraic varieties,” subproject M08-10 “Moduli of vector bundles on higher-dimensional varieties.” DW was also supported by the Fondation Sciences Mathématiques de Paris, as well as public grants overseen by the Agence national de la recherche (ANR) of France as part of the Investissements d'avenir program, under reference numbers ANR-10-LABX-0098 and ANR-15-CE40-0008 (Défigéo).","type":"journal_article","date_updated":"2025-04-14T09:12:46Z","title":"Arithmetic and metric aspects of open de Rham spaces","project":[{"grant_number":"320593","call_identifier":"FP7","name":"Arithmetic and physics of Higgs moduli spaces","_id":"25E549F4-B435-11E9-9278-68D0E5697425"},{"grant_number":"153627","_id":"25E6C798-B435-11E9-9278-68D0E5697425","name":"Arithmetic quantization of character and quiver varities"}],"publication_status":"published","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"isi":1,"external_id":{"isi":["001049312700001"],"arxiv":["1807.04057"]},"date_published":"2023-10-01T00:00:00Z","quality_controlled":"1","abstract":[{"text":"In this paper, we determine the motivic class — in particular, the weight polynomial and conjecturally the Poincaré polynomial — of the open de Rham space, defined and studied by Boalch, of certain moduli spaces of irregular meromorphic connections on the trivial rank \r\n bundle on P1. The computation is by motivic Fourier transform. We show that the result satisfies the purity conjecture, that is, it agrees with the pure part of the conjectured mixed Hodge polynomial of the corresponding wild character variety. We also identify the open de Rham spaces with quiver varieties with multiplicities of Yamakawa and Geiss–Leclerc–Schröer. We finish with constructing natural complete hyperkähler metrics on them, which in the four-dimensional cases are expected to be of type ALF.","lang":"eng"}],"publication_identifier":{"eissn":["1460-244X"],"issn":["0024-6115"]},"corr_author":"1","arxiv":1,"ec_funded":1},{"isi":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_published":"2023-07-26T00:00:00Z","external_id":{"isi":["001047690500001"],"arxiv":["1612.08215"]},"title":"Horospherical coordinates of lattice points in hyperbolic spaces: Effective counting and equidistribution","publication_status":"published","date_updated":"2024-10-09T21:06:46Z","abstract":[{"text":"We establish effective counting results for lattice points in families of domains in real, complex and quaternionic hyperbolic spaces of any dimension. The domains we focus on are defined as product sets with respect to an Iwasawa decomposition. Several natural diophantine problems can be reduced to counting lattice points in such domains. These include equidistribution of the ratio of the length of the shortest solution (x,y) to the gcd equation bx−ay=1 relative to the length of (a,b), where (a,b) ranges over primitive vectors in a disc whose radius increases, the natural analog of this problem in imaginary quadratic number fields, as well as equidistribution of integral solutions to the diophantine equation defined by an integral Lorentz form in three or more variables. We establish an effective rate of convergence for these equidistribution problems, depending on the size of the spectral gap associated with a suitable lattice subgroup in the isometry group of the relevant hyperbolic space. The main result underlying our discussion amounts to establishing effective joint equidistribution for the horospherical component and the radial component in the Iwasawa decomposition of lattice elements.","lang":"eng"}],"quality_controlled":"1","corr_author":"1","publication_identifier":{"eissn":["1945-5844"],"issn":["0030-8730"]},"arxiv":1,"citation":{"chicago":"Horesh, Tal, and Amos Nevo. “Horospherical Coordinates of Lattice Points in Hyperbolic Spaces: Effective Counting and Equidistribution.” <i>Pacific Journal of Mathematics</i>. Mathematical Sciences Publishers, 2023. <a href=\"https://doi.org/10.2140/pjm.2023.324.265\">https://doi.org/10.2140/pjm.2023.324.265</a>.","apa":"Horesh, T., &#38; Nevo, A. (2023). Horospherical coordinates of lattice points in hyperbolic spaces: Effective counting and equidistribution. <i>Pacific Journal of Mathematics</i>. Mathematical Sciences Publishers. <a href=\"https://doi.org/10.2140/pjm.2023.324.265\">https://doi.org/10.2140/pjm.2023.324.265</a>","short":"T. Horesh, A. Nevo, Pacific Journal of Mathematics 324 (2023) 265–294.","ama":"Horesh T, Nevo A. Horospherical coordinates of lattice points in hyperbolic spaces: Effective counting and equidistribution. <i>Pacific Journal of Mathematics</i>. 2023;324(2):265-294. doi:<a href=\"https://doi.org/10.2140/pjm.2023.324.265\">10.2140/pjm.2023.324.265</a>","ieee":"T. Horesh and A. Nevo, “Horospherical coordinates of lattice points in hyperbolic spaces: Effective counting and equidistribution,” <i>Pacific Journal of Mathematics</i>, vol. 324, no. 2. Mathematical Sciences Publishers, pp. 265–294, 2023.","ista":"Horesh T, Nevo A. 2023. Horospherical coordinates of lattice points in hyperbolic spaces: Effective counting and equidistribution. Pacific Journal of Mathematics. 324(2), 265–294.","mla":"Horesh, Tal, and Amos Nevo. “Horospherical Coordinates of Lattice Points in Hyperbolic Spaces: Effective Counting and Equidistribution.” <i>Pacific Journal of Mathematics</i>, vol. 324, no. 2, Mathematical Sciences Publishers, 2023, pp. 265–94, doi:<a href=\"https://doi.org/10.2140/pjm.2023.324.265\">10.2140/pjm.2023.324.265</a>."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Pacific Journal of Mathematics","file":[{"checksum":"a675b53cfb31fa46be1e879b7e77fe8c","relation":"main_file","content_type":"application/pdf","file_size":654895,"file_id":"14267","creator":"dernst","access_level":"open_access","date_updated":"2023-09-05T07:26:17Z","success":1,"file_name":"2023_PacificJourMaths_Horesh.pdf","date_created":"2023-09-05T07:26:17Z"}],"acknowledgement":"The authors thank the referee for important comments which led to significant improvements is the presentation of several results in the paper. They also thank Ami Paz for preparing the figures for this paper. Horesh thanks Ami Paz and Yakov Karasik for helpful discussions. Nevo thanks John Parker and Rene Rühr for providing some very useful references. Nevo is supported by ISF Grant No. 2095/15.","type":"journal_article","intvolume":"       324","doi":"10.2140/pjm.2023.324.265","scopus_import":"1","volume":324,"date_created":"2023-08-27T22:01:18Z","author":[{"first_name":"Tal","id":"C8B7BF48-8D81-11E9-BCA9-F536E6697425","last_name":"Horesh","full_name":"Horesh, Tal"},{"first_name":"Amos","last_name":"Nevo","full_name":"Nevo, Amos"}],"month":"07","oa":1,"_id":"14245","file_date_updated":"2023-09-05T07:26:17Z","ddc":["510"],"status":"public","oa_version":"Published Version","year":"2023","language":[{"iso":"eng"}],"page":"265-294","issue":"2","day":"26","article_type":"original","publisher":"Mathematical Sciences Publishers","department":[{"_id":"TiBr"}],"article_processing_charge":"Yes","has_accepted_license":"1"},{"year":"2023","language":[{"iso":"eng"}],"keyword":["General Physics and Astronomy"],"publisher":"Springer Nature","article_type":"original","day":"22","article_processing_charge":"Yes (via OA deal)","has_accepted_license":"1","department":[{"_id":"MiLe"}],"doi":"10.1038/s42005-023-01281-2","scopus_import":"1","intvolume":"         6","author":[{"first_name":"Fabian","full_name":"Brauneis, Fabian","last_name":"Brauneis"},{"orcid":"0000-0001-9666-3543","last_name":"Ghazaryan","full_name":"Ghazaryan, Areg","first_name":"Areg","id":"4AF46FD6-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Hammer, Hans-Werner","last_name":"Hammer","first_name":"Hans-Werner"},{"full_name":"Volosniev, Artem","last_name":"Volosniev","orcid":"0000-0003-0393-5525","id":"37D278BC-F248-11E8-B48F-1D18A9856A87","first_name":"Artem"}],"volume":6,"date_created":"2023-08-28T12:36:49Z","oa":1,"month":"08","file_date_updated":"2023-09-05T08:45:49Z","_id":"14246","ddc":["530"],"oa_version":"Published Version","status":"public","article_number":"224","citation":{"chicago":"Brauneis, Fabian, Areg Ghazaryan, Hans-Werner Hammer, and Artem Volosniev. “Emergence of a Bose Polaron in a Small Ring Threaded by the Aharonov-Bohm Flux.” <i>Communications Physics</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s42005-023-01281-2\">https://doi.org/10.1038/s42005-023-01281-2</a>.","apa":"Brauneis, F., Ghazaryan, A., Hammer, H.-W., &#38; Volosniev, A. (2023). Emergence of a Bose polaron in a small ring threaded by the Aharonov-Bohm flux. <i>Communications Physics</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s42005-023-01281-2\">https://doi.org/10.1038/s42005-023-01281-2</a>","short":"F. Brauneis, A. Ghazaryan, H.-W. Hammer, A. Volosniev, Communications Physics 6 (2023).","ieee":"F. Brauneis, A. Ghazaryan, H.-W. Hammer, and A. Volosniev, “Emergence of a Bose polaron in a small ring threaded by the Aharonov-Bohm flux,” <i>Communications Physics</i>, vol. 6. Springer Nature, 2023.","ama":"Brauneis F, Ghazaryan A, Hammer H-W, Volosniev A. Emergence of a Bose polaron in a small ring threaded by the Aharonov-Bohm flux. <i>Communications Physics</i>. 2023;6. doi:<a href=\"https://doi.org/10.1038/s42005-023-01281-2\">10.1038/s42005-023-01281-2</a>","mla":"Brauneis, Fabian, et al. “Emergence of a Bose Polaron in a Small Ring Threaded by the Aharonov-Bohm Flux.” <i>Communications Physics</i>, vol. 6, 224, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s42005-023-01281-2\">10.1038/s42005-023-01281-2</a>.","ista":"Brauneis F, Ghazaryan A, Hammer H-W, Volosniev A. 2023. Emergence of a Bose polaron in a small ring threaded by the Aharonov-Bohm flux. Communications Physics. 6, 224."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Communications Physics","file":[{"checksum":"6edfc59b0ee7dc406d0968b05236e83d","content_type":"application/pdf","relation":"main_file","file_size":855960,"file_id":"14268","creator":"dernst","access_level":"open_access","success":1,"date_updated":"2023-09-05T08:45:49Z","date_created":"2023-09-05T08:45:49Z","file_name":"2023_CommPhysics_Brauneis.pdf"}],"acknowledgement":"Open Access funding enabled and organized by Projekt DEAL.\r\nWe would like to thank Jonas Jager for sharing his data with us in the early stages of this project. We thank Joachim Brand and Ray Yang for sharing with us data from Yang et al.46. This work has received funding from the DFG Project no. 413495248 [VO 2437/1-1] (F.B., H.-W.H., A.G.V.). We acknowledge support from the Deutsche Forschungsgemeinschaft (DFG - German Research Foundation) and the Open Access Publishing Fund of the Technical University of Darmstadt.","type":"journal_article","publication_status":"published","title":"Emergence of a Bose polaron in a small ring threaded by the Aharonov-Bohm flux","date_updated":"2024-10-09T21:06:47Z","date_published":"2023-08-22T00:00:00Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"isi":1,"external_id":{"isi":["001052577500002"],"arxiv":["2301.10488"]},"abstract":[{"lang":"eng","text":"The model of a ring threaded by the Aharonov-Bohm flux underlies our understanding of a coupling between gauge potentials and matter. The typical formulation of the model is based upon a single particle picture, and should be extended when interactions with other particles become relevant. Here, we illustrate such an extension for a particle in an Aharonov-Bohm ring subject to interactions with a weakly interacting Bose gas. We show that the ground state of the system can be described using the Bose-polaron concept—a particle dressed by interactions with a bosonic environment. We connect the energy spectrum to the effective mass of the polaron, and demonstrate how to change currents in the system by tuning boson-particle interactions. Our results suggest the Aharonov-Bohm ring as a platform for studying coherence and few- to many-body crossover of quasi-particles that arise from an impurity immersed in a medium."}],"quality_controlled":"1","corr_author":"1","publication_identifier":{"issn":["2399-3650"]},"arxiv":1}]