--- _id: '13992' abstract: - lang: eng text: Understanding the chirality of molecular reaction pathways is essential for a broad range of fundamental and applied sciences. However, the current ability to probe chirality on the time scale of primary processes underlying chemical reactions remains very limited. Here, we demonstrate time-resolved photoelectron circular dichroism (TRPECD) with ultrashort circularly polarized vacuum-ultraviolet (VUV) pulses from a tabletop source. We demonstrate the capabilities of VUV-TRPECD by resolving the chirality changes in time during the photodissociation of atomic iodine from two chiral molecules. We identify several general key features of TRPECD, which include the ability to probe dynamical chirality along the complete photochemical reaction path, the sensitivity to the local chirality of the evolving scattering potential, and the influence of electron scattering off dissociating photofragments. Our results are interpreted by comparison with high-level ab-initio calculations of transient PECDs from molecular photoionization calculations. Our experimental and theoretical techniques define a general approach to femtochirality. article_number: abq2811 article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Vít full_name: Svoboda, Vít last_name: Svoboda - first_name: Niraghatam Bhargava full_name: Ram, Niraghatam Bhargava last_name: Ram - first_name: Denitsa Rangelova full_name: Baykusheva, Denitsa Rangelova id: 71b4d059-2a03-11ee-914d-dfa3beed6530 last_name: Baykusheva - first_name: Daniel full_name: Zindel, Daniel last_name: Zindel - first_name: Max D. J. full_name: Waters, Max D. J. last_name: Waters - first_name: Benjamin full_name: Spenger, Benjamin last_name: Spenger - first_name: Manuel full_name: Ochsner, Manuel last_name: Ochsner - first_name: Holger full_name: Herburger, Holger last_name: Herburger - first_name: Jürgen full_name: Stohner, Jürgen last_name: Stohner - first_name: Hans Jakob full_name: Wörner, Hans Jakob last_name: Wörner citation: ama: Svoboda V, Ram NB, Baykusheva DR, et al. Femtosecond photoelectron circular dichroism of chemical reactions. Science Advances. 2022;8(28). doi:10.1126/sciadv.abq2811 apa: Svoboda, V., Ram, N. B., Baykusheva, D. R., Zindel, D., Waters, M. D. J., Spenger, B., … Wörner, H. J. (2022). Femtosecond photoelectron circular dichroism of chemical reactions. Science Advances. American Association for the Advancement of Science. https://doi.org/10.1126/sciadv.abq2811 chicago: Svoboda, Vít, Niraghatam Bhargava Ram, Denitsa Rangelova Baykusheva, Daniel Zindel, Max D. J. Waters, Benjamin Spenger, Manuel Ochsner, Holger Herburger, Jürgen Stohner, and Hans Jakob Wörner. “Femtosecond Photoelectron Circular Dichroism of Chemical Reactions.” Science Advances. American Association for the Advancement of Science, 2022. https://doi.org/10.1126/sciadv.abq2811. ieee: V. Svoboda et al., “Femtosecond photoelectron circular dichroism of chemical reactions,” Science Advances, vol. 8, no. 28. American Association for the Advancement of Science, 2022. ista: Svoboda V, Ram NB, Baykusheva DR, Zindel D, Waters MDJ, Spenger B, Ochsner M, Herburger H, Stohner J, Wörner HJ. 2022. Femtosecond photoelectron circular dichroism of chemical reactions. Science Advances. 8(28), abq2811. mla: Svoboda, Vít, et al. “Femtosecond Photoelectron Circular Dichroism of Chemical Reactions.” Science Advances, vol. 8, no. 28, abq2811, American Association for the Advancement of Science, 2022, doi:10.1126/sciadv.abq2811. short: V. Svoboda, N.B. Ram, D.R. Baykusheva, D. Zindel, M.D.J. Waters, B. Spenger, M. Ochsner, H. Herburger, J. Stohner, H.J. Wörner, Science Advances 8 (2022). date_created: 2023-08-09T13:08:04Z date_published: 2022-07-15T00:00:00Z date_updated: 2023-08-22T07:24:01Z day: '15' doi: 10.1126/sciadv.abq2811 extern: '1' external_id: arxiv: - '2206.04099' pmid: - '35857523' intvolume: ' 8' issue: '28' keyword: - Multidisciplinary language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1126/sciadv.abq2811 month: '07' oa: 1 oa_version: Published Version pmid: 1 publication: Science Advances publication_identifier: eissn: - 2375-2548 publication_status: published publisher: American Association for the Advancement of Science quality_controlled: '1' scopus_import: '1' status: public title: Femtosecond photoelectron circular dichroism of chemical reactions type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 8 year: '2022' ... --- _id: '13993' abstract: - lang: eng text: Photoionization is a process taking place on attosecond time scales. How its properties evolve from isolated particles to the condensed phase is an open question of both fundamental and practical relevance. Here, we review recent work that has advanced the study of photoionization dynamics from atoms to molecules, clusters and the liquid phase. The first measurements of molecular photoionization delays have revealed the attosecond dynamics of electron emission from a molecular shape resonance and their sensitivity to the molecular potential. Using electron-ion coincidence spectroscopy these measurements have been extended from isolated molecules to clusters. A continuous increase of the delays with the water-cluster size has been observed up to a size of 4-5 molecules, followed by a saturation towards larger clusters. Comparison with calculations has revealed a correlation of the time delay with the spatial extension of the created electron hole. Using cylindrical liquid-microjet techniques, these measurements have also been extended to liquid water, revealing a delay relative to isolated water molecules that was very similar to the largest water clusters studied. Detailed modeling based on Monte-Carlo simulations confirmed that these delays are dominated by the contributions of the first two solvation shells, which agrees with the results of the cluster measurements. These combined results open the perspective of experimentally characterizing the delocalization of electronic wave functions in complex systems and studying their evolution on attosecond time scales. article_processing_charge: No article_type: original author: - first_name: Xiaochun full_name: Gong, Xiaochun last_name: Gong - first_name: Inga full_name: Jordan, Inga last_name: Jordan - first_name: Martin full_name: Huppert, Martin last_name: Huppert - first_name: Saijoscha full_name: Heck, Saijoscha last_name: Heck - first_name: Denitsa Rangelova full_name: Baykusheva, Denitsa Rangelova id: 71b4d059-2a03-11ee-914d-dfa3beed6530 last_name: Baykusheva - first_name: Denis full_name: Jelovina, Denis last_name: Jelovina - first_name: Axel full_name: Schild, Axel last_name: Schild - first_name: Hans Jakob full_name: Wörner, Hans Jakob last_name: Wörner citation: ama: 'Gong X, Jordan I, Huppert M, et al. Attosecond photoionization dynamics: from molecules over clusters to the liquid phase. Chimia. 2022;76(6):520-528. doi:10.2533/chimia.2022.520' apa: 'Gong, X., Jordan, I., Huppert, M., Heck, S., Baykusheva, D. R., Jelovina, D., … Wörner, H. J. (2022). Attosecond photoionization dynamics: from molecules over clusters to the liquid phase. Chimia. Swiss Chemical Society. https://doi.org/10.2533/chimia.2022.520' chicago: 'Gong, Xiaochun, Inga Jordan, Martin Huppert, Saijoscha Heck, Denitsa Rangelova Baykusheva, Denis Jelovina, Axel Schild, and Hans Jakob Wörner. “Attosecond Photoionization Dynamics: From Molecules over Clusters to the Liquid Phase.” Chimia. Swiss Chemical Society, 2022. https://doi.org/10.2533/chimia.2022.520.' ieee: 'X. Gong et al., “Attosecond photoionization dynamics: from molecules over clusters to the liquid phase,” Chimia, vol. 76, no. 6. Swiss Chemical Society, pp. 520–528, 2022.' ista: 'Gong X, Jordan I, Huppert M, Heck S, Baykusheva DR, Jelovina D, Schild A, Wörner HJ. 2022. Attosecond photoionization dynamics: from molecules over clusters to the liquid phase. Chimia. 76(6), 520–528.' mla: 'Gong, Xiaochun, et al. “Attosecond Photoionization Dynamics: From Molecules over Clusters to the Liquid Phase.” Chimia, vol. 76, no. 6, Swiss Chemical Society, 2022, pp. 520–28, doi:10.2533/chimia.2022.520.' short: X. Gong, I. Jordan, M. Huppert, S. Heck, D.R. Baykusheva, D. Jelovina, A. Schild, H.J. Wörner, Chimia 76 (2022) 520–528. date_created: 2023-08-09T13:08:15Z date_published: 2022-06-29T00:00:00Z date_updated: 2023-08-22T07:26:39Z day: '29' doi: 10.2533/chimia.2022.520 extern: '1' intvolume: ' 76' issue: '6' keyword: - General Medicine - General Chemistry language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.2533/chimia.2022.520 month: '06' oa: 1 oa_version: Published Version page: 520-528 publication: Chimia publication_identifier: eissn: - 2673-2424 issn: - 0009-4293 publication_status: published publisher: Swiss Chemical Society quality_controlled: '1' scopus_import: '1' status: public title: 'Attosecond photoionization dynamics: from molecules over clusters to the liquid phase' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 76 year: '2022' ... --- _id: '13994' abstract: - lang: eng text: "Ultrafast lasers are an increasingly important tool to control and stabilize emergent phases in quantum materials. Among a variety of possible excitation protocols, a particularly intriguing route is the direct light engineering of microscopic electronic parameters, such as the electron hopping and the local Coulomb repulsion (Hubbard \r\nU). In this work, we use time-resolved x-ray absorption spectroscopy to demonstrate the light-induced renormalization of the Hubbard U in a cuprate superconductor, La1.905Ba0.095CuO4. We show that intense femtosecond laser pulses induce a substantial redshift of the upper Hubbard band while leaving the Zhang-Rice singlet energy unaffected. By comparing the experimental data to time-dependent spectra of single- and three-band Hubbard models, we assign this effect to an approximately 140-meV reduction of the on-site Coulomb repulsion on the copper sites. Our demonstration of a dynamical Hubbard U renormalization in a copper oxide paves the way to a novel strategy for the manipulation of superconductivity and magnetism as well as to the realization of other long-range-ordered phases in light-driven quantum materials." article_number: '011013' article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Denitsa Rangelova full_name: Baykusheva, Denitsa Rangelova id: 71b4d059-2a03-11ee-914d-dfa3beed6530 last_name: Baykusheva - first_name: Hoyoung full_name: Jang, Hoyoung last_name: Jang - first_name: Ali A. full_name: Husain, Ali A. last_name: Husain - first_name: Sangjun full_name: Lee, Sangjun last_name: Lee - first_name: Sophia F. R. full_name: TenHuisen, Sophia F. R. last_name: TenHuisen - first_name: Preston full_name: Zhou, Preston last_name: Zhou - first_name: Sunwook full_name: Park, Sunwook last_name: Park - first_name: Hoon full_name: Kim, Hoon last_name: Kim - first_name: Jin-Kwang full_name: Kim, Jin-Kwang last_name: Kim - first_name: Hyeong-Do full_name: Kim, Hyeong-Do last_name: Kim - first_name: Minseok full_name: Kim, Minseok last_name: Kim - first_name: Sang-Youn full_name: Park, Sang-Youn last_name: Park - first_name: Peter full_name: Abbamonte, Peter last_name: Abbamonte - first_name: B. J. full_name: Kim, B. J. last_name: Kim - first_name: G. D. full_name: Gu, G. D. last_name: Gu - first_name: Yao full_name: Wang, Yao last_name: Wang - first_name: Matteo full_name: Mitrano, Matteo last_name: Mitrano citation: ama: Baykusheva DR, Jang H, Husain AA, et al. Ultrafast renormalization of the on-site Coulomb repulsion in a cuprate superconductor. Physical Review X. 2022;12(1). doi:10.1103/physrevx.12.011013 apa: Baykusheva, D. R., Jang, H., Husain, A. A., Lee, S., TenHuisen, S. F. R., Zhou, P., … Mitrano, M. (2022). Ultrafast renormalization of the on-site Coulomb repulsion in a cuprate superconductor. Physical Review X. American Physical Society. https://doi.org/10.1103/physrevx.12.011013 chicago: Baykusheva, Denitsa Rangelova, Hoyoung Jang, Ali A. Husain, Sangjun Lee, Sophia F. R. TenHuisen, Preston Zhou, Sunwook Park, et al. “Ultrafast Renormalization of the On-Site Coulomb Repulsion in a Cuprate Superconductor.” Physical Review X. American Physical Society, 2022. https://doi.org/10.1103/physrevx.12.011013. ieee: D. R. Baykusheva et al., “Ultrafast renormalization of the on-site Coulomb repulsion in a cuprate superconductor,” Physical Review X, vol. 12, no. 1. American Physical Society, 2022. ista: Baykusheva DR, Jang H, Husain AA, Lee S, TenHuisen SFR, Zhou P, Park S, Kim H, Kim J-K, Kim H-D, Kim M, Park S-Y, Abbamonte P, Kim BJ, Gu GD, Wang Y, Mitrano M. 2022. Ultrafast renormalization of the on-site Coulomb repulsion in a cuprate superconductor. Physical Review X. 12(1), 011013. mla: Baykusheva, Denitsa Rangelova, et al. “Ultrafast Renormalization of the On-Site Coulomb Repulsion in a Cuprate Superconductor.” Physical Review X, vol. 12, no. 1, 011013, American Physical Society, 2022, doi:10.1103/physrevx.12.011013. short: D.R. Baykusheva, H. Jang, A.A. Husain, S. Lee, S.F.R. TenHuisen, P. Zhou, S. Park, H. Kim, J.-K. Kim, H.-D. Kim, M. Kim, S.-Y. Park, P. Abbamonte, B.J. Kim, G.D. Gu, Y. Wang, M. Mitrano, Physical Review X 12 (2022). date_created: 2023-08-09T13:08:26Z date_published: 2022-01-20T00:00:00Z date_updated: 2024-10-14T12:23:26Z day: '20' doi: 10.1103/physrevx.12.011013 extern: '1' external_id: arxiv: - '2109.13229' intvolume: ' 12' issue: '1' keyword: - General Physics and Astronomy language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1103/PhysRevX.12.011013 month: '01' oa: 1 oa_version: Published Version publication: Physical Review X publication_identifier: eissn: - 2160-3308 publication_status: published publisher: American Physical Society quality_controlled: '1' scopus_import: '1' status: public title: Ultrafast renormalization of the on-site Coulomb repulsion in a cuprate superconductor type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 12 year: '2022' ... --- _id: '14093' abstract: - lang: eng text: ' We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully increasing the batch size over the course of the algorithm’s execution, which leads to computing full gradients. In contrast, the proposed method, equipped with a stochastic average gradient (SAG) estimator, requires only one sample per iteration. Nevertheless, it guarantees fast convergence rates on par with more sophisticated variance reduction techniques. In applications we put special emphasis on problems with a large number of separable constraints. Such problems are prevalent among semidefinite programming (SDP) formulations arising in machine learning and theoretical computer science. We provide numerical experiments on matrix completion, unsupervised clustering, and sparsest-cut SDPs. ' alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Gideon full_name: Dresdner, Gideon last_name: Dresdner - first_name: Maria-Luiza full_name: Vladarean, Maria-Luiza last_name: Vladarean - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Volkan full_name: Cevher, Volkan last_name: Cevher - first_name: Alp full_name: Yurtsever, Alp last_name: Yurtsever citation: ama: 'Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. Faster one-sample stochastic conditional gradient method for composite convex minimization. In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. Vol 151. ML Research Press; 2022:8439-8457.' apa: 'Dresdner, G., Vladarean, M.-L., Rätsch, G., Locatello, F., Cevher, V., & Yurtsever, A. (2022). Faster one-sample stochastic conditional gradient method for composite convex minimization. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (Vol. 151, pp. 8439–8457). Virtual: ML Research Press.' chicago: Dresdner, Gideon, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, and Alp Yurtsever. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, 151:8439–57. ML Research Press, 2022. ieee: G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, and A. Yurtsever, “ Faster one-sample stochastic conditional gradient method for composite convex minimization,” in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, Virtual, 2022, vol. 151, pp. 8439–8457. ista: 'Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. 2022. Faster one-sample stochastic conditional gradient method for composite convex minimization. Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 151, 8439–8457.' mla: Dresdner, Gideon, et al. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, vol. 151, ML Research Press, 2022, pp. 8439–57. short: G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, A. Yurtsever, in:, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2022, pp. 8439–8457. conference: end_date: 2022-03-30 location: Virtual name: 'AISTATS: Conference on Artificial Intelligence and Statistics' start_date: 2022-03-28 date_created: 2023-08-21T09:27:43Z date_published: 2022-04-01T00:00:00Z date_updated: 2023-09-06T10:28:17Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2202.13212' intvolume: ' 151' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2202.13212 month: '04' oa: 1 oa_version: Preprint page: 8439-8457 publication: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics publication_identifier: issn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: ' Faster one-sample stochastic conditional gradient method for composite convex minimization' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 151 year: '2022' ... --- _id: '14098' abstract: - lang: eng text: Magnetic fields can drastically change predictions of evolutionary models of massive stars via mass-loss quenching, magnetic braking, and efficient angular momentum transport, which we aim to quantify in this work. We use the MESA software instrument to compute an extensive main-sequence grid of stellar structure and evolution models, as well as isochrones, accounting for the effects attributed to a surface fossil magnetic field. The grid is densely populated in initial mass (3–60 M⊙), surface equatorial magnetic field strength (0–50 kG), and metallicity (representative of the Solar neighbourhood and the Magellanic Clouds). We use two magnetic braking and two chemical mixing schemes and compare the model predictions for slowly rotating, nitrogen-enriched (‘Group 2’) stars with observations in the Large Magellanic Cloud. We quantify a range of initial field strengths that allow for producing Group 2 stars and find that typical values (up to a few kG) lead to solutions. Between the subgrids, we find notable departures in surface abundances and evolutionary paths. In our magnetic models, chemical mixing is always less efficient compared to non-magnetic models due to the rapid spin-down. We identify that quasi-chemically homogeneous main sequence evolution by efficient mixing could be prevented by fossil magnetic fields. We recommend comparing this grid of evolutionary models with spectropolarimetric and spectroscopic observations with the goals of (i) revisiting the derived stellar parameters of known magnetic stars, and (ii) observationally constraining the uncertain magnetic braking and chemical mixing schemes. article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Z. full_name: Keszthelyi, Z. last_name: Keszthelyi - first_name: A. de full_name: Koter, A. de last_name: Koter - first_name: Ylva Louise Linsdotter full_name: Götberg, Ylva Louise Linsdotter id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d last_name: Götberg orcid: 0000-0002-6960-6911 - first_name: G. full_name: Meynet, G. last_name: Meynet - first_name: S. A. full_name: Brands, S. A. last_name: Brands - first_name: V. full_name: Petit, V. last_name: Petit - first_name: M. full_name: Carrington, M. last_name: Carrington - first_name: A. David-Uraz full_name: A. David-Uraz, A. David-Uraz last_name: A. David-Uraz - first_name: S. T. full_name: Geen, S. T. last_name: Geen - first_name: C. full_name: Georgy, C. last_name: Georgy - first_name: R. full_name: Hirschi, R. last_name: Hirschi - first_name: J. full_name: Puls, J. last_name: Puls - first_name: K. J. full_name: Ramalatswa, K. J. last_name: Ramalatswa - first_name: M. E. full_name: Shultz, M. E. last_name: Shultz - first_name: A. ud-Doula full_name: A. ud-Doula, A. ud-Doula last_name: A. ud-Doula citation: ama: 'Keszthelyi Z, Koter A de, Götberg YLL, et al. The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities. Monthly Notices of the Royal Astronomical Society. 2022;517(2):2028-2055. doi:10.1093/mnras/stac2598' apa: 'Keszthelyi, Z., Koter, A. de, Götberg, Y. L. L., Meynet, G., Brands, S. A., Petit, V., … A. ud-Doula, A. ud-Doula. (2022). The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities. Monthly Notices of the Royal Astronomical Society. Oxford Academic. https://doi.org/10.1093/mnras/stac2598' chicago: 'Keszthelyi, Z., A. de Koter, Ylva Louise Linsdotter Götberg, G. Meynet, S. A. Brands, V. Petit, M. Carrington, et al. “The Effects of Surface Fossil Magnetic Fields on Massive Star Evolution: IV. Grids of Models at Solar, LMC, and SMC Metallicities.” Monthly Notices of the Royal Astronomical Society. Oxford Academic, 2022. https://doi.org/10.1093/mnras/stac2598.' ieee: 'Z. Keszthelyi et al., “The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities,” Monthly Notices of the Royal Astronomical Society, vol. 517, no. 2. Oxford Academic, pp. 2028–2055, 2022.' ista: 'Keszthelyi Z, Koter A de, Götberg YLL, Meynet G, Brands SA, Petit V, Carrington M, A. David-Uraz AD-U, Geen ST, Georgy C, Hirschi R, Puls J, Ramalatswa KJ, Shultz ME, A. ud-Doula A ud-Doula. 2022. The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities. Monthly Notices of the Royal Astronomical Society. 517(2), 2028–2055.' mla: 'Keszthelyi, Z., et al. “The Effects of Surface Fossil Magnetic Fields on Massive Star Evolution: IV. Grids of Models at Solar, LMC, and SMC Metallicities.” Monthly Notices of the Royal Astronomical Society, vol. 517, no. 2, Oxford Academic, 2022, pp. 2028–55, doi:10.1093/mnras/stac2598.' short: Z. Keszthelyi, A. de Koter, Y.L.L. Götberg, G. Meynet, S.A. Brands, V. Petit, M. Carrington, A.D.-U. A. David-Uraz, S.T. Geen, C. Georgy, R. Hirschi, J. Puls, K.J. Ramalatswa, M.E. Shultz, A. ud-Doula A. ud-Doula, Monthly Notices of the Royal Astronomical Society 517 (2022) 2028–2055. date_created: 2023-08-21T10:11:21Z date_published: 2022-12-01T00:00:00Z date_updated: 2023-08-22T13:18:34Z day: '01' doi: 10.1093/mnras/stac2598 extern: '1' external_id: arxiv: - '2209.06350' intvolume: ' 517' issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1093/mnras/stac2598 month: '12' oa: 1 oa_version: Published Version page: 2028-2055 publication: Monthly Notices of the Royal Astronomical Society publication_identifier: eissn: - 1365-2966 issn: - 0035-8711 publication_status: published publisher: Oxford Academic quality_controlled: '1' scopus_import: '1' status: public title: 'The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at solar, LMC, and SMC metallicities' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 517 year: '2022' ... --- _id: '14099' abstract: - lang: eng text: Magnetism can greatly impact the evolution of stars. In some stars with OBA spectral types there is direct evidence via the Zeeman effect for stable, large-scale magnetospheres, which lead to the spin-down of the stellar surface and reduced mass loss. So far, a comprehensive grid of stellar structure and evolution models accounting for these effects was lacking. For this reason, we computed and studied models with two magnetic braking and two chemical mixing schemes in three metallicity environments with the MESA software instrument. We find notable differences between the subgrids, which affects the model predictions and thus the detailed characterisation of stars. We are able to quantify the impact of magnetic fields in terms of preventing quasi-chemically homogeneous evolution and producing slowly-rotating, nitrogen-enriched ("Group 2") stars. Our model grid is fully open access and open source. article_number: '2211.07060' article_processing_charge: No arxiv: 1 author: - first_name: Z. full_name: Keszthelyi, Z. last_name: Keszthelyi - first_name: A. de full_name: Koter, A. de last_name: Koter - first_name: Ylva Louise Linsdotter full_name: Götberg, Ylva Louise Linsdotter id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d last_name: Götberg orcid: 0000-0002-6960-6911 - first_name: G. full_name: Meynet, G. last_name: Meynet - first_name: S. A. full_name: Brands, S. A. last_name: Brands - first_name: V. full_name: Petit, V. last_name: Petit - first_name: M. full_name: Carrington, M. last_name: Carrington - first_name: A. David-Uraz full_name: A. David-Uraz, A. David-Uraz last_name: A. David-Uraz - first_name: S. T. full_name: Geen, S. T. last_name: Geen - first_name: C. full_name: Georgy, C. last_name: Georgy - first_name: R. full_name: Hirschi, R. last_name: Hirschi - first_name: J. full_name: Puls, J. last_name: Puls - first_name: K. J. full_name: Ramalatswa, K. J. last_name: Ramalatswa - first_name: M. E. full_name: Shultz, M. E. last_name: Shultz - first_name: A. ud-Doula full_name: A. ud-Doula, A. ud-Doula last_name: A. ud-Doula citation: ama: Keszthelyi Z, Koter A de, Götberg YLL, et al. Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields. arXiv. doi:10.48550/arXiv.2211.07060 apa: Keszthelyi, Z., Koter, A. de, Götberg, Y. L. L., Meynet, G., Brands, S. A., Petit, V., … A. ud-Doula, A. ud-Doula. (n.d.). Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields. arXiv. https://doi.org/10.48550/arXiv.2211.07060 chicago: Keszthelyi, Z., A. de Koter, Ylva Louise Linsdotter Götberg, G. Meynet, S. A. Brands, V. Petit, M. Carrington, et al. “Spin-down and Reduced Mass Loss in Early-Type Stars with Large-Scale Magnetic Fields.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2211.07060. ieee: Z. Keszthelyi et al., “Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields,” arXiv. . ista: Keszthelyi Z, Koter A de, Götberg YLL, Meynet G, Brands SA, Petit V, Carrington M, A. David-Uraz AD-U, Geen ST, Georgy C, Hirschi R, Puls J, Ramalatswa KJ, Shultz ME, A. ud-Doula A ud-Doula. Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields. arXiv, 2211.07060. mla: Keszthelyi, Z., et al. “Spin-down and Reduced Mass Loss in Early-Type Stars with Large-Scale Magnetic Fields.” ArXiv, 2211.07060, doi:10.48550/arXiv.2211.07060. short: Z. Keszthelyi, A. de Koter, Y.L.L. Götberg, G. Meynet, S.A. Brands, V. Petit, M. Carrington, A.D.-U. A. David-Uraz, S.T. Geen, C. Georgy, R. Hirschi, J. Puls, K.J. Ramalatswa, M.E. Shultz, A. ud-Doula A. ud-Doula, ArXiv (n.d.). date_created: 2023-08-21T10:11:37Z date_published: 2022-11-14T00:00:00Z date_updated: 2023-08-22T13:20:15Z day: '14' doi: 10.48550/arXiv.2211.07060 extern: '1' external_id: arxiv: - '2211.07060' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.07060 month: '11' oa: 1 oa_version: Submitted Version publication: arXiv publication_status: submitted status: public title: Spin-down and reduced mass loss in early-type stars with large-scale magnetic fields type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14106' abstract: - lang: eng text: "We show that deep networks trained to satisfy demographic parity often do so\r\nthrough a form of race or gender awareness, and that the more we force a network\r\nto be fair, the more accurately we can recover race or gender from the internal state\r\nof the network. Based on this observation, we investigate an alternative fairness\r\napproach: we add a second classification head to the network to explicitly predict\r\nthe protected attribute (such as race or gender) alongside the original task. After\r\ntraining the two-headed network, we enforce demographic parity by merging the\r\ntwo heads, creating a network with the same architecture as the original network.\r\nWe establish a close relationship between existing approaches and our approach\r\nby showing (1) that the decisions of a fair classifier are well-approximated by our\r\napproach, and (2) that an unfair and optimally accurate classifier can be recovered\r\nfrom a fair classifier and our second head predicting the protected attribute. We use\r\nour explicit formulation to argue that the existing fairness approaches, just as ours,\r\ndemonstrate disparate treatment and that they are likely to be unlawful in a wide\r\nrange of scenarios under US law." alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Michael full_name: Lohaus, Michael last_name: Lohaus - first_name: Matthäus full_name: Kleindessner, Matthäus last_name: Kleindessner - first_name: Krishnaram full_name: Kenthapadi, Krishnaram last_name: Kenthapadi - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Chris full_name: Russell, Chris last_name: Russell citation: ama: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads the same as one? Identifying disparate treatment in fair neural networks. In: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:16548-16562.' apa: 'Lohaus, M., Kleindessner, M., Kenthapadi, K., Locatello, F., & Russell, C. (2022). Are two heads the same as one? Identifying disparate treatment in fair neural networks. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information Processing Systems Foundation.' chicago: Lohaus, Michael, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, and Chris Russell. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” In 36th Conference on Neural Information Processing Systems, 35:16548–62. Neural Information Processing Systems Foundation, 2022. ieee: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are two heads the same as one? Identifying disparate treatment in fair neural networks,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 16548–16562. ista: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. 2022. Are two heads the same as one? Identifying disparate treatment in fair neural networks. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35, 16548–16562.' mla: Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 16548–62. short: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 16548–16562. conference: end_date: 2022-12-09 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-21T12:12:42Z date_published: 2022-12-15T00:00:00Z date_updated: 2024-10-14T12:27:01Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '2204.04440' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2204.04440 month: '12' oa: 1 oa_version: Preprint page: 16548-16562 publication: 36th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713871088' publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Are two heads the same as one? Identifying disparate treatment in fair neural networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ... --- _id: '14107' abstract: - lang: eng text: "Amodal perception requires inferring the full shape of an object that is partially occluded. This task is particularly challenging on two levels: (1) it requires more information than what is contained in the instant retina or imaging sensor, (2) it is difficult to obtain enough well-annotated amodal labels for supervision. To this end, this paper develops a new framework of\r\nSelf-supervised amodal Video object segmentation (SaVos). Our method efficiently leverages the visual information of video temporal sequences to infer the amodal mask of objects. The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.\r\nAccordingly, we derive a novel self-supervised learning paradigm that efficiently utilizes the visible object parts as the supervision to guide the training on videos. In addition to learning type prior to complete masks for known types, SaVos also learns the spatiotemporal prior, which is also useful for the amodal task and could generalize to unseen types. The proposed\r\nframework achieves the state-of-the-art performance on the synthetic amodal segmentation benchmark FISHBOWL and the real world benchmark KINS-Video-Car. Further, it lends itself well to being transferred to novel distributions using test-time adaptation, outperforming existing models even after the transfer to a new distribution." article_processing_charge: No arxiv: 1 author: - first_name: Jian full_name: Yao, Jian last_name: Yao - first_name: Yuxin full_name: Hong, Yuxin last_name: Hong - first_name: Chiyu full_name: Wang, Chiyu last_name: Wang - first_name: Tianjun full_name: Xiao, Tianjun last_name: Xiao - first_name: Tong full_name: He, Tong last_name: He - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: David full_name: Wipf, David last_name: Wipf - first_name: Yanwei full_name: Fu, Yanwei last_name: Fu - first_name: Zheng full_name: Zhang, Zheng last_name: Zhang citation: ama: 'Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation. In: 36th Conference on Neural Information Processing Systems. ; 2022. doi:10.48550/arXiv.2210.12733' apa: Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022). Self-supervised amodal video object segmentation. In 36th Conference on Neural Information Processing Systems. New Orleans, LA, United States. https://doi.org/10.48550/arXiv.2210.12733 chicago: Yao, Jian, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, and Zheng Zhang. “Self-Supervised Amodal Video Object Segmentation.” In 36th Conference on Neural Information Processing Systems, 2022. https://doi.org/10.48550/arXiv.2210.12733. ieee: J. Yao et al., “Self-supervised amodal video object segmentation,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022. ista: 'Yao J, Hong Y, Wang C, Xiao T, He T, Locatello F, Wipf D, Fu Y, Zhang Z. 2022. Self-supervised amodal video object segmentation. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.' mla: Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.” 36th Conference on Neural Information Processing Systems, 2022, doi:10.48550/arXiv.2210.12733. short: J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z. Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022. conference: end_date: 2022-12-01 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-21T12:13:25Z date_published: 2022-10-23T00:00:00Z date_updated: 2023-09-11T09:34:17Z day: '23' department: - _id: FrLo doi: 10.48550/arXiv.2210.12733 extern: '1' external_id: arxiv: - '2210.12733' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2210.12733 month: '10' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: published status: public title: Self-supervised amodal video object segmentation type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14114' abstract: - lang: eng text: Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups. article_processing_charge: No arxiv: 1 author: - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - first_name: Michael full_name: Lohaus, Michael last_name: Lohaus - first_name: Guha full_name: Balakrishnan, Guha last_name: Balakrishnan - first_name: Matthaus full_name: Kleindessner, Matthaus last_name: Kleindessner - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bernhard full_name: Scholkopf, Bernhard last_name: Scholkopf - first_name: Chris full_name: Russell, Chris last_name: Russell citation: ama: 'Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016' apa: 'Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F., Scholkopf, B., & Russell, C. (2022). Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10400–10411). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01016' chicago: 'Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner, Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10400–411. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01016.' ieee: 'D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.' ista: 'Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.' mla: 'Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–11, doi:10.1109/cvpr52688.2022.01016.' short: D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B. Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411. conference: end_date: 2022-06-24 location: New Orleans, LA, United States name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2022-06-18 date_created: 2023-08-21T12:18:00Z date_published: 2022-07-01T00:00:00Z date_updated: 2023-09-11T09:19:14Z day: '01' department: - _id: FrLo doi: 10.1109/cvpr52688.2022.01016 extern: '1' external_id: arxiv: - '2203.04913' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2203.04913 month: '07' oa: 1 oa_version: Preprint page: 10400-10411 publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition publication_identifier: eissn: - 2575-7075 isbn: - '9781665469470' issn: - 1063-6919 publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' scopus_import: '1' status: public title: 'Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14168' abstract: - lang: eng text: "Recent work has seen the development of general purpose neural architectures\r\nthat can be trained to perform tasks across diverse data modalities. General\r\npurpose models typically make few assumptions about the underlying\r\ndata-structure and are known to perform well in the large-data regime. At the\r\nsame time, there has been growing interest in modular neural architectures that\r\nrepresent the data using sparsely interacting modules. These models can be more\r\nrobust out-of-distribution, computationally efficient, and capable of\r\nsample-efficient adaptation to new data. However, they tend to make\r\ndomain-specific assumptions about the data, and present challenges in how\r\nmodule behavior (i.e., parameterization) and connectivity (i.e., their layout)\r\ncan be jointly learned. In this work, we introduce a general purpose, yet\r\nmodular neural architecture called Neural Attentive Circuits (NACs) that\r\njointly learns the parameterization and a sparse connectivity of neural modules\r\nwithout using domain knowledge. NACs are best understood as the combination of\r\ntwo systems that are jointly trained end-to-end: one that determines the module\r\nconfiguration and the other that executes it on an input. We demonstrate\r\nqualitatively that NACs learn diverse and meaningful module configurations on\r\nthe NLVR2 dataset without additional supervision. Quantitatively, we show that\r\nby incorporating modularity in this way, NACs improve upon a strong non-modular\r\nbaseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about\r\n10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that\r\nNACs can achieve an 8x speedup at inference time while losing less than 3%\r\nperformance. Finally, we find NACs to yield competitive results on diverse data\r\nmodalities spanning point-cloud classification, symbolic processing and\r\ntext-classification from ASCII bytes, thereby confirming its general purpose\r\nnature." alternative_title: - ' Advances in Neural Information Processing Systems' article_processing_charge: No arxiv: 1 author: - first_name: Nasim full_name: Rahaman, Nasim last_name: Rahaman - first_name: Martin full_name: Weiss, Martin last_name: Weiss - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Chris full_name: Pal, Chris last_name: Pal - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Li Erran full_name: Li, Li Erran last_name: Li - first_name: Nicolas full_name: Ballas, Nicolas last_name: Ballas citation: ama: 'Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: 36th Conference on Neural Information Processing Systems. Vol 35. ; 2022.' apa: Rahaman, N., Weiss, M., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B., … Ballas, N. (2022). Neural attentive circuits. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, United States. chicago: Rahaman, Nasim, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, and Nicolas Ballas. “Neural Attentive Circuits.” In 36th Conference on Neural Information Processing Systems, Vol. 35, 2022. ieee: N. Rahaman et al., “Neural attentive circuits,” in 36th Conference on Neural Information Processing Systems, New Orleans, United States, 2022, vol. 35. ista: 'Rahaman N, Weiss M, Locatello F, Pal C, Bengio Y, Schölkopf B, Li LE, Ballas N. 2022. Neural attentive circuits. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35.' mla: Rahaman, Nasim, et al. “Neural Attentive Circuits.” 36th Conference on Neural Information Processing Systems, vol. 35, 2022. short: N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E. Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems, 2022. conference: end_date: 2022-12-01 location: New Orleans, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-29 date_created: 2023-08-22T13:57:27Z date_published: 2022-10-14T00:00:00Z date_updated: 2023-09-11T09:29:09Z day: '14' department: - _id: FrLo extern: '1' external_id: arxiv: - '2210.08031' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2210.08031 month: '10' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: published status: public title: Neural attentive circuits type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ... --- _id: '14170' abstract: - lang: eng text: "The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and performance of downstream tasks in scenes with multiple objects. In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets and evaluate segmentation metrics and downstream object property prediction. In addition, we study generalization and robustness by investigating the settings where either a single object is out of distribution -- e.g., having an unseen color, texture, or shape -- or global properties of the scene are altered -- e.g., by occlusions, cropping, or increasing the number of objects. From our experimental study, we find object-centric representations to be useful for\r\ndownstream tasks and generally robust to most distribution shifts affecting objects. However, when the distribution shift affects the input in a less structured manner, robustness in terms of segmentation and downstream task performance may vary significantly across models and distribution shifts. " alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Samuele full_name: Papa, Samuele last_name: Papa - first_name: Michele De full_name: Vita, Michele De last_name: Vita - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Ole full_name: Winther, Ole last_name: Winther - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. In: Proceedings of the 39th International Conference on Machine Learning. Vol 2022. ML Research Press; :5221-5285.' apa: 'Dittadi, A., Papa, S., Vita, M. D., Schölkopf, B., Winther, O., & Locatello, F. (n.d.). Generalization and robustness implications in object-centric learning. In Proceedings of the 39th International Conference on Machine Learning (Vol. 2022, pp. 5221–5285). Baltimore, MD, United States: ML Research Press.' chicago: Dittadi, Andrea, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello. “Generalization and Robustness Implications in Object-Centric Learning.” In Proceedings of the 39th International Conference on Machine Learning, 2022:5221–85. ML Research Press, n.d. ieee: A. Dittadi, S. Papa, M. D. Vita, B. Schölkopf, O. Winther, and F. Locatello, “Generalization and robustness implications in object-centric learning,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, vol. 2022, pp. 5221–5285. ista: Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 2022, 5221–5285. mla: Dittadi, Andrea, et al. “Generalization and Robustness Implications in Object-Centric Learning.” Proceedings of the 39th International Conference on Machine Learning, vol. 2022, ML Research Press, pp. 5221–85. short: A. Dittadi, S. Papa, M.D. Vita, B. Schölkopf, O. Winther, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, n.d., pp. 5221–5285. conference: end_date: 2022-07-23 location: Baltimore, MD, United States name: International Conference on Machine Learning start_date: 2022-07-17 date_created: 2023-08-22T13:59:55Z date_published: 2022-07-22T00:00:00Z date_updated: 2023-09-11T10:08:14Z day: '22' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.00637' intvolume: ' 2022' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2107.00637 month: '07' oa: 1 oa_version: Preprint page: 5221-5285 publication: Proceedings of the 39th International Conference on Machine Learning publication_status: submitted publisher: ML Research Press quality_controlled: '1' status: public title: Generalization and robustness implications in object-centric learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2022 year: '2022' ... --- _id: '14171' abstract: - lang: eng text: "This paper demonstrates how to recover causal graphs from the score of the\r\ndata distribution in non-linear additive (Gaussian) noise models. Using score\r\nmatching algorithms as a building block, we show how to design a new generation\r\nof scalable causal discovery methods. To showcase our approach, we also propose\r\na new efficient method for approximating the score's Jacobian, enabling to\r\nrecover the causal graph. Empirically, we find that the new algorithm, called\r\nSCORE, is competitive with state-of-the-art causal discovery methods while\r\nbeing significantly faster." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Paul full_name: Rolland, Paul last_name: Rolland - first_name: Volkan full_name: Cevher, Volkan last_name: Cevher - first_name: Matthäus full_name: Kleindessner, Matthäus last_name: Kleindessner - first_name: Chris full_name: Russel, Chris last_name: Russel - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Dominik full_name: Janzing, Dominik last_name: Janzing - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal discovery of nonlinear additive noise  models. In: Proceedings of the 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:18741-18753.' apa: 'Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing, D., & Locatello, F. (2022). Score matching enables causal discovery of nonlinear additive noise  models. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 18741–18753). Baltimore, MD, United States: ML Research Press.' chicago: Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” In Proceedings of the 39th International Conference on Machine Learning, 162:18741–53. ML Research Press, 2022. ieee: P. Rolland et al., “Score matching enables causal discovery of nonlinear additive noise  models,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753. ista: Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello F. 2022. Score matching enables causal discovery of nonlinear additive noise  models. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 162, 18741–18753. mla: Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 18741–53. short: P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 18741–18753. conference: end_date: 2022-07-23 location: Baltimore, MD, United States name: International Conference on Machine Learning start_date: 2022-07-17 date_created: 2023-08-22T14:00:18Z date_published: 2022-07-22T00:00:00Z date_updated: 2023-09-11T10:14:20Z day: '22' department: - _id: FrLo extern: '1' external_id: arxiv: - '2203.04413' intvolume: ' 162' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2203.04413 month: '07' oa: 1 oa_version: Preprint page: 18741-18753 publication: Proceedings of the 39th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: Score matching enables causal discovery of nonlinear additive noise models type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 162 year: '2022' ... --- _id: '14172' abstract: - lang: eng text: "An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly infer the generative factors of variation in simple datasets (dSprites, Shapes3D, MPI3D) from controlled environments, and on our contributed CelebGlow dataset. In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models\r\nthat learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards\r\nmore realistic real-world datasets. Despite their inability to identify the correct mechanism, the models are quite modular as their ability to infer other in-distribution factors remains fairly stable, providing only a single factoris out-of-distribution. These results point to an important yet understudied problem of learning mechanistic models of observations that can facilitate\r\ngeneralization." article_processing_charge: No arxiv: 1 author: - first_name: Lukas full_name: Schott, Lukas last_name: Schott - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Chris full_name: Russell, Chris last_name: Russell - first_name: Matthias full_name: Bethge, Matthias last_name: Bethge - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Wieland full_name: Brendel, Wieland last_name: Brendel citation: ama: 'Schott L, Kügelgen J von, Träuble F, et al. Visual representation learning does not generalize strongly within the  same domain. In: 10th International Conference on Learning Representations. ; 2022.' apa: Schott, L., Kügelgen, J. von, Träuble, F., Gehler, P., Russell, C., Bethge, M., … Brendel, W. (2022). Visual representation learning does not generalize strongly within the  same domain. In 10th International Conference on Learning Representations. Virtual. chicago: Schott, Lukas, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” In 10th International Conference on Learning Representations, 2022. ieee: L. Schott et al., “Visual representation learning does not generalize strongly within the  same domain,” in 10th International Conference on Learning Representations, Virtual, 2022. ista: 'Schott L, Kügelgen J von, Träuble F, Gehler P, Russell C, Bethge M, Schölkopf B, Locatello F, Brendel W. 2022. Visual representation learning does not generalize strongly within the  same domain. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” 10th International Conference on Learning Representations, 2022. short: L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:00:50Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:40:52Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.08221' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2107.08221 month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: Visual representation learning does not generalize strongly within the same domain type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14173' abstract: - lang: eng text: "Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same\r\nexperimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and\r\nfew-shot setting. Our findings confirm that in- and out-of-distribution accuracies tend to increase jointly, but show that their relation is largely dataset-dependent, and in general more nuanced and more complex than posited by previous, smaller scale studies." alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Florian full_name: Wenzel, Florian last_name: Wenzel - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Peter Vincent full_name: Gehler, Peter Vincent last_name: Gehler - first_name: Carl-Johann Simon-Gabriel full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel last_name: Carl-Johann Simon-Gabriel - first_name: Max full_name: Horn, Max last_name: Horn - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - first_name: David full_name: Kernert, David last_name: Kernert - first_name: Chris full_name: Russell, Chris last_name: Russell - first_name: Thomas full_name: Brox, Thomas last_name: Brox - first_name: Bernt full_name: Schiele, Bernt last_name: Schiele - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Wenzel F, Dittadi A, Gehler PV, et al. Assaying out-of-distribution generalization in transfer learning. In: 36th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:7181-7198.' apa: 'Wenzel, F., Dittadi, A., Gehler, P. V., Carl-Johann Simon-Gabriel, C.-J. S.-G., Horn, M., Zietlow, D., … Locatello, F. (2022). Assaying out-of-distribution generalization in transfer learning. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 7181–7198). New Orleans, LA, United States: Neural Information Processing Systems Foundation.' chicago: Wenzel, Florian, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” In 36th Conference on Neural Information Processing Systems, 35:7181–98. Neural Information Processing Systems Foundation, 2022. ieee: F. Wenzel et al., “Assaying out-of-distribution generalization in transfer learning,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 7181–7198. ista: 'Wenzel F, Dittadi A, Gehler PV, Carl-Johann Simon-Gabriel C-JS-G, Horn M, Zietlow D, Kernert D, Russell C, Brox T, Schiele B, Schölkopf B, Locatello F. 2022. Assaying out-of-distribution generalization in transfer learning. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35, 7181–7198.' mla: Wenzel, Florian, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 7181–98. short: F. Wenzel, A. Dittadi, P.V. Gehler, C.-J.S.-G. Carl-Johann Simon-Gabriel, M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf, F. Locatello, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 7181–7198. conference: end_date: 2022-12-09 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-22T14:01:13Z date_published: 2022-12-15T00:00:00Z date_updated: 2023-09-06T10:34:43Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '2207.09239' intvolume: ' 35' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2207.09239 month: '12' oa: 1 oa_version: Preprint page: 7181-7198 publication: 36th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713871088' publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Assaying out-of-distribution generalization in transfer learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2022' ... --- _id: '14174' abstract: - lang: eng text: "Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facilitate efficient downstream learning and generalization. By training 240 representations and over 10,000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of\r\npretrained VAE-based representations affect the OOD generalization of downstream agents. We observe that many agents are surprisingly robust to realistic distribution shifts, including the challenging sim-to-real case. In addition, we find that the generalization performance of a simple downstream proxy task reliably predicts the generalization performance of our RL agents\r\nunder a wide range of OOD settings. Such proxy tasks can thus be used to select pretrained representations that will lead to agents that generalize." article_processing_charge: No arxiv: 1 author: - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Manuel full_name: Wüthrich, Manuel last_name: Wüthrich - first_name: Felix full_name: Widmaier, Felix last_name: Widmaier - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Ole full_name: Winther, Ole last_name: Winther - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Olivier full_name: Bachem, Olivier last_name: Bachem - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer citation: ama: 'Dittadi A, Träuble F, Wüthrich M, et al. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In: 10th International Conference on Learning Representations. ; 2022.' apa: Dittadi, A., Träuble, F., Wüthrich, M., Widmaier, F., Gehler, P., Winther, O., … Bauer, S. (2022). The role of pretrained representations for the OOD generalization of  reinforcement learning agents. In 10th International Conference on Learning Representations. Virtual. chicago: Dittadi, Andrea, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” In 10th International Conference on Learning Representations, 2022. ieee: A. Dittadi et al., “The role of pretrained representations for the OOD generalization of  reinforcement learning agents,” in 10th International Conference on Learning Representations, Virtual, 2022. ista: 'Dittadi A, Träuble F, Wüthrich M, Widmaier F, Gehler P, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2022. The role of pretrained representations for the OOD generalization of  reinforcement learning agents. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Dittadi, Andrea, et al. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” 10th International Conference on Learning Representations, 2022. short: A. Dittadi, F. Träuble, M. Wüthrich, F. Widmaier, P. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:02:13Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:48:36Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.05686' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2107.05686' month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: The role of pretrained representations for the OOD generalization of reinforcement learning agents type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14175' abstract: - lang: eng text: "Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions\r\nof different cues to model performance based on a variant of Shapley values. Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social\r\ninteraction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality." article_processing_charge: No arxiv: 1 author: - first_name: Osama full_name: Makansi, Osama last_name: Makansi - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Dominik full_name: Janzing, Dominik last_name: Janzing - first_name: Thomas full_name: Brox, Thomas last_name: Brox - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Makansi O, Kügelgen J von, Locatello F, et al. You mostly walk alone: Analyzing feature attribution in trajectory prediction. In: 10th International Conference on Learning Representations. ; 2022.' apa: 'Makansi, O., Kügelgen, J. von, Locatello, F., Gehler, P., Janzing, D., Brox, T., & Schölkopf, B. (2022). You mostly walk alone: Analyzing feature attribution in trajectory prediction. In 10th International Conference on Learning Representations. Virtual.' chicago: 'Makansi, Osama, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, and Bernhard Schölkopf. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” In 10th International Conference on Learning Representations, 2022.' ieee: 'O. Makansi et al., “You mostly walk alone: Analyzing feature attribution in trajectory prediction,” in 10th International Conference on Learning Representations, Virtual, 2022.' ista: 'Makansi O, Kügelgen J von, Locatello F, Gehler P, Janzing D, Brox T, Schölkopf B. 2022. You mostly walk alone: Analyzing feature attribution in trajectory prediction. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: 'Makansi, Osama, et al. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” 10th International Conference on Learning Representations, 2022.' short: O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox, B. Schölkopf, in:, 10th International Conference on Learning Representations, 2022. conference: end_date: 2022-04-29 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2022-04-25 date_created: 2023-08-22T14:02:34Z date_published: 2022-04-25T00:00:00Z date_updated: 2023-09-11T09:52:20Z day: '25' department: - _id: FrLo extern: '1' external_id: arxiv: - '2110.05304' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2110.05304 month: '04' oa: 1 oa_version: Preprint publication: 10th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: 'You mostly walk alone: Analyzing feature attribution in trajectory prediction' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14215' abstract: - lang: eng text: Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals. article_processing_charge: No arxiv: 1 author: - first_name: Nasim full_name: Rahaman, Nasim last_name: Rahaman - first_name: Martin full_name: Weiss, Martin last_name: Weiss - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Alexandre full_name: Lacoste, Alexandre last_name: Lacoste - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio - first_name: Chris full_name: Pal, Chris last_name: Pal - first_name: Li Erran full_name: Li, Li Erran last_name: Li - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Rahaman N, Weiss M, Träuble F, et al. A general purpose neural architecture for geospatial systems. In: 36th Conference on Neural Information Processing Systems.' apa: Rahaman, N., Weiss, M., Träuble, F., Locatello, F., Lacoste, A., Bengio, Y., … Schölkopf, B. (n.d.). A general purpose neural architecture for geospatial systems. In 36th Conference on Neural Information Processing Systems. New Orleans, LA, United States. chicago: Rahaman, Nasim, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, and Bernhard Schölkopf. “A General Purpose Neural Architecture for Geospatial Systems.” In 36th Conference on Neural Information Processing Systems, n.d. ieee: N. Rahaman et al., “A general purpose neural architecture for geospatial systems,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States. ista: 'Rahaman N, Weiss M, Träuble F, Locatello F, Lacoste A, Bengio Y, Pal C, Li LE, Schölkopf B. A general purpose neural architecture for geospatial systems. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.' mla: Rahaman, Nasim, et al. “A General Purpose Neural Architecture for Geospatial Systems.” 36th Conference on Neural Information Processing Systems. short: N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C. Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing Systems, n.d. conference: end_date: 2022-12-09 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2022-11-28 date_created: 2023-08-22T14:21:47Z date_published: 2022-11-04T00:00:00Z date_updated: 2023-09-13T09:35:59Z day: '04' department: - _id: FrLo extern: '1' external_id: arxiv: - '2211.02348' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.02348 month: '11' oa: 1 oa_version: Preprint publication: 36th Conference on Neural Information Processing Systems publication_status: submitted quality_controlled: '1' status: public title: A general purpose neural architecture for geospatial systems type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14220' abstract: - lang: eng text: Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings and extrapolate this skill zero-shot without any drop in performance when the number of objects changes. We consider the generic task of bringing a specific cube out of a set to a goal position. We find that previous approaches, which primarily leverage attention and graph neural network-based architectures, do not generalize their skills when the number of input objects changes while scaling as K2. We propose an alternative plug-and-play module based on relational inductive biases to overcome these limitations. Besides exceeding performances in their training environment, we show that our approach, which scales linearly in K, allows agents to extrapolate and generalize zero-shot to any new object number. article_number: '2201.13388' article_processing_charge: No arxiv: 1 author: - first_name: Davide full_name: Mambelli, Davide last_name: Mambelli - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv. doi:10.48550/arXiv.2201.13388 apa: Mambelli, D., Träuble, F., Bauer, S., Schölkopf, B., & Locatello, F. (n.d.). Compositional multi-object reinforcement learning with linear relation networks. arXiv. https://doi.org/10.48550/arXiv.2201.13388 chicago: Mambelli, Davide, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, and Francesco Locatello. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2201.13388. ieee: D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, and F. Locatello, “Compositional multi-object reinforcement learning with linear relation networks,” arXiv. . ista: Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object reinforcement learning with linear relation networks. arXiv, 2201.13388. mla: Mambelli, Davide, et al. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.” ArXiv, 2201.13388, doi:10.48550/arXiv.2201.13388. short: D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.). date_created: 2023-08-22T14:23:16Z date_published: 2022-01-31T00:00:00Z date_updated: 2024-10-14T12:27:39Z day: '31' department: - _id: FrLo doi: 10.48550/arXiv.2201.13388 extern: '1' external_id: arxiv: - '2201.13388' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2201.13388 month: '01' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Compositional multi-object reinforcement learning with linear relation networks type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14236' abstract: - lang: eng text: We show an $(1+\epsilon)$-approximation algorithm for maintaining maximum $s$-$t$ flow under $m$ edge insertions in $m^{1/2+o(1)} \epsilon^{-1/2}$ amortized update time for directed, unweighted graphs. This constitutes the first sublinear dynamic maximum flow algorithm in general sparse graphs with arbitrarily good approximation guarantee. article_number: '2211.09606' article_processing_charge: No arxiv: 1 author: - first_name: Gramoz full_name: Goranci, Gramoz last_name: Goranci - first_name: Monika H full_name: Henzinger, Monika H id: 540c9bbd-f2de-11ec-812d-d04a5be85630 last_name: Henzinger orcid: 0000-0002-5008-6530 citation: ama: Goranci G, Henzinger M. Incremental approximate maximum flow in m1/2+o(1) update time. arXiv. doi:10.48550/arXiv.2211.09606 apa: Goranci, G., & Henzinger, M. (n.d.). Incremental approximate maximum flow in m1/2+o(1) update time. arXiv. https://doi.org/10.48550/arXiv.2211.09606 chicago: Goranci, Gramoz, and Monika Henzinger. “Incremental Approximate Maximum Flow in M1/2+o(1) Update Time.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2211.09606. ieee: G. Goranci and M. Henzinger, “Incremental approximate maximum flow in m1/2+o(1) update time,” arXiv. . ista: Goranci G, Henzinger M. Incremental approximate maximum flow in m1/2+o(1) update time. arXiv, 2211.09606. mla: Goranci, Gramoz, and Monika Henzinger. “Incremental Approximate Maximum Flow in M1/2+o(1) Update Time.” ArXiv, 2211.09606, doi:10.48550/arXiv.2211.09606. short: G. Goranci, M. Henzinger, ArXiv (n.d.). date_created: 2023-08-25T15:04:29Z date_published: 2022-11-17T00:00:00Z date_updated: 2024-11-06T12:01:45Z day: '17' doi: 10.48550/arXiv.2211.09606 extern: '1' external_id: arxiv: - '2211.09606' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.09606 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Incremental approximate maximum flow in m1/2+o(1) update time type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '14248' abstract: - lang: eng text: "Recent work by Forsgård indicates that not every convex lattice polygon arises as the characteristic polygon of an affine dimer or, equivalently, an admissible oriented line arrangement on the torus in general position. We begin the classication of convex lattice polygons arising as characteristic polygons of affine dimers. We present several general constructions of new affine dimers from old, and an algorithm for finding affine dimers with prescribed polygon.\r\n\r\nWith these tools we prove that all lattice triangles, generalised parallelograms, and polygons of genus at most two admit an affine dimer." article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Daniel full_name: Holmes, Daniel id: 3a443b4c-080d-11ed-979a-feb062bdcee0 last_name: Holmes citation: ama: Holmes D. Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. 2022;5:24-51. apa: Holmes, D. (2022). Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. California State University. chicago: Holmes, Daniel. “Affine Dimers from Characteristic Polygons.” PUMP Journal of Undergraduate Research. California State University, 2022. ieee: D. Holmes, “Affine dimers from characteristic polygons,” PUMP Journal of Undergraduate Research, vol. 5. California State University, pp. 24–51, 2022. ista: Holmes D. 2022. Affine dimers from characteristic polygons. PUMP Journal of Undergraduate Research. 5, 24–51. mla: Holmes, Daniel. “Affine Dimers from Characteristic Polygons.” PUMP Journal of Undergraduate Research, vol. 5, California State University, 2022, pp. 24–51. short: D. Holmes, PUMP Journal of Undergraduate Research 5 (2022) 24–51. corr_author: '1' date_created: 2023-08-29T13:08:09Z date_published: 2022-02-13T00:00:00Z date_updated: 2024-10-09T21:06:47Z day: '13' extern: '1' external_id: arxiv: - '2110.01703' intvolume: ' 5' keyword: - dimer model - hyperplane arrangement - torus - lattice polygon language: - iso: eng main_file_link: - open_access: '1' url: https://journals.calstate.edu/pump/article/view/2711 month: '02' oa: 1 oa_version: Published Version page: 24-51 publication: PUMP Journal of Undergraduate Research publication_identifier: issn: - 2576-3725 publication_status: published publisher: California State University quality_controlled: '1' status: public title: Affine dimers from characteristic polygons type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 5 year: '2022' ...