--- _id: '14241' 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. 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" article_number: '20' article_processing_charge: No author: - first_name: Kenji full_name: Tojo, Kenji last_name: Tojo - first_name: Ariel full_name: Shamir, Ariel last_name: Shamir - first_name: Bernd full_name: Bickel, Bernd id: 49876194-F248-11E8-B48F-1D18A9856A87 last_name: Bickel orcid: 0000-0001-6511-9385 - first_name: Nobuyuki full_name: Umetani, Nobuyuki last_name: Umetani citation: ama: 'Tojo K, Shamir A, Bickel B, Umetani N. Stealth shaper: Reflectivity optimization as surface stylization. In: SIGGRAPH 2023 Conference Proceedings. Association for Computing Machinery; 2023. doi:10.1145/3588432.3591542' apa: 'Tojo, K., Shamir, A., Bickel, B., & Umetani, N. (2023). Stealth shaper: Reflectivity optimization as surface stylization. In SIGGRAPH 2023 Conference Proceedings. Los Angeles, CA, United States: Association for Computing Machinery. https://doi.org/10.1145/3588432.3591542' chicago: 'Tojo, Kenji, Ariel Shamir, Bernd Bickel, and Nobuyuki Umetani. “Stealth Shaper: Reflectivity Optimization as Surface Stylization.” In SIGGRAPH 2023 Conference Proceedings. Association for Computing Machinery, 2023. https://doi.org/10.1145/3588432.3591542.' ieee: 'K. Tojo, A. Shamir, B. Bickel, and N. Umetani, “Stealth shaper: Reflectivity optimization as surface stylization,” in SIGGRAPH 2023 Conference Proceedings, Los Angeles, CA, United States, 2023.' 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.” SIGGRAPH 2023 Conference Proceedings, 20, Association for Computing Machinery, 2023, doi:10.1145/3588432.3591542.' short: K. Tojo, A. Shamir, B. Bickel, N. Umetani, in:, SIGGRAPH 2023 Conference Proceedings, Association for Computing Machinery, 2023. conference: end_date: 2023-08-10 location: Los Angeles, CA, United States name: 'SIGGRAPH: Computer Graphics and Interactive Techniques Conference' start_date: 2023-08-06 date_created: 2023-08-27T22:01:17Z date_published: 2023-07-23T00:00:00Z date_updated: 2023-09-05T07:22:03Z day: '23' department: - _id: BeBi doi: 10.1145/3588432.3591542 external_id: arxiv: - '2305.05944' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2305.05944 month: '07' oa: 1 oa_version: Preprint publication: SIGGRAPH 2023 Conference Proceedings publication_identifier: isbn: - '9798400701597' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' scopus_import: '1' status: public title: 'Stealth shaper: Reflectivity optimization as surface stylization' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '12562' abstract: - lang: eng text: Presynaptic inputs determine the pattern of activation of postsynaptic neurons in a neural circuit. Molecular and genetic pathways that regulate the selective formation of subsets of presynaptic inputs are largely unknown, despite significant understanding of the general process of synaptogenesis. In this study, we have begun to identify such factors using the spinal monosynaptic stretch reflex circuit as a model system. In this neuronal circuit, Ia proprioceptive afferents establish monosynaptic connections with spinal motor neurons that project to the same muscle (termed homonymous connections) or muscles with related or synergistic function. However, monosynaptic connections are not formed with motor neurons innervating muscles with antagonistic functions. The ETS transcription factor ER81 (also known as ETV1) is expressed by all proprioceptive afferents, but only a small set of motor neuron pools in the lumbar spinal cord of the mouse. Here we use conditional mouse genetic techniques to eliminate Er81 expression selectively from motor neurons. We find that ablation of Er81 in motor neurons reduces synaptic inputs from proprioceptive afferents conveying information from homonymous and synergistic muscles, with no change observed in the connectivity pattern from antagonistic proprioceptive afferents. In summary, these findings suggest a role for ER81 in defined motor neuron pools to control the assembly of specific presynaptic inputs and thereby influence the profile of activation of these motor neurons. acknowledgement: The authors gratefully thank Dr. Silvia Arber, University of Basel and Friedrich Miescher Institute for Biomedical Research, for support and in whose lab the data were collected. For advice on statistical analysis, we thank Michael Bottomley from the Statistical Consulting Center, College of Science and Mathematics, Wright State University. article_processing_charge: No article_type: original author: - first_name: David R. full_name: Ladle, David R. last_name: Ladle - first_name: Simon full_name: Hippenmeyer, Simon id: 37B36620-F248-11E8-B48F-1D18A9856A87 last_name: Hippenmeyer orcid: 0000-0003-2279-1061 citation: ama: Ladle DR, Hippenmeyer S. Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons. Journal of Neurophysiology. 2023;129(3):501-512. doi:10.1152/jn.00172.2022 apa: Ladle, D. R., & Hippenmeyer, S. (2023). Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons. Journal of Neurophysiology. American Physiological Society. https://doi.org/10.1152/jn.00172.2022 chicago: Ladle, David R., and Simon Hippenmeyer. “Loss of ETV1/ER81 in Motor Neurons Leads to Reduced Monosynaptic Inputs from Proprioceptive Sensory Neurons.” Journal of Neurophysiology. American Physiological Society, 2023. https://doi.org/10.1152/jn.00172.2022. ieee: D. R. Ladle and S. Hippenmeyer, “Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons,” Journal of Neurophysiology, vol. 129, no. 3. American Physiological Society, pp. 501–512, 2023. ista: Ladle DR, Hippenmeyer S. 2023. Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons. Journal of Neurophysiology. 129(3), 501–512. mla: Ladle, David R., and Simon Hippenmeyer. “Loss of ETV1/ER81 in Motor Neurons Leads to Reduced Monosynaptic Inputs from Proprioceptive Sensory Neurons.” Journal of Neurophysiology, vol. 129, no. 3, American Physiological Society, 2023, pp. 501–12, doi:10.1152/jn.00172.2022. short: D.R. Ladle, S. Hippenmeyer, Journal of Neurophysiology 129 (2023) 501–512. date_created: 2023-02-15T14:46:14Z date_published: 2023-03-01T00:00:00Z date_updated: 2023-09-05T12:13:34Z day: '01' department: - _id: SiHi doi: 10.1152/jn.00172.2022 external_id: isi: - '000957721600001' pmid: - '36695533' intvolume: ' 129' isi: 1 issue: '3' keyword: - Physiology - General Neuroscience language: - iso: eng month: '03' oa_version: None page: 501-512 pmid: 1 publication: Journal of Neurophysiology publication_identifier: eissn: - 1522-1598 issn: - 0022-3077 publication_status: published publisher: American Physiological Society quality_controlled: '1' status: public title: Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 129 year: '2023' ... --- _id: '13310' abstract: - lang: eng text: Machine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive attributes. We present runtime verification of algorithmic fairness for systems whose models are unknown, but are assumed to have a Markov chain structure. We introduce a specification language that can model many common algorithmic fairness properties, such as demographic parity, equal opportunity, and social burden. We build monitors that observe a long sequence of events as generated by a given system, and output, after each observation, a quantitative estimate of how fair or biased the system was on that run until that point in time. The estimate is proven to be correct modulo a variable error bound and a given confidence level, where the error bound gets tighter as the observed sequence gets longer. Our monitors are of two types, and use, respectively, frequentist and Bayesian statistical inference techniques. While the frequentist monitors compute estimates that are objectively correct with respect to the ground truth, the Bayesian monitors compute estimates that are correct subject to a given prior belief about the system’s model. Using a prototype implementation, we show how we can monitor if a bank is fair in giving loans to applicants from different social backgrounds, and if a college is fair in admitting students while maintaining a reasonable financial burden on the society. Although they exhibit different theoretical complexities in certain cases, in our experiments, both frequentist and Bayesian monitors took less than a millisecond to update their verdicts after each observation. acknowledgement: 'This work is supported by the European Research Council under Grant No.: ERC-2020-AdG101020093.' alternative_title: - LNCS article_processing_charge: Yes (in subscription journal) author: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Mahyar full_name: Karimi, Mahyar id: f1dedef5-2f78-11ee-989a-c4c97bccf506 last_name: Karimi orcid: 0009-0005-0820-1696 - first_name: Konstantin full_name: Kueffner, Konstantin id: 8121a2d0-dc85-11ea-9058-af578f3b4515 last_name: Kueffner orcid: 0000-0001-8974-2542 - first_name: Kaushik full_name: Mallik, Kaushik id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598 last_name: Mallik orcid: 0000-0001-9864-7475 citation: ama: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. Monitoring algorithmic fairness. In: Computer Aided Verification. Vol 13965. Springer Nature; 2023:358–382. doi:10.1007/978-3-031-37703-7_17' apa: 'Henzinger, T. A., Karimi, M., Kueffner, K., & Mallik, K. (2023). Monitoring algorithmic fairness. In Computer Aided Verification (Vol. 13965, pp. 358–382). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37703-7_17' chicago: Henzinger, Thomas A, Mahyar Karimi, Konstantin Kueffner, and Kaushik Mallik. “Monitoring Algorithmic Fairness.” In Computer Aided Verification, 13965:358–382. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37703-7_17. ieee: T. A. Henzinger, M. Karimi, K. Kueffner, and K. Mallik, “Monitoring algorithmic fairness,” in Computer Aided Verification, Paris, France, 2023, vol. 13965, pp. 358–382. ista: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. 2023. Monitoring algorithmic fairness. Computer Aided Verification. CAV: Computer Aided Verification, LNCS, vol. 13965, 358–382.' mla: Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness.” Computer Aided Verification, vol. 13965, Springer Nature, 2023, pp. 358–382, doi:10.1007/978-3-031-37703-7_17. short: T.A. Henzinger, M. Karimi, K. Kueffner, K. Mallik, in:, Computer Aided Verification, Springer Nature, 2023, pp. 358–382. conference: end_date: 2023-07-22 location: Paris, France name: 'CAV: Computer Aided Verification' start_date: 2023-07-17 date_created: 2023-07-25T18:32:40Z date_published: 2023-07-18T00:00:00Z date_updated: 2023-09-05T15:14:00Z day: '18' ddc: - '000' department: - _id: GradSch - _id: ToHe doi: 10.1007/978-3-031-37703-7_17 ec_funded: 1 external_id: arxiv: - '2305.15979' file: - access_level: open_access checksum: ccaf94bf7d658ba012c016e11869b54c content_type: application/pdf creator: dernst date_created: 2023-07-31T08:11:20Z date_updated: 2023-07-31T08:11:20Z file_id: '13327' file_name: 2023_LNCS_CAV_HenzingerT.pdf file_size: 647760 relation: main_file success: 1 file_date_updated: 2023-07-31T08:11:20Z has_accepted_license: '1' intvolume: ' 13965' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '07' oa: 1 oa_version: Published Version page: 358–382 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: Computer Aided Verification publication_identifier: eisbn: - '9783031377037' eissn: - 1611-3349 isbn: - '9783031377020' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' status: public title: Monitoring algorithmic fairness tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 13965 year: '2023' ... --- _id: '12205' abstract: - lang: eng text: "Background: This study seeks to evaluate the impact of breast cancer (BRCA) gene status on tumor dissemination pattern, surgical outcome and survival in a multicenter cohort of paired primary ovarian cancer (pOC) and recurrent ovarian cancer (rOC).\r\n\r\nPatients and Methods: Medical records and follow-up data from 190 patients were gathered retrospectively. All patients had surgery at pOC and at least one further rOC surgery at four European high-volume centers. Patients were divided into one cohort with confirmed mutation for BRCA1 and/or BRCA2 (BRCAmut) and a second cohort with BRCA wild type or unknown (BRCAwt). Patterns of tumor presentation, surgical outcome and survival data were analyzed between the two groups.\r\n\r\nResults: Patients with BRCAmut disease were on average 4 years younger and had significantly more tumor involvement upon diagnosis. Patients with BRCAmut disease showed higher debulking rates at all stages. Multivariate analysis showed that only patient age had significant predictive value for complete tumor resection in pOC. At rOC, however, only BRCAmut status significantly correlated with optimal debulking. Patients with BRCAmut disease showed significantly prolonged overall survival (OS) by 24.3 months. Progression-free survival (PFS) was prolonged in the BRCAmut group at all stages as well, reaching statistical significance during recurrence.\r\n\r\nConclusions: Patients with BRCAmut disease showed a more aggressive course of disease with earlier onset and more extensive tumor dissemination at pOC. However, surgical outcome and OS were significantly better in patients with BRCAmut disease compared with patients with BRCAwt disease. We therefore propose to consider BRCAmut status in regard to patient selection for cytoreductive surgery, especially in rOC." acknowledgement: "E.I.B. is a Feodor Lynen fellow of the Humboldt Foundation and a participant of the Charité Clinical Scientist Program funded by the Charité Universitätsmedizin Berlin and the Berlin Institute of Health. This work was supported by European Commission’s Seventh Framework Programme under grant agreement no. 279113 (OCTIPS; www.octips.eu).\r\nOpen Access funding enabled and organized by Projekt DEAL." article_processing_charge: No article_type: original author: - first_name: Jacek full_name: Glajzer, Jacek last_name: Glajzer - first_name: Dan Cacsire full_name: Castillo-Tong, Dan Cacsire last_name: Castillo-Tong - first_name: Rolf full_name: Richter, Rolf last_name: Richter - first_name: Ignace full_name: Vergote, Ignace last_name: Vergote - first_name: Hagen full_name: Kulbe, Hagen last_name: Kulbe - first_name: Adriaan full_name: Vanderstichele, Adriaan last_name: Vanderstichele - first_name: Ilary full_name: Ruscito, Ilary last_name: Ruscito - first_name: Fabian full_name: Trillsch, Fabian last_name: Trillsch - first_name: Alexander full_name: Mustea, Alexander last_name: Mustea - first_name: Caroline full_name: Kreuzinger, Caroline id: 382077BA-F248-11E8-B48F-1D18A9856A87 last_name: Kreuzinger - first_name: Charlie full_name: Gourley, Charlie last_name: Gourley - first_name: Hani full_name: Gabra, Hani last_name: Gabra - first_name: Eliane T. full_name: Taube, Eliane T. last_name: Taube - first_name: Oliver full_name: Dorigo, Oliver last_name: Dorigo - first_name: David full_name: Horst, David last_name: Horst - first_name: Carlotta full_name: Keunecke, Carlotta last_name: Keunecke - first_name: Joanna full_name: Baum, Joanna last_name: Baum - first_name: Timothy full_name: Angelotti, Timothy last_name: Angelotti - first_name: Jalid full_name: Sehouli, Jalid last_name: Sehouli - first_name: Elena Ioana full_name: Braicu, Elena Ioana last_name: Braicu citation: ama: 'Glajzer J, Castillo-Tong DC, Richter R, et al. Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome and patient survival in primary and recurrent high-grade serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium. Annals of Surgical Oncology. 2023;30:35-45. doi:10.1245/s10434-022-12459-3' apa: 'Glajzer, J., Castillo-Tong, D. C., Richter, R., Vergote, I., Kulbe, H., Vanderstichele, A., … Braicu, E. I. (2023). Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome and patient survival in primary and recurrent high-grade serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium. Annals of Surgical Oncology. Springer Nature. https://doi.org/10.1245/s10434-022-12459-3' chicago: 'Glajzer, Jacek, Dan Cacsire Castillo-Tong, Rolf Richter, Ignace Vergote, Hagen Kulbe, Adriaan Vanderstichele, Ilary Ruscito, et al. “Impact of BRCA Mutation Status on Tumor Dissemination Pattern, Surgical Outcome and Patient Survival in Primary and Recurrent High-Grade Serous Ovarian Cancer: A Multicenter Retrospective Study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) Consortium.” Annals of Surgical Oncology. Springer Nature, 2023. https://doi.org/10.1245/s10434-022-12459-3.' ieee: 'J. Glajzer et al., “Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome and patient survival in primary and recurrent high-grade serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium,” Annals of Surgical Oncology, vol. 30. Springer Nature, pp. 35–45, 2023.' ista: 'Glajzer J, Castillo-Tong DC, Richter R, Vergote I, Kulbe H, Vanderstichele A, Ruscito I, Trillsch F, Mustea A, Kreuzinger C, Gourley C, Gabra H, Taube ET, Dorigo O, Horst D, Keunecke C, Baum J, Angelotti T, Sehouli J, Braicu EI. 2023. Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome and patient survival in primary and recurrent high-grade serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium. Annals of Surgical Oncology. 30, 35–45.' mla: 'Glajzer, Jacek, et al. “Impact of BRCA Mutation Status on Tumor Dissemination Pattern, Surgical Outcome and Patient Survival in Primary and Recurrent High-Grade Serous Ovarian Cancer: A Multicenter Retrospective Study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) Consortium.” Annals of Surgical Oncology, vol. 30, Springer Nature, 2023, pp. 35–45, doi:10.1245/s10434-022-12459-3.' short: J. Glajzer, D.C. Castillo-Tong, R. Richter, I. Vergote, H. Kulbe, A. Vanderstichele, I. Ruscito, F. Trillsch, A. Mustea, C. Kreuzinger, C. Gourley, H. Gabra, E.T. Taube, O. Dorigo, D. Horst, C. Keunecke, J. Baum, T. Angelotti, J. Sehouli, E.I. Braicu, Annals of Surgical Oncology 30 (2023) 35–45. date_created: 2023-01-16T09:44:36Z date_published: 2023-01-01T00:00:00Z date_updated: 2023-09-05T15:18:37Z day: '01' ddc: - '610' department: - _id: JoDa doi: 10.1245/s10434-022-12459-3 external_id: isi: - '000852125500006' file: - access_level: open_access checksum: 36a1200e1011f4b2155a8041d0308f34 content_type: application/pdf creator: dernst date_created: 2023-02-02T13:01:20Z date_updated: 2023-02-02T13:01:20Z file_id: '12490' file_name: 2023_AnnalsSurgicalOncology_Glajzer.pdf file_size: 365865 relation: main_file success: 1 file_date_updated: 2023-02-02T13:01:20Z has_accepted_license: '1' intvolume: ' 30' isi: 1 keyword: - Oncology - Surgery language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: 35-45 publication: Annals of Surgical Oncology publication_identifier: eissn: - 1534-4681 issn: - 1068-9265 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '12115' relation: other status: public scopus_import: '1' status: public title: 'Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome and patient survival in primary and recurrent high-grade serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 30 year: '2023' ... --- _id: '12115' acknowledgement: This work was supported by European Commission’s Seventh Framework Programme under Grant Agreement No. 279113 (OCTIPS; www.octips.eu). article_processing_charge: No article_type: original author: - first_name: Jacek full_name: Glajzer, Jacek last_name: Glajzer - first_name: Dan Cacsire full_name: Castillo-Tong, Dan Cacsire last_name: Castillo-Tong - first_name: Rolf full_name: Richter, Rolf last_name: Richter - first_name: Ignace full_name: Vergote, Ignace last_name: Vergote - first_name: Hagen full_name: Kulbe, Hagen last_name: Kulbe - first_name: Adriaan full_name: Vanderstichele, Adriaan last_name: Vanderstichele - first_name: Ilary full_name: Ruscito, Ilary last_name: Ruscito - first_name: Fabian full_name: Trillsch, Fabian last_name: Trillsch - first_name: Alexander full_name: Mustea, Alexander last_name: Mustea - first_name: Caroline full_name: Kreuzinger, Caroline id: 382077BA-F248-11E8-B48F-1D18A9856A87 last_name: Kreuzinger - first_name: Charlie full_name: Gourley, Charlie last_name: Gourley - first_name: Hani full_name: Gabra, Hani last_name: Gabra - first_name: Eliane T. full_name: Taube, Eliane T. last_name: Taube - first_name: Oliver full_name: Dorigo, Oliver last_name: Dorigo - first_name: David full_name: Horst, David last_name: Horst - first_name: Carlotta full_name: Keunecke, Carlotta last_name: Keunecke - first_name: Joanna full_name: Baum, Joanna last_name: Baum - first_name: Timothy full_name: Angelotti, Timothy last_name: Angelotti - first_name: Jalid full_name: Sehouli, Jalid last_name: Sehouli - first_name: Elena Ioana full_name: Braicu, Elena Ioana last_name: Braicu citation: ama: 'Glajzer J, Castillo-Tong DC, Richter R, et al. ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome, and patient survival in primary and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS) consortium. Annals of Surgical Oncology. 2023;30:46-47. doi:10.1245/s10434-022-12681-z' apa: 'Glajzer, J., Castillo-Tong, D. C., Richter, R., Vergote, I., Kulbe, H., Vanderstichele, A., … Braicu, E. I. (2023). ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome, and patient survival in primary and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS) consortium. Annals of Surgical Oncology. Springer Nature. https://doi.org/10.1245/s10434-022-12681-z' chicago: 'Glajzer, Jacek, Dan Cacsire Castillo-Tong, Rolf Richter, Ignace Vergote, Hagen Kulbe, Adriaan Vanderstichele, Ilary Ruscito, et al. “ASO Visual Abstract: Impact of BRCA Mutation Status on Tumor Dissemination Pattern, Surgical Outcome, and Patient Survival in Primary and Recurrent High-Grade Serous Ovarian Cancer (HGSOC). A Multicenter, Retrospective Study of the Ovarian Cancer Therapy—Innovative Models Prolong Survival (OCTIPS) Consortium.” Annals of Surgical Oncology. Springer Nature, 2023. https://doi.org/10.1245/s10434-022-12681-z.' ieee: 'J. Glajzer et al., “ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome, and patient survival in primary and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS) consortium,” Annals of Surgical Oncology, vol. 30. Springer Nature, pp. 46–47, 2023.' ista: 'Glajzer J, Castillo-Tong DC, Richter R, Vergote I, Kulbe H, Vanderstichele A, Ruscito I, Trillsch F, Mustea A, Kreuzinger C, Gourley C, Gabra H, Taube ET, Dorigo O, Horst D, Keunecke C, Baum J, Angelotti T, Sehouli J, Braicu EI. 2023. ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome, and patient survival in primary and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS) consortium. Annals of Surgical Oncology. 30, 46–47.' mla: 'Glajzer, Jacek, et al. “ASO Visual Abstract: Impact of BRCA Mutation Status on Tumor Dissemination Pattern, Surgical Outcome, and Patient Survival in Primary and Recurrent High-Grade Serous Ovarian Cancer (HGSOC). A Multicenter, Retrospective Study of the Ovarian Cancer Therapy—Innovative Models Prolong Survival (OCTIPS) Consortium.” Annals of Surgical Oncology, vol. 30, Springer Nature, 2023, pp. 46–47, doi:10.1245/s10434-022-12681-z.' short: J. Glajzer, D.C. Castillo-Tong, R. Richter, I. Vergote, H. Kulbe, A. Vanderstichele, I. Ruscito, F. Trillsch, A. Mustea, C. Kreuzinger, C. Gourley, H. Gabra, E.T. Taube, O. Dorigo, D. Horst, C. Keunecke, J. Baum, T. Angelotti, J. Sehouli, E.I. Braicu, Annals of Surgical Oncology 30 (2023) 46–47. date_created: 2023-01-12T11:56:22Z date_published: 2023-01-01T00:00:00Z date_updated: 2023-09-05T15:18:36Z day: '01' department: - _id: JoDa doi: 10.1245/s10434-022-12681-z external_id: isi: - '000879151800001' intvolume: ' 30' isi: 1 keyword: - Oncology - Surgery language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1245/s10434-022-12681-z month: '01' oa: 1 oa_version: Published Version page: 46-47 publication: Annals of Surgical Oncology publication_identifier: eissn: - 1534-4681 issn: - 1068-9265 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '12205' relation: other status: public scopus_import: '1' status: public title: 'ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome, and patient survival in primary and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS) consortium' type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 30 year: '2023' ... --- _id: '14253' abstract: - lang: eng text: Junctions between the endoplasmic reticulum (ER) and the plasma membrane (PM) are specialized membrane contacts ubiquitous in eukaryotic cells. Concentration of intracellular signaling machinery near ER-PM junctions allows these domains to serve critical roles in lipid and Ca2+ signaling and homeostasis. Subcellular compartmentalization of protein kinase A (PKA) signaling also regulates essential cellular functions, however, no specific association between PKA and ER-PM junctional domains is known. Here, we show that in brain neurons type I PKA is directed to Kv2.1 channel-dependent ER-PM junctional domains via SPHKAP, a type I PKA-specific anchoring protein. SPHKAP association with type I PKA regulatory subunit RI and ER-resident VAP proteins results in the concentration of type I PKA between stacked ER cisternae associated with ER-PM junctions. This ER-associated PKA signalosome enables reciprocal regulation between PKA and Ca2+ signaling machinery to support Ca2+ influx and excitation-transcription coupling. These data reveal that neuronal ER-PM junctions support a receptor-independent form of PKA signaling driven by membrane depolarization and intracellular Ca2+, allowing conversion of information encoded in electrical signals into biochemical changes universally recognized throughout the cell. acknowledgement: We thank Kayla Templeton and Peter Turcanu for technical assistance, Michelle Salemi for assistance with LC-MS data acquisition and analysis, Dr. Belvin Gong for advice on monoclonal antibody generation, Drs. Maria Casas Prat and Eamonn Dickson for assistance with super-resolution TIRF microscopy, Dr. Oscar Cerda for assistance with the design of TAT-FFAT peptides, Dr. Fernando Santana for helpful discussions, and Dr. Jodi Nunnari for a careful reading of our manuscript. We also thank Dr. Alan Howe, Dr. Sohum Mehta, and Dr. Jin Zhang for providing plasmids used in this study. This project was funded by NIH Grants R01NS114210 and R21NS101648 (J.S.T.), and F32NS108519 (N.C.V.). article_number: '5231' article_processing_charge: Yes article_type: original author: - first_name: Nicholas C. full_name: Vierra, Nicholas C. last_name: Vierra - first_name: Luisa full_name: Ribeiro-Silva, Luisa last_name: Ribeiro-Silva - first_name: Michael full_name: Kirmiz, Michael last_name: Kirmiz - first_name: Deborah full_name: Van Der List, Deborah last_name: Van Der List - first_name: Pradeep full_name: Bhandari, Pradeep id: 45EDD1BC-F248-11E8-B48F-1D18A9856A87 last_name: Bhandari orcid: 0000-0003-0863-4481 - first_name: Olivia A. full_name: Mack, Olivia A. last_name: Mack - first_name: James full_name: Carroll, James last_name: Carroll - first_name: Elodie full_name: Le Monnier, Elodie id: 3B59276A-F248-11E8-B48F-1D18A9856A87 last_name: Le Monnier - first_name: Sue A. full_name: Aicher, Sue A. last_name: Aicher - first_name: Ryuichi full_name: Shigemoto, Ryuichi id: 499F3ABC-F248-11E8-B48F-1D18A9856A87 last_name: Shigemoto orcid: 0000-0001-8761-9444 - first_name: James S. full_name: Trimmer, James S. last_name: Trimmer citation: ama: Vierra NC, Ribeiro-Silva L, Kirmiz M, et al. Neuronal ER-plasma membrane junctions couple excitation to Ca2+-activated PKA signaling. Nature Communications. 2023;14. doi:10.1038/s41467-023-40930-6 apa: Vierra, N. C., Ribeiro-Silva, L., Kirmiz, M., Van Der List, D., Bhandari, P., Mack, O. A., … Trimmer, J. S. (2023). Neuronal ER-plasma membrane junctions couple excitation to Ca2+-activated PKA signaling. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-023-40930-6 chicago: Vierra, Nicholas C., Luisa Ribeiro-Silva, Michael Kirmiz, Deborah Van Der List, Pradeep Bhandari, Olivia A. Mack, James Carroll, et al. “Neuronal ER-Plasma Membrane Junctions Couple Excitation to Ca2+-Activated PKA Signaling.” Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-40930-6. ieee: N. C. Vierra et al., “Neuronal ER-plasma membrane junctions couple excitation to Ca2+-activated PKA signaling,” Nature Communications, vol. 14. Springer Nature, 2023. ista: Vierra NC, Ribeiro-Silva L, Kirmiz M, Van Der List D, Bhandari P, Mack OA, Carroll J, Le Monnier E, Aicher SA, Shigemoto R, Trimmer JS. 2023. Neuronal ER-plasma membrane junctions couple excitation to Ca2+-activated PKA signaling. Nature Communications. 14, 5231. mla: Vierra, Nicholas C., et al. “Neuronal ER-Plasma Membrane Junctions Couple Excitation to Ca2+-Activated PKA Signaling.” Nature Communications, vol. 14, 5231, Springer Nature, 2023, doi:10.1038/s41467-023-40930-6. short: N.C. Vierra, L. Ribeiro-Silva, M. Kirmiz, D. Van Der List, P. Bhandari, O.A. Mack, J. Carroll, E. Le Monnier, S.A. Aicher, R. Shigemoto, J.S. Trimmer, Nature Communications 14 (2023). date_created: 2023-09-03T22:01:14Z date_published: 2023-08-26T00:00:00Z date_updated: 2023-09-06T06:53:32Z day: '26' ddc: - '570' department: - _id: RySh doi: 10.1038/s41467-023-40930-6 external_id: pmid: - '37633939' file: - access_level: open_access checksum: 6ab8aab4e957f626a09a1c73db3388fb content_type: application/pdf creator: dernst date_created: 2023-09-06T06:50:07Z date_updated: 2023-09-06T06:50:07Z file_id: '14270' file_name: 2023_NatureComm_Vierra.pdf file_size: 9412549 relation: main_file success: 1 file_date_updated: 2023-09-06T06:50:07Z has_accepted_license: '1' intvolume: ' 14' language: - iso: eng month: '08' oa: 1 oa_version: Published Version pmid: 1 publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Neuronal ER-plasma membrane junctions couple excitation to Ca2+-activated PKA signaling tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 14 year: '2023' ... --- _id: '14259' abstract: - lang: eng text: "We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several applications. Not only can the guessed strategy be applied as best-effort in cases where the game’s huge size prohibits rigorous approaches, but it can also increase the scalability of rigorous LTL synthesis in several ways. Firstly, checking whether a guessed strategy is winning is easier than constructing one. Secondly, even if the guess is wrong in some places, it can be fixed by strategy iteration faster than constructing one from scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize exploration in the most fruitful directions.\r\nIn contrast to previous works, we (i) reflect the highly structured logical information in game’s states, the so-called semantic labelling, coming from the recent LTL-to-automata translations, and (ii) learn to reflect it properly by learning from previously solved games, bringing the solving process closer to human-like reasoning." acknowledgement: This research was funded in part by the German Research Foundation (DFG) project 427755713 Group-By Objectives in Probabilistic Verification (GOPro). alternative_title: - LNCS article_processing_charge: Yes (in subscription journal) author: - first_name: Jan full_name: Kretinsky, Jan id: 44CEF464-F248-11E8-B48F-1D18A9856A87 last_name: Kretinsky orcid: 0000-0002-8122-2881 - first_name: Tobias full_name: Meggendorfer, Tobias id: b21b0c15-30a2-11eb-80dc-f13ca25802e1 last_name: Meggendorfer orcid: 0000-0002-1712-2165 - first_name: Maximilian full_name: Prokop, Maximilian last_name: Prokop - first_name: Sabine full_name: Rieder, Sabine last_name: Rieder citation: ama: 'Kretinsky J, Meggendorfer T, Prokop M, Rieder S. Guessing winning policies in LTL synthesis by semantic learning. In: 35th International Conference on Computer Aided Verification . Vol 13964. Springer Nature; 2023:390-414. doi:10.1007/978-3-031-37706-8_20' apa: 'Kretinsky, J., Meggendorfer, T., Prokop, M., & Rieder, S. (2023). Guessing winning policies in LTL synthesis by semantic learning. In 35th International Conference on Computer Aided Verification (Vol. 13964, pp. 390–414). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37706-8_20' chicago: Kretinsky, Jan, Tobias Meggendorfer, Maximilian Prokop, and Sabine Rieder. “Guessing Winning Policies in LTL Synthesis by Semantic Learning.” In 35th International Conference on Computer Aided Verification , 13964:390–414. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37706-8_20. ieee: J. Kretinsky, T. Meggendorfer, M. Prokop, and S. Rieder, “Guessing winning policies in LTL synthesis by semantic learning,” in 35th International Conference on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 390–414. ista: 'Kretinsky J, Meggendorfer T, Prokop M, Rieder S. 2023. Guessing winning policies in LTL synthesis by semantic learning. 35th International Conference on Computer Aided Verification . CAV: Computer Aided Verification, LNCS, vol. 13964, 390–414.' mla: Kretinsky, Jan, et al. “Guessing Winning Policies in LTL Synthesis by Semantic Learning.” 35th International Conference on Computer Aided Verification , vol. 13964, Springer Nature, 2023, pp. 390–414, doi:10.1007/978-3-031-37706-8_20. short: J. Kretinsky, T. Meggendorfer, M. Prokop, S. Rieder, in:, 35th International Conference on Computer Aided Verification , Springer Nature, 2023, pp. 390–414. conference: end_date: 2023-07-22 location: Paris, France name: 'CAV: Computer Aided Verification' start_date: 2023-07-17 date_created: 2023-09-03T22:01:16Z date_published: 2023-07-17T00:00:00Z date_updated: 2023-09-06T08:27:33Z day: '17' ddc: - '000' department: - _id: KrCh doi: 10.1007/978-3-031-37706-8_20 file: - access_level: open_access checksum: ed66278b61bb869e1baba3d9b9081271 content_type: application/pdf creator: dernst date_created: 2023-09-06T08:25:50Z date_updated: 2023-09-06T08:25:50Z file_id: '14276' file_name: 2023_LNCS_CAV_Kretinsky.pdf file_size: 428354 relation: main_file success: 1 file_date_updated: 2023-09-06T08:25:50Z has_accepted_license: '1' intvolume: ' 13964' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 390-414 publication: '35th International Conference on Computer Aided Verification ' publication_identifier: eissn: - 1611-3349 isbn: - '9783031377051' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Guessing winning policies in LTL synthesis by semantic learning tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 13964 year: '2023' ... --- _id: '14105' abstract: - lang: eng text: "Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline testtime adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms [4, 43], while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks:\r\nobject detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new stateof-the art for test-time domain adaptation algorithms. " article_processing_charge: No author: - first_name: Samarth full_name: Sinha, Samarth last_name: Sinha - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bernt full_name: Schiele, Bernt last_name: Schiele citation: ama: 'Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under distribution shift. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Institute of Electrical and Electronics Engineers; 2023. doi:10.1109/wacv56688.2023.00278' apa: 'Sinha, S., Gehler, P., Locatello, F., & Schiele, B. (2023). TeST: Test-time Self-Training under distribution shift. In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa, HI, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/wacv56688.2023.00278' chicago: 'Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele. “TeST: Test-Time Self-Training under Distribution Shift.” In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Institute of Electrical and Electronics Engineers, 2023. https://doi.org/10.1109/wacv56688.2023.00278.' ieee: 'S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, United States, 2023.' ista: 'Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision.' mla: 'Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.” 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023, doi:10.1109/wacv56688.2023.00278.' short: S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023. conference: end_date: 2023-01-07 location: Waikoloa, HI, United States name: 'WACV: Winter Conference on Applications of Computer Vision' start_date: 2023-01-02 date_created: 2023-08-21T12:11:38Z date_published: 2023-02-06T00:00:00Z date_updated: 2023-09-06T10:26:56Z day: '06' department: - _id: FrLo doi: 10.1109/wacv56688.2023.00278 extern: '1' external_id: arxiv: - '2209.11459' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2209.11459 month: '02' oa: 1 oa_version: Preprint publication: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision publication_identifier: eissn: - 2642-9381 isbn: - '9781665493475' publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' scopus_import: '1' status: public title: 'TeST: Test-time Self-Training under distribution shift' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14256' abstract: - lang: eng text: "Context. Space asteroseismology is revolutionizing our knowledge of the internal structure and dynamics of stars. A breakthrough is ongoing with the recent discoveries of signatures of strong magnetic fields in the core of red giant stars. The key signature for such a detection is the asymmetry these fields induce in the frequency splittings of observed dipolar mixed gravito-acoustic modes.\r\nAims. We investigate the ability of the observed asymmetries of the frequency splittings of dipolar mixed modes to constrain the geometrical properties of deep magnetic fields.\r\nMethods. We used the powerful analytical Racah-Wigner algebra used in quantum mechanics to characterize the geometrical couplings of dipolar mixed oscillation modes with various realistically plausible topologies of fossil magnetic fields. We also computed the induced perturbation of their frequencies.\r\nResults. First, in the case of an oblique magnetic dipole, we provide the exact analytical expression of the asymmetry as a function of the angle between the rotation and magnetic axes. Its value provides a direct measure of this angle. Second, considering a combination of axisymmetric dipolar and quadrupolar fields, we show how the asymmetry is blind to the unraveling of the relative strength and sign of each component. Finally, in the case of a given multipole, we show that a negative asymmetry is a signature of non-axisymmetric topologies.\r\nConclusions. Asymmetries of dipolar mixed modes provide a key bit of information on the geometrical topology of deep fossil magnetic fields, but this is insufficient on its own. Asteroseismic constraints should therefore be combined with spectropolarimetric observations and numerical simulations, which aim to predict the more probable stable large-scale geometries." acknowledgement: The authors are grateful to the referee for her/his detailed and constructive report, which has allowed us to improve our article. S. M. acknowledges support from the CNES GOLF-SOHO and PLATO grants at CEA/DAp and PNPS (CNRS/INSU). We thank R. A. Garcia for fruitful discussions and suggestions. article_number: L9 article_processing_charge: Yes (in subscription journal) article_type: letter_note author: - first_name: S. full_name: Mathis, S. last_name: Mathis - first_name: Lisa Annabelle full_name: Bugnet, Lisa Annabelle id: d9edb345-f866-11ec-9b37-d119b5234501 last_name: Bugnet orcid: 0000-0003-0142-4000 citation: ama: 'Mathis S, Bugnet LA. Asymmetries of frequency splittings of dipolar mixed modes: A window on the topology of deep magnetic fields. Astronomy and Astrophysics. 2023;676. doi:10.1051/0004-6361/202346832' apa: 'Mathis, S., & Bugnet, L. A. (2023). Asymmetries of frequency splittings of dipolar mixed modes: A window on the topology of deep magnetic fields. Astronomy and Astrophysics. EDP Sciences. https://doi.org/10.1051/0004-6361/202346832' chicago: 'Mathis, S., and Lisa Annabelle Bugnet. “Asymmetries of Frequency Splittings of Dipolar Mixed Modes: A Window on the Topology of Deep Magnetic Fields.” Astronomy and Astrophysics. EDP Sciences, 2023. https://doi.org/10.1051/0004-6361/202346832.' ieee: 'S. Mathis and L. A. Bugnet, “Asymmetries of frequency splittings of dipolar mixed modes: A window on the topology of deep magnetic fields,” Astronomy and Astrophysics, vol. 676. EDP Sciences, 2023.' ista: 'Mathis S, Bugnet LA. 2023. Asymmetries of frequency splittings of dipolar mixed modes: A window on the topology of deep magnetic fields. Astronomy and Astrophysics. 676, L9.' mla: 'Mathis, S., and Lisa Annabelle Bugnet. “Asymmetries of Frequency Splittings of Dipolar Mixed Modes: A Window on the Topology of Deep Magnetic Fields.” Astronomy and Astrophysics, vol. 676, L9, EDP Sciences, 2023, doi:10.1051/0004-6361/202346832.' short: S. Mathis, L.A. Bugnet, Astronomy and Astrophysics 676 (2023). date_created: 2023-09-03T22:01:15Z date_published: 2023-08-01T00:00:00Z date_updated: 2023-09-06T11:05:58Z day: '01' ddc: - '520' department: - _id: LiBu doi: 10.1051/0004-6361/202346832 external_id: arxiv: - '2306.11587' isi: - '001046037700007' file: - access_level: open_access checksum: 7b30d26fb2b7bcb5b5be1414950615f9 content_type: application/pdf creator: dernst date_created: 2023-09-06T07:13:19Z date_updated: 2023-09-06T07:13:19Z file_id: '14271' file_name: 2023_AstronomyAstrophysics_Mathis.pdf file_size: 458120 relation: main_file success: 1 file_date_updated: 2023-09-06T07:13:19Z has_accepted_license: '1' intvolume: ' 676' isi: 1 language: - iso: eng month: '08' oa: 1 oa_version: Published Version publication: Astronomy and Astrophysics publication_identifier: eissn: - 1432-0746 issn: - 0004-6361 publication_status: published publisher: EDP Sciences quality_controlled: '1' scopus_import: '1' status: public title: 'Asymmetries of frequency splittings of dipolar mixed modes: A window on the topology of deep magnetic fields' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 676 year: '2023' ... --- _id: '14261' abstract: - lang: eng text: In this work, a generalized, adapted Numerov implementation capable of determining band structures of periodic quantum systems is outlined. Based on the input potential, the presented approach numerically solves the Schrödinger equation in position space at each momentum space point. Thus, in addition to the band structure, the method inherently provides information about the state functions and probability densities in position space at each momentum space point considered. The generalized, adapted Numerov framework provided reliable estimates for a variety of increasingly complex test suites in one, two, and three dimensions. The accuracy of the proposed methodology was benchmarked against results obtained for the analytically solvable Kronig-Penney model. Furthermore, the presented numerical solver was applied to a model potential representing a 2D optical lattice being a challenging application relevant, for example, in the field of quantum computing. acknowledgement: Financial supports for this work via a PhD scholarship for J. Gamper issued by the Leopold-Franzens-University of Innsbruck (Vicerector Prof. Dr Ulrike Tanzer) are gratefully acknowledged. The computational results presented have been achieved (in part) using the HPC infrastructure of the University of Innsbruck. article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Jakob full_name: Gamper, Jakob last_name: Gamper - first_name: Florian full_name: Kluibenschedl, Florian id: 7499e70e-eb2c-11ec-b98b-f925648bc9d9 last_name: Kluibenschedl - first_name: Alexander K.H. full_name: Weiss, Alexander K.H. last_name: Weiss - first_name: Thomas S. full_name: Hofer, Thomas S. last_name: Hofer citation: ama: Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. Accessing position space wave functions in band structure calculations of periodic systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional quantum problems. Journal of Physical Chemistry Letters. 2023;14(33):7395-7403. doi:10.1021/acs.jpclett.3c01707 apa: Gamper, J., Kluibenschedl, F., Weiss, A. K. H., & Hofer, T. S. (2023). Accessing position space wave functions in band structure calculations of periodic systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional quantum problems. Journal of Physical Chemistry Letters. American Chemical Society. https://doi.org/10.1021/acs.jpclett.3c01707 chicago: Gamper, Jakob, Florian Kluibenschedl, Alexander K.H. Weiss, and Thomas S. Hofer. “Accessing Position Space Wave Functions in Band Structure Calculations of Periodic Systems - a Generalized, Adapted Numerov Implementation for One-, Two-, and Three-Dimensional Quantum Problems.” Journal of Physical Chemistry Letters. American Chemical Society, 2023. https://doi.org/10.1021/acs.jpclett.3c01707. ieee: J. Gamper, F. Kluibenschedl, A. K. H. Weiss, and T. S. Hofer, “Accessing position space wave functions in band structure calculations of periodic systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional quantum problems,” Journal of Physical Chemistry Letters, vol. 14, no. 33. American Chemical Society, pp. 7395–7403, 2023. ista: Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. 2023. Accessing position space wave functions in band structure calculations of periodic systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional quantum problems. Journal of Physical Chemistry Letters. 14(33), 7395–7403. mla: Gamper, Jakob, et al. “Accessing Position Space Wave Functions in Band Structure Calculations of Periodic Systems - a Generalized, Adapted Numerov Implementation for One-, Two-, and Three-Dimensional Quantum Problems.” Journal of Physical Chemistry Letters, vol. 14, no. 33, American Chemical Society, 2023, pp. 7395–403, doi:10.1021/acs.jpclett.3c01707. short: J. Gamper, F. Kluibenschedl, A.K.H. Weiss, T.S. Hofer, Journal of Physical Chemistry Letters 14 (2023) 7395–7403. date_created: 2023-09-03T22:01:16Z date_published: 2023-08-11T00:00:00Z date_updated: 2023-09-06T11:04:31Z day: '11' ddc: - '530' - '540' department: - _id: GradSch doi: 10.1021/acs.jpclett.3c01707 external_id: isi: - '001048165800001' pmid: - '37566743' file: - access_level: open_access checksum: 637454e2b3a357498d8d622d241c4bf6 content_type: application/pdf creator: dernst date_created: 2023-09-06T07:32:39Z date_updated: 2023-09-06T07:32:39Z file_id: '14272' file_name: 2023_JourPhysChemistry_Gamper.pdf file_size: 4986859 relation: main_file success: 1 file_date_updated: 2023-09-06T07:32:39Z has_accepted_license: '1' intvolume: ' 14' isi: 1 issue: '33' language: - iso: eng month: '08' oa: 1 oa_version: Published Version page: 7395-7403 pmid: 1 publication: Journal of Physical Chemistry Letters publication_identifier: eissn: - 1948-7185 publication_status: published publisher: American Chemical Society quality_controlled: '1' scopus_import: '1' status: public title: Accessing position space wave functions in band structure calculations of periodic systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional quantum problems tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 14 year: '2023' ... --- _id: '14208' 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. alternative_title: - PMLR article_processing_charge: No author: - first_name: Zhenyu full_name: Zhu, Zhenyu last_name: Zhu - first_name: Fanghui full_name: Liu, Fanghui last_name: Liu - first_name: Grigorios G full_name: Chrysos, Grigorios G last_name: Chrysos - 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 citation: ama: 'Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep neural networks under lazy training. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:43105-43128.' apa: 'Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., & Cevher, V. (2023). Benign overfitting in deep neural networks under lazy training. In Proceedings of the 40th International Conference on Machine Learning (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 Proceedings of the 40th International Conference on Machine Learning, 202:43105–28. ML Research Press, 2023. ieee: Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting in deep neural networks under lazy training,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, United States, 2023, vol. 202, pp. 43105–43128. 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. mla: Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy Training.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 43105–28. 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. conference: end_date: 2023-07-29 location: Honolulu, Hawaii, United States name: International Conference on Machine Learning start_date: 2023-07-23 date_created: 2023-08-22T14:18:18Z date_published: 2023-05-30T00:00:00Z date_updated: 2023-09-13T08:46:46Z day: '30' department: - _id: FrLo extern: '1' external_id: arxiv: - '2305.19377' intvolume: ' 202' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2305.19377 month: '05' oa: 1 oa_version: Preprint page: 43105-43128 publication: Proceedings of the 40th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: Benign overfitting in deep neural networks under lazy training type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 202 year: '2023' ... --- _id: '14209' abstract: - lang: eng 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. article_number: '2304.10253' article_processing_charge: No author: - first_name: Max F. full_name: Burg, Max F. last_name: Burg - first_name: Florian full_name: Wenzel, Florian last_name: Wenzel - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - first_name: Max full_name: Horn, Max last_name: Horn - first_name: Osama full_name: Makansi, Osama last_name: Makansi - 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: Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253 apa: Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., & Russell, C. (n.d.). A data augmentation perspective on diffusion models and retrieval. arXiv. https://doi.org/10.48550/arXiv.2304.10253 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.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2304.10253. ieee: M. F. Burg et al., “A data augmentation perspective on diffusion models and retrieval,” arXiv. . 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. mla: Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” ArXiv, 2304.10253, doi:10.48550/arXiv.2304.10253. short: M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.). date_created: 2023-08-22T14:18:43Z date_published: 2023-04-20T00:00:00Z date_updated: 2023-09-13T08:51:56Z day: '20' department: - _id: FrLo doi: 10.48550/arXiv.2304.10253 extern: '1' external_id: arxiv: - '2304.10253' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2304.10253 month: '04' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: A data augmentation perspective on diffusion models and retrieval type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14211' abstract: - lang: eng 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.' article_processing_charge: No author: - first_name: Francesco full_name: Montagna, Francesco last_name: Montagna - first_name: Nicoletta full_name: Noceti, Nicoletta last_name: Noceti - first_name: Lorenzo full_name: Rosasco, Lorenzo last_name: Rosasco - first_name: Kun full_name: Zhang, Kun last_name: Zhang - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with score matching on additive models with arbitrary noise. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.' apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023). Causal discovery with score matching on additive models with arbitrary noise. In 2nd Conference on Causal Learning and Reasoning. 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 2nd Conference on Causal Learning and Reasoning, 2023. ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery with score matching on additive models with arbitrary noise,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 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.' mla: Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” 2nd Conference on Causal Learning and Reasoning, 2023. short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023. conference: end_date: 2023-04-14 location: Tübingen, Germany name: 'CLeaR: Conference on Causal Learning and Reasoning' start_date: 2023-04-11 date_created: 2023-08-22T14:19:21Z date_published: 2023-04-01T00:00:00Z date_updated: 2023-09-13T09:00:31Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2304.03265' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2304.03265 month: '04' oa: 1 oa_version: Preprint publication: 2nd Conference on Causal Learning and Reasoning publication_status: published quality_controlled: '1' scopus_import: '1' status: public title: Causal discovery with score matching on additive models with arbitrary noise type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14212' abstract: - lang: eng 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. article_processing_charge: No author: - first_name: Francesco full_name: Montagna, Francesco last_name: Montagna - first_name: Nicoletta full_name: Noceti, Nicoletta last_name: Noceti - first_name: Lorenzo full_name: Rosasco, Lorenzo last_name: Rosasco - first_name: Kun full_name: Zhang, Kun last_name: Zhang - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery with score matching. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.' apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023). Scalable causal discovery with score matching. In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany. chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In 2nd Conference on Causal Learning and Reasoning, 2023. ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable causal discovery with score matching,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023. 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.” 2nd Conference on Causal Learning and Reasoning, 2023. short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023. conference: end_date: 2023-04-14 location: Tübingen, Germany name: 'CLeaR: Conference on Causal Learning and Reasoning' start_date: 2023-04-11 date_created: 2023-08-22T14:19:40Z date_published: 2023-04-01T00:00:00Z date_updated: 2023-09-13T09:03:24Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2304.03382' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2304.03382 month: '04' oa: 1 oa_version: Preprint publication: 2nd Conference on Causal Learning and Reasoning publication_status: published quality_controlled: '1' scopus_import: '1' status: public title: Scalable causal discovery with score matching type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14214' abstract: - lang: eng 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.' article_processing_charge: No author: - first_name: Yuejiang full_name: Liu, Yuejiang last_name: Liu - first_name: Alexandre full_name: Alahi, Alexandre last_name: Alahi - first_name: Chris full_name: Russell, Chris last_name: Russell - first_name: Max full_name: Horn, Max last_name: Horn - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - 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: 'Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.' apa: '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. In 2nd Conference on Causal Learning and Reasoning. 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 2nd Conference on Causal Learning and Reasoning, 2023.' ieee: 'Y. Liu et al., “Causal triplet: An open challenge for intervention-centric causal representation learning,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 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.” 2nd Conference on Causal Learning and Reasoning, 2023.' short: Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023. conference: end_date: 2023-04-14 location: Tübingen, Germany name: 'CLeaR: Conference on Causal Learning and Reasoning' start_date: 2023-04-11 date_created: 2023-08-22T14:20:18Z date_published: 2023-04-12T00:00:00Z date_updated: 2023-09-13T09:23:08Z day: '12' department: - _id: FrLo extern: '1' external_id: arxiv: - '2301.05169' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2301.05169 month: '04' oa: 1 oa_version: Preprint publication: 2nd Conference on Causal Learning and Reasoning publication_status: published quality_controlled: '1' status: public title: 'Causal triplet: An open challenge for intervention-centric causal representation learning' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14217' 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).' article_processing_charge: No author: - first_name: Luca full_name: Moschella, Luca last_name: Moschella - first_name: Valentino full_name: Maiorca, Valentino last_name: Maiorca - first_name: Marco full_name: Fumero, Marco last_name: Fumero - first_name: Antonio full_name: Norelli, Antonio last_name: Norelli - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Emanuele full_name: Rodolà, Emanuele last_name: Rodolà citation: ama: 'Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative representations enable zero-shot latent space communication. In: The 11th International Conference on Learning Representations. ; 2023.' apa: Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., & Rodolà, E. (2023). Relative representations enable zero-shot latent space communication. In The 11th International Conference on Learning Representations. Kigali, Rwanda. chicago: Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent Space Communication.” 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 The 11th International Conference on Learning Representations, Kigali, Rwanda, 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.” The 11th International Conference on Learning Representations, 2023. short: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà, in:, The 11th International Conference on Learning Representations, 2023. conference: end_date: 2023-05-05 location: Kigali, Rwanda name: International Conference on Machine Learning Representations start_date: 2023-05-01 date_created: 2023-08-22T14:22:20Z date_published: 2023-05-01T00:00:00Z date_updated: 2023-09-13T09:44:26Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2209.15430' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2209.15430 month: '05' oa: 1 oa_version: Preprint publication: The 11th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: Relative representations enable zero-shot latent space communication type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14222' 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. article_number: '2110.06562' article_processing_charge: No author: - first_name: Matthias full_name: Tangemann, Matthias last_name: Tangemann - first_name: Steffen full_name: Schneider, Steffen last_name: Schneider - 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: Thomas full_name: Brox, Thomas last_name: Brox - first_name: Matthias full_name: Kümmerer, Matthias last_name: Kümmerer - first_name: Matthias full_name: Bethge, Matthias last_name: Bethge - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning via common fate. 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 2nd Conference on Causal Learning and Reasoning. 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 2nd Conference on Causal Learning and Reasoning, 2023. ieee: M. Tangemann et al., “Unsupervised object learning via common fate,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023. 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.” 2nd Conference on Causal Learning and Reasoning, 2110.06562, 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. conference: end_date: 2023-04-14 location: Tübingen, Germany name: 'CLeaR: Conference on Causal Learning and Reasoning' start_date: 2023-04-11 date_created: 2023-08-22T14:23:54Z date_published: 2023-04-15T00:00:00Z date_updated: 2023-09-13T11:31:14Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '2110.06562' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2110.06562 month: '04' oa: 1 oa_version: Preprint publication: 2nd Conference on Causal Learning and Reasoning publication_status: published quality_controlled: '1' status: public title: Unsupervised object learning via common fate type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14218' 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. article_processing_charge: No author: - first_name: Maximilian full_name: Seitzer, Maximilian last_name: Seitzer - first_name: Max full_name: Horn, Max last_name: Horn - first_name: Andrii full_name: Zadaianchuk, Andrii last_name: Zadaianchuk - first_name: Dominik full_name: Zietlow, Dominik last_name: Zietlow - first_name: Tianjun full_name: Xiao, Tianjun last_name: Xiao - first_name: Carl-Johann Simon-Gabriel full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel last_name: Carl-Johann Simon-Gabriel - first_name: Tong full_name: He, Tong last_name: He - first_name: Zheng full_name: Zhang, Zheng last_name: Zhang - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Thomas full_name: Brox, Thomas last_name: Brox - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric learning. In: The 11th International Conference on Learning Representations. ; 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 The 11th International Conference on Learning Representations. Kigali, Rwanda. 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 The 11th International Conference on Learning Representations, 2023. ieee: M. Seitzer et al., “Bridging the gap to real-world object-centric learning,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023. 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.” The 11th International Conference on Learning Representations, 2023. 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. conference: end_date: 2023-05-05 location: Kigali, Rwanda name: 'ICLR: International Conference on Learning Representations' start_date: 2023-05-01 date_created: 2023-08-22T14:22:41Z date_published: 2023-05-10T00:00:00Z date_updated: 2023-09-13T11:37:03Z day: '10' department: - _id: FrLo extern: '1' external_id: arxiv: - '2209.14860' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2209.14860 month: '05' oa: 1 oa_version: Preprint publication: The 11th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: Bridging the gap to real-world object-centric learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14219' abstract: - lang: eng 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." article_processing_charge: No author: - first_name: Andrii full_name: Zadaianchuk, Andrii last_name: Zadaianchuk - first_name: Matthaeus full_name: Kleindessner, Matthaeus last_name: Kleindessner - first_name: Yi full_name: Zhu, Yi last_name: Zhu - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Thomas full_name: Brox, Thomas last_name: Brox citation: ama: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic segmentation with self-supervised object-centric representations. In: The 11th International Conference on Learning Representations. ; 2023.' apa: Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., & Brox, T. (2023). Unsupervised semantic segmentation with self-supervised object-centric representations. In The 11th International Conference on Learning Representations. Kigali, Rwanda. chicago: Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” In The 11th International Conference on Learning Representations, 2023. ieee: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised semantic segmentation with self-supervised object-centric representations,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023. 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.” The 11th International Conference on Learning Representations, 2023. short: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The 11th International Conference on Learning Representations, 2023. conference: end_date: 2023-05-05 location: Kigali, Rwanda name: 'ICLR: International Conference on Learning Representations' start_date: 2023-05-01 date_created: 2023-08-22T14:22:58Z date_published: 2023-05-01T00:00:00Z date_updated: 2023-09-13T11:25:43Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2207.05027' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2207.05027 month: '05' oa: 1 oa_version: Preprint publication: The 11th International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: Unsupervised semantic segmentation with self-supervised object-centric representations type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14333' abstract: - lang: eng text: "As causal ground truth is incredibly rare, causal discovery algorithms are\r\ncommonly only evaluated on simulated data. This is concerning, given that\r\nsimulations reflect common preconceptions about generating processes regarding\r\nnoise distributions, model classes, and more. In this work, we propose a novel\r\nmethod for falsifying the output of a causal discovery algorithm in the absence\r\nof ground truth. Our key insight is that while statistical learning seeks\r\nstability across subsets of data points, causal learning should seek stability\r\nacross subsets of variables. Motivated by this insight, our method relies on a\r\nnotion of compatibility between causal graphs learned on different subsets of\r\nvariables. We prove that detecting incompatibilities can falsify wrongly\r\ninferred causal relations due to violation of assumptions or errors from finite\r\nsample effects. Although passing such compatibility tests is only a necessary\r\ncriterion for good performance, we argue that it provides strong evidence for\r\nthe causal models whenever compatibility entails strong implications for the\r\njoint distribution. We also demonstrate experimentally that detection of\r\nincompatibilities can aid in causal model selection." article_number: '2307.09552' article_processing_charge: No author: - first_name: Philipp M. full_name: Faller, Philipp M. last_name: Faller - first_name: Leena Chennuru full_name: Vankadara, Leena Chennuru last_name: Vankadara - first_name: Atalanti A. full_name: Mastakouri, Atalanti A. last_name: Mastakouri - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Dominik full_name: Janzing, Dominik last_name: Janzing citation: ama: 'Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility: Evaluating causal discovery without ground truth. arXiv. doi:10.48550/arXiv.2307.09552' apa: 'Faller, P. M., Vankadara, L. C., Mastakouri, A. A., Locatello, F., & Janzing, D. (n.d.). Self-compatibility: Evaluating causal discovery without ground truth. arXiv. https://doi.org/10.48550/arXiv.2307.09552' chicago: 'Faller, Philipp M., Leena Chennuru Vankadara, Atalanti A. Mastakouri, Francesco Locatello, and Dominik Janzing. “Self-Compatibility: Evaluating Causal Discovery without Ground Truth.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2307.09552.' ieee: 'P. M. Faller, L. C. Vankadara, A. A. Mastakouri, F. Locatello, and D. Janzing, “Self-compatibility: Evaluating causal discovery without ground truth,” arXiv. .' ista: 'Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility: Evaluating causal discovery without ground truth. arXiv, 2307.09552.' mla: 'Faller, Philipp M., et al. “Self-Compatibility: Evaluating Causal Discovery without Ground Truth.” ArXiv, 2307.09552, doi:10.48550/arXiv.2307.09552.' short: P.M. Faller, L.C. Vankadara, A.A. Mastakouri, F. Locatello, D. Janzing, ArXiv (n.d.). date_created: 2023-09-13T12:44:59Z date_published: 2023-07-18T00:00:00Z date_updated: 2023-09-13T12:47:53Z day: '18' department: - _id: FrLo doi: 10.48550/arXiv.2307.09552 extern: '1' external_id: arxiv: - '2307.09552' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2307.09552 month: '07' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: 'Self-compatibility: Evaluating causal discovery without ground truth' type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ...