--- _id: '9571' abstract: - lang: eng text: As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-parallel SGD is QSGD (Alistarh et al., 2017), which quantizes and encodes gradients to reduce communication costs. The baseline variant of QSGD provides strong theoretical guarantees, however, for practical purposes, the authors proposed a heuristic variant which we call QSGDinf, which demonstrated impressive empirical gains for distributed training of large neural networks. In this paper, we build on this work to propose a new gradient quantization scheme, and show that it has both stronger theoretical guarantees than QSGD, and matches and exceeds the empirical performance of the QSGDinf heuristic and of other compression methods. article_processing_charge: No article_type: original author: - first_name: Ali full_name: Ramezani-Kebrya, Ali last_name: Ramezani-Kebrya - first_name: Fartash full_name: Faghri, Fartash last_name: Faghri - first_name: Ilya full_name: Markov, Ilya last_name: Markov - first_name: Vitalii full_name: Aksenov, Vitalii id: 2980135A-F248-11E8-B48F-1D18A9856A87 last_name: Aksenov - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X - first_name: Daniel M. full_name: Roy, Daniel M. last_name: Roy citation: ama: 'Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 2021;22(114):1−43.' apa: 'Ramezani-Kebrya, A., Faghri, F., Markov, I., Aksenov, V., Alistarh, D.-A., & Roy, D. M. (2021). NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. Journal of Machine Learning Research.' chicago: 'Ramezani-Kebrya, Ali, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan-Adrian Alistarh, and Daniel M. Roy. “NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.' ieee: 'A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, and D. M. Roy, “NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization,” Journal of Machine Learning Research, vol. 22, no. 114. Journal of Machine Learning Research, p. 1−43, 2021.' ista: 'Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. 2021. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 22(114), 1−43.' mla: 'Ramezani-Kebrya, Ali, et al. “NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization.” Journal of Machine Learning Research, vol. 22, no. 114, Journal of Machine Learning Research, 2021, p. 1−43.' short: A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, D.M. Roy, Journal of Machine Learning Research 22 (2021) 1−43. date_created: 2021-06-20T22:01:33Z date_published: 2021-04-01T00:00:00Z date_updated: 2024-03-06T12:22:07Z day: '01' ddc: - '000' department: - _id: DaAl external_id: arxiv: - '1908.06077' file: - access_level: open_access checksum: 6428aa8bcb67768b6949c99b55d5281d content_type: application/pdf creator: asandaue date_created: 2021-06-23T07:09:41Z date_updated: 2021-06-23T07:09:41Z file_id: '9595' file_name: 2021_JournalOfMachineLearningResearch_Ramezani-Kebrya.pdf file_size: 11237154 relation: main_file success: 1 file_date_updated: 2021-06-23T07:09:41Z has_accepted_license: '1' intvolume: ' 22' issue: '114' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ main_file_link: - open_access: '1' url: https://www.jmlr.org/papers/v22/20-255.html month: '04' oa: 1 oa_version: Published Version page: 1−43 publication: Journal of Machine Learning Research publication_identifier: eissn: - '15337928' issn: - '15324435' publication_status: published publisher: Journal of Machine Learning Research quality_controlled: '1' scopus_import: '1' status: public title: 'NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization' 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: 22 year: '2021' ... --- _id: '8544' abstract: - lang: eng text: The synaptotrophic hypothesis posits that synapse formation stabilizes dendritic branches, yet this hypothesis has not been causally tested in vivo in the mammalian brain. Presynaptic ligand cerebellin-1 (Cbln1) and postsynaptic receptor GluD2 mediate synaptogenesis between granule cells and Purkinje cells in the molecular layer of the cerebellar cortex. Here we show that sparse but not global knockout of GluD2 causes under-elaboration of Purkinje cell dendrites in the deep molecular layer and overelaboration in the superficial molecular layer. Developmental, overexpression, structure-function, and genetic epistasis analyses indicate that dendrite morphogenesis defects result from competitive synaptogenesis in a Cbln1/GluD2-dependent manner. A generative model of dendritic growth based on competitive synaptogenesis largely recapitulates GluD2 sparse and global knockout phenotypes. Our results support the synaptotrophic hypothesis at initial stages of dendrite development, suggest a second mode in which cumulative synapse formation inhibits further dendrite growth, and highlight the importance of competition in dendrite morphogenesis. acknowledgement: We thank M. Mishina for GluD2fl frozen embryos, T.C. Südhof and J.I. Morgan for Cbln1fl mice, L. Anderson for help in generating the MADM alleles, W. Joo for a previously unpublished construct, M. Yuzaki, K. Shen, J. Ding, and members of the Luo lab, including J.M. Kebschull, H. Li, J. Li, T. Li, C.M. McLaughlin, D. Pederick, J. Ren, D.C. Wang and C. Xu for discussions and critiques of the manuscript, and M. Yuzaki for supporting Y.H.T. during the final phase of this project. Y.H.T. was supported by a JSPS fellowship; S.A.S. was supported by a Stanford Graduate Fellowship and an NSF Predoctoral Fellowship; L.J. is supported by a Stanford Graduate Fellowship and an NSF Predoctoral Fellowship; M.J.W. is supported by a Burroughs Wellcome Fund CASI Award. This work was supported by an NIH grant (R01-NS050538) to L.L.; the European Research Council (ERC) under the European Union's Horizon 2020 research and innovations programme (No. 725780 LinPro) to S.H.; and Simons and James S. McDonnell Foundations and an NSF CAREER award to S.G.; L.L. is an HHMI investigator. article_processing_charge: No article_type: original author: - first_name: Yukari H. full_name: Takeo, Yukari H. last_name: Takeo - first_name: S. Andrew full_name: Shuster, S. Andrew last_name: Shuster - first_name: Linnie full_name: Jiang, Linnie last_name: Jiang - first_name: Miley full_name: Hu, Miley last_name: Hu - first_name: David J. full_name: Luginbuhl, David J. last_name: Luginbuhl - first_name: Thomas full_name: Rülicke, Thomas last_name: Rülicke - first_name: Ximena full_name: Contreras, Ximena id: 475990FE-F248-11E8-B48F-1D18A9856A87 last_name: Contreras - first_name: Simon full_name: Hippenmeyer, Simon id: 37B36620-F248-11E8-B48F-1D18A9856A87 last_name: Hippenmeyer orcid: 0000-0003-2279-1061 - first_name: Mark J. full_name: Wagner, Mark J. last_name: Wagner - first_name: Surya full_name: Ganguli, Surya last_name: Ganguli - first_name: Liqun full_name: Luo, Liqun last_name: Luo citation: ama: Takeo YH, Shuster SA, Jiang L, et al. GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. Neuron. 2021;109(4):P629-644.E8. doi:10.1016/j.neuron.2020.11.028 apa: Takeo, Y. H., Shuster, S. A., Jiang, L., Hu, M., Luginbuhl, D. J., Rülicke, T., … Luo, L. (2021). GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2020.11.028 chicago: Takeo, Yukari H., S. Andrew Shuster, Linnie Jiang, Miley Hu, David J. Luginbuhl, Thomas Rülicke, Ximena Contreras, et al. “GluD2- and Cbln1-Mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells.” Neuron. Elsevier, 2021. https://doi.org/10.1016/j.neuron.2020.11.028. ieee: Y. H. Takeo et al., “GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells,” Neuron, vol. 109, no. 4. Elsevier, p. P629–644.E8, 2021. ista: Takeo YH, Shuster SA, Jiang L, Hu M, Luginbuhl DJ, Rülicke T, Contreras X, Hippenmeyer S, Wagner MJ, Ganguli S, Luo L. 2021. GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. Neuron. 109(4), P629–644.E8. mla: Takeo, Yukari H., et al. “GluD2- and Cbln1-Mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells.” Neuron, vol. 109, no. 4, Elsevier, 2021, p. P629–644.E8, doi:10.1016/j.neuron.2020.11.028. short: Y.H. Takeo, S.A. Shuster, L. Jiang, M. Hu, D.J. Luginbuhl, T. Rülicke, X. Contreras, S. Hippenmeyer, M.J. Wagner, S. Ganguli, L. Luo, Neuron 109 (2021) P629–644.E8. date_created: 2020-09-21T11:59:47Z date_published: 2021-02-17T00:00:00Z date_updated: 2024-03-06T12:12:48Z day: '17' department: - _id: SiHi doi: 10.1016/j.neuron.2020.11.028 ec_funded: 1 intvolume: ' 109' issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1101/2020.06.14.151258 month: '02' oa: 1 oa_version: Preprint page: P629-644.E8 project: - _id: 260018B0-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '725780' name: Principles of Neural Stem Cell Lineage Progression in Cerebral Cortex Development publication: Neuron publication_identifier: eissn: - 1097-4199 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 109 year: '2021' ... --- _id: '9791' abstract: - lang: eng text: We provide a definition of the effective mass for the classical polaron described by the Landau-Pekar equations. It is based on a novel variational principle, minimizing the energy functional over states with given (initial) velocity. The resulting formula for the polaron's effective mass agrees with the prediction by Landau and Pekar. acknowledgement: We thank Herbert Spohn for helpful comments. Funding from the European Union’s Horizon 2020 research and innovation programme under the ERC grant agreement No. 694227 (D.F. and R.S.) and under the Marie Skłodowska-Curie Grant Agreement No. 754411 (S.R.) is gratefully acknowledged.. article_number: '2107.03720 ' article_processing_charge: No author: - first_name: Dario full_name: Feliciangeli, Dario id: 41A639AA-F248-11E8-B48F-1D18A9856A87 last_name: Feliciangeli orcid: 0000-0003-0754-8530 - first_name: Simone Anna Elvira full_name: Rademacher, Simone Anna Elvira id: 856966FE-A408-11E9-977E-802DE6697425 last_name: Rademacher orcid: 0000-0001-5059-4466 - first_name: Robert full_name: Seiringer, Robert id: 4AFD0470-F248-11E8-B48F-1D18A9856A87 last_name: Seiringer orcid: 0000-0002-6781-0521 citation: ama: Feliciangeli D, Rademacher SAE, Seiringer R. The effective mass problem for the Landau-Pekar equations. arXiv. apa: Feliciangeli, D., Rademacher, S. A. E., & Seiringer, R. (n.d.). The effective mass problem for the Landau-Pekar equations. arXiv. chicago: Feliciangeli, Dario, Simone Anna Elvira Rademacher, and Robert Seiringer. “The Effective Mass Problem for the Landau-Pekar Equations.” ArXiv, n.d. ieee: D. Feliciangeli, S. A. E. Rademacher, and R. Seiringer, “The effective mass problem for the Landau-Pekar equations,” arXiv. . ista: Feliciangeli D, Rademacher SAE, Seiringer R. The effective mass problem for the Landau-Pekar equations. arXiv, 2107.03720. mla: Feliciangeli, Dario, et al. “The Effective Mass Problem for the Landau-Pekar Equations.” ArXiv, 2107.03720. short: D. Feliciangeli, S.A.E. Rademacher, R. Seiringer, ArXiv (n.d.). date_created: 2021-08-06T08:49:45Z date_published: 2021-07-08T00:00:00Z date_updated: 2024-03-06T12:30:45Z day: '08' ddc: - '510' department: - _id: RoSe ec_funded: 1 external_id: arxiv: - '2107.03720' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2107.03720 month: '07' oa: 1 oa_version: Preprint project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships - _id: 25C6DC12-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '694227' name: Analysis of quantum many-body systems publication: arXiv publication_status: submitted related_material: record: - id: '10755' relation: later_version status: public - id: '9733' relation: dissertation_contains status: public status: public title: The effective mass problem for the Landau-Pekar equations type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '7553' abstract: - lang: eng text: Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems. acknowledgement: The authors thank Dario Ringach for providing the V1 receptive fields and Olivier Marre for providing the retinal receptive fields. W.M. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers Science grant no. HFSP RGP0032/2018. article_processing_charge: No author: - first_name: Wiktor F full_name: Mlynarski, Wiktor F id: 358A453A-F248-11E8-B48F-1D18A9856A87 last_name: Mlynarski - first_name: Michal full_name: Hledik, Michal id: 4171253A-F248-11E8-B48F-1D18A9856A87 last_name: Hledik - first_name: Thomas R full_name: Sokolowski, Thomas R id: 3E999752-F248-11E8-B48F-1D18A9856A87 last_name: Sokolowski orcid: 0000-0002-1287-3779 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality of neural systems. Neuron. 2021;109(7):1227-1241.e5. doi:10.1016/j.neuron.2021.01.020 apa: Mlynarski, W. F., Hledik, M., Sokolowski, T. R., & Tkačik, G. (2021). Statistical analysis and optimality of neural systems. Neuron. Cell Press. https://doi.org/10.1016/j.neuron.2021.01.020 chicago: Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik. “Statistical Analysis and Optimality of Neural Systems.” Neuron. Cell Press, 2021. https://doi.org/10.1016/j.neuron.2021.01.020. ieee: W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical analysis and optimality of neural systems,” Neuron, vol. 109, no. 7. Cell Press, p. 1227–1241.e5, 2021. ista: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis and optimality of neural systems. Neuron. 109(7), 1227–1241.e5. mla: Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural Systems.” Neuron, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:10.1016/j.neuron.2021.01.020. short: W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021) 1227–1241.e5. date_created: 2020-02-28T11:00:12Z date_published: 2021-04-07T00:00:00Z date_updated: 2024-03-06T14:22:51Z day: '07' department: - _id: GaTk doi: 10.1016/j.neuron.2021.01.020 ec_funded: 1 external_id: isi: - '000637809600006' intvolume: ' 109' isi: 1 issue: '7' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1101/848374 month: '04' oa: 1 oa_version: Preprint page: 1227-1241.e5 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Neuron publication_status: published publisher: Cell Press quality_controlled: '1' related_material: link: - description: News on IST Homepage relation: press_release url: https://ist.ac.at/en/news/can-evolution-be-predicted/ record: - id: '15020' relation: dissertation_contains status: public scopus_import: '1' status: public title: Statistical analysis and optimality of neural systems type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 109 year: '2021' ... --- _id: '10598' abstract: - lang: eng text: ' We consider the problem of estimating a signal from measurements obtained via a generalized linear model. We focus on estimators based on approximate message passing (AMP), a family of iterative algorithms with many appealing features: the performance of AMP in the high-dimensional limit can be succinctly characterized under suitable model assumptions; AMP can also be tailored to the empirical distribution of the signal entries, and for a wide class of estimation problems, AMP is conjectured to be optimal among all polynomial-time algorithms. However, a major issue of AMP is that in many models (such as phase retrieval), it requires an initialization correlated with the ground-truth signal and independent from the measurement matrix. Assuming that such an initialization is available is typically not realistic. In this paper, we solve this problem by proposing an AMP algorithm initialized with a spectral estimator. With such an initialization, the standard AMP analysis fails since the spectral estimator depends in a complicated way on the design matrix. Our main contribution is a rigorous characterization of the performance of AMP with spectral initialization in the high-dimensional limit. The key technical idea is to define and analyze a two-phase artificial AMP algorithm that first produces the spectral estimator, and then closely approximates the iterates of the true AMP. We also provide numerical results that demonstrate the validity of the proposed approach. ' acknowledgement: The authors would like to thank Andrea Montanari for helpful discussions. M. Mondelli was partially supported by the 2019 Lopez-Loreta Prize. R. Venkataramanan was partially supported by the Alan Turing Institute under the EPSRC grant EP/N510129/1. alternative_title: - Proceedings of Machine Learning Research article_processing_charge: Yes (via OA deal) author: - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 - first_name: Ramji full_name: Venkataramanan, Ramji last_name: Venkataramanan citation: ama: 'Mondelli M, Venkataramanan R. Approximate message passing with spectral initialization for generalized linear models. In: Banerjee A, Fukumizu K, eds. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. Vol 130. ML Research Press; 2021:397-405.' apa: 'Mondelli, M., & Venkataramanan, R. (2021). Approximate message passing with spectral initialization for generalized linear models. In A. Banerjee & K. Fukumizu (Eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Vol. 130, pp. 397–405). Virtual, San Diego, CA, United States: ML Research Press.' chicago: Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing with Spectral Initialization for Generalized Linear Models.” In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, edited by Arindam Banerjee and Kenji Fukumizu, 130:397–405. ML Research Press, 2021. ieee: M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral initialization for generalized linear models,” in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, Virtual, San Diego, CA, United States, 2021, vol. 130, pp. 397–405. ista: 'Mondelli M, Venkataramanan R. 2021. Approximate message passing with spectral initialization for generalized linear models. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. AISTATS: Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 130, 397–405.' mla: Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing with Spectral Initialization for Generalized Linear Models.” Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, edited by Arindam Banerjee and Kenji Fukumizu, vol. 130, ML Research Press, 2021, pp. 397–405. short: M. Mondelli, R. Venkataramanan, in:, A. Banerjee, K. Fukumizu (Eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2021, pp. 397–405. conference: end_date: 2021-04-15 location: Virtual, San Diego, CA, United States name: 'AISTATS: Artificial Intelligence and Statistics' start_date: 2021-04-13 date_created: 2022-01-03T11:34:22Z date_published: 2021-04-01T00:00:00Z date_updated: 2024-03-07T10:36:53Z day: '01' department: - _id: MaMo editor: - first_name: Arindam full_name: Banerjee, Arindam last_name: Banerjee - first_name: Kenji full_name: Fukumizu, Kenji last_name: Fukumizu external_id: arxiv: - '2010.03460' intvolume: ' 130' language: - iso: eng main_file_link: - open_access: '1' url: https://proceedings.mlr.press/v130/mondelli21a.html month: '04' oa: 1 oa_version: Preprint page: 397-405 project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: Proceedings of The 24th International Conference on Artificial Intelligence and Statistics publication_identifier: issn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '12480' relation: later_version status: public scopus_import: '1' status: public title: Approximate message passing with spectral initialization for generalized linear models type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 130 year: '2021' ...