--- _id: '150' abstract: - lang: eng text: A short, 14-amino-acid segment called SP1, located in the Gag structural protein1, has a critical role during the formation of the HIV-1 virus particle. During virus assembly, the SP1 peptide and seven preceding residues fold into a six-helix bundle, which holds together the Gag hexamer and facilitates the formation of a curved immature hexagonal lattice underneath the viral membrane2,3. Upon completion of assembly and budding, proteolytic cleavage of Gag leads to virus maturation, in which the immature lattice is broken down; the liberated CA domain of Gag then re-assembles into the mature conical capsid that encloses the viral genome and associated enzymes. Folding and proteolysis of the six-helix bundle are crucial rate-limiting steps of both Gag assembly and disassembly, and the six-helix bundle is an established target of HIV-1 inhibitors4,5. Here, using a combination of structural and functional analyses, we show that inositol hexakisphosphate (InsP6, also known as IP6) facilitates the formation of the six-helix bundle and assembly of the immature HIV-1 Gag lattice. IP6 makes ionic contacts with two rings of lysine residues at the centre of the Gag hexamer. Proteolytic cleavage then unmasks an alternative binding site, where IP6 interaction promotes the assembly of the mature capsid lattice. These studies identify IP6 as a naturally occurring small molecule that promotes both assembly and maturation of HIV-1. article_processing_charge: No article_type: original author: - first_name: Robert full_name: Dick, Robert last_name: Dick - first_name: Kaneil K full_name: Zadrozny, Kaneil K last_name: Zadrozny - first_name: Chaoyi full_name: Xu, Chaoyi last_name: Xu - first_name: Florian full_name: Schur, Florian id: 48AD8942-F248-11E8-B48F-1D18A9856A87 last_name: Schur orcid: 0000-0003-4790-8078 - first_name: Terri D full_name: Lyddon, Terri D last_name: Lyddon - first_name: Clifton L full_name: Ricana, Clifton L last_name: Ricana - first_name: Jonathan M full_name: Wagner, Jonathan M last_name: Wagner - first_name: Juan R full_name: Perilla, Juan R last_name: Perilla - first_name: Pornillos Barbie K full_name: Ganser, Pornillos Barbie K last_name: Ganser - first_name: Marc C full_name: Johnson, Marc C last_name: Johnson - first_name: Owen full_name: Pornillos, Owen last_name: Pornillos - first_name: Volker full_name: Vogt, Volker last_name: Vogt citation: ama: Dick R, Zadrozny KK, Xu C, et al. Inositol phosphates are assembly co-factors for HIV-1. Nature. 2018;560(7719):509–512. doi:10.1038/s41586-018-0396-4 apa: Dick, R., Zadrozny, K. K., Xu, C., Schur, F. K., Lyddon, T. D., Ricana, C. L., … Vogt, V. (2018). Inositol phosphates are assembly co-factors for HIV-1. Nature. Nature Publishing Group. https://doi.org/10.1038/s41586-018-0396-4 chicago: Dick, Robert, Kaneil K Zadrozny, Chaoyi Xu, Florian KM Schur, Terri D Lyddon, Clifton L Ricana, Jonathan M Wagner, et al. “Inositol Phosphates Are Assembly Co-Factors for HIV-1.” Nature. Nature Publishing Group, 2018. https://doi.org/10.1038/s41586-018-0396-4. ieee: R. Dick et al., “Inositol phosphates are assembly co-factors for HIV-1,” Nature, vol. 560, no. 7719. Nature Publishing Group, pp. 509–512, 2018. ista: Dick R, Zadrozny KK, Xu C, Schur FK, Lyddon TD, Ricana CL, Wagner JM, Perilla JR, Ganser PBK, Johnson MC, Pornillos O, Vogt V. 2018. Inositol phosphates are assembly co-factors for HIV-1. Nature. 560(7719), 509–512. mla: Dick, Robert, et al. “Inositol Phosphates Are Assembly Co-Factors for HIV-1.” Nature, vol. 560, no. 7719, Nature Publishing Group, 2018, pp. 509–512, doi:10.1038/s41586-018-0396-4. short: R. Dick, K.K. Zadrozny, C. Xu, F.K. Schur, T.D. Lyddon, C.L. Ricana, J.M. Wagner, J.R. Perilla, P.B.K. Ganser, M.C. Johnson, O. Pornillos, V. Vogt, Nature 560 (2018) 509–512. date_created: 2018-12-11T11:44:53Z date_published: 2018-08-29T00:00:00Z date_updated: 2023-09-12T07:44:37Z day: '29' department: - _id: FlSc doi: 10.1038/s41586-018-0396-4 external_id: isi: - '000442483400046' pmid: - '30158708' intvolume: ' 560' isi: 1 issue: '7719' language: - iso: eng main_file_link: - open_access: '1' url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6242333/ month: '08' oa: 1 oa_version: Submitted Version page: 509–512 pmid: 1 publication: Nature publication_identifier: eissn: - 1476-4687 publication_status: published publisher: Nature Publishing Group quality_controlled: '1' related_material: link: - relation: erratum url: https://doi.org/10.1038/s41586-018-0505-4 scopus_import: '1' status: public title: Inositol phosphates are assembly co-factors for HIV-1 type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 560 year: '2018' ... --- _id: '303' abstract: - lang: eng text: The theory of tropical series, that we develop here, firstly appeared in the study of the growth of pluriharmonic functions. Motivated by waves in sandpile models we introduce a dynamic on the set of tropical series, and it is experimentally observed that this dynamic obeys a power law. So, this paper serves as a compilation of results we need for other articles and also introduces several objects interesting by themselves. acknowledgement: The first author, Nikita Kalinin, is funded by SNCF PostDoc.Mobility grant 168647. Support from the Basic Research Program of the National Research University Higher School of Economics is gratefully acknowledged. The second author, Mikhail Shkolnikov, is supported in part by the grant 159240 of the Swiss National Science Foundation as well as by the National Center of Competence in Research SwissMAP of the Swiss National Science Foundation. article_processing_charge: No author: - first_name: Nikita full_name: Kalinin, Nikita last_name: Kalinin - first_name: Mikhail full_name: Shkolnikov, Mikhail id: 35084A62-F248-11E8-B48F-1D18A9856A87 last_name: Shkolnikov orcid: 0000-0002-4310-178X citation: ama: Kalinin N, Shkolnikov M. Introduction to tropical series and wave dynamic on them. Discrete and Continuous Dynamical Systems- Series A. 2018;38(6):2827-2849. doi:10.3934/dcds.2018120 apa: Kalinin, N., & Shkolnikov, M. (2018). Introduction to tropical series and wave dynamic on them. Discrete and Continuous Dynamical Systems- Series A. AIMS. https://doi.org/10.3934/dcds.2018120 chicago: Kalinin, Nikita, and Mikhail Shkolnikov. “Introduction to Tropical Series and Wave Dynamic on Them.” Discrete and Continuous Dynamical Systems- Series A. AIMS, 2018. https://doi.org/10.3934/dcds.2018120. ieee: N. Kalinin and M. Shkolnikov, “Introduction to tropical series and wave dynamic on them,” Discrete and Continuous Dynamical Systems- Series A, vol. 38, no. 6. AIMS, pp. 2827–2849, 2018. ista: Kalinin N, Shkolnikov M. 2018. Introduction to tropical series and wave dynamic on them. Discrete and Continuous Dynamical Systems- Series A. 38(6), 2827–2849. mla: Kalinin, Nikita, and Mikhail Shkolnikov. “Introduction to Tropical Series and Wave Dynamic on Them.” Discrete and Continuous Dynamical Systems- Series A, vol. 38, no. 6, AIMS, 2018, pp. 2827–49, doi:10.3934/dcds.2018120. short: N. Kalinin, M. Shkolnikov, Discrete and Continuous Dynamical Systems- Series A 38 (2018) 2827–2849. date_created: 2018-12-11T11:45:43Z date_published: 2018-06-01T00:00:00Z date_updated: 2023-09-12T07:45:37Z day: '01' department: - _id: TaHa doi: 10.3934/dcds.2018120 external_id: arxiv: - '1706.03062' isi: - '000438818400007' intvolume: ' 38' isi: 1 issue: '6' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1706.03062 month: '06' oa: 1 oa_version: Submitted Version page: 2827 - 2849 publication: Discrete and Continuous Dynamical Systems- Series A publication_status: published publisher: AIMS publist_id: '7576' quality_controlled: '1' scopus_import: '1' status: public title: Introduction to tropical series and wave dynamic on them type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 38 year: '2018' ... --- _id: '14202' abstract: - lang: eng text: "Approximating a probability density in a tractable manner is a central task\r\nin Bayesian statistics. Variational Inference (VI) is a popular technique that\r\nachieves tractability by choosing a relatively simple variational family.\r\nBorrowing ideas from the classic boosting framework, recent approaches attempt\r\nto \\emph{boost} VI by replacing the selection of a single density with a\r\ngreedily constructed mixture of densities. In order to guarantee convergence,\r\nprevious works impose stringent assumptions that require significant effort for\r\npractitioners. Specifically, they require a custom implementation of the greedy\r\nstep (called the LMO) for every probabilistic model with respect to an\r\nunnatural variational family of truncated distributions. Our work fixes these\r\nissues with novel theoretical and algorithmic insights. On the theoretical\r\nside, we show that boosting VI satisfies a relaxed smoothness assumption which\r\nis sufficient for the convergence of the functional Frank-Wolfe (FW) algorithm.\r\nFurthermore, we rephrase the LMO problem and propose to maximize the Residual\r\nELBO (RELBO) which replaces the standard ELBO optimization in VI. These\r\ntheoretical enhancements allow for black box implementation of the boosting\r\nsubroutine. Finally, we present a stopping criterion drawn from the duality gap\r\nin the classic FW analyses and exhaustive experiments to illustrate the\r\nusefulness of our theoretical and algorithmic contributions." article_processing_charge: No author: - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Gideon full_name: Dresdner, Gideon last_name: Dresdner - first_name: Rajiv full_name: Khanna, Rajiv last_name: Khanna - first_name: Isabel full_name: Valera, Isabel last_name: Valera - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch citation: ama: 'Locatello F, Dresdner G, Khanna R, Valera I, Rätsch G. Boosting black box variational inference. In: Advances in Neural Information Processing Systems. Vol 31. Neural Information Processing Systems Foundation; 2018.' apa: 'Locatello, F., Dresdner, G., Khanna, R., Valera, I., & Rätsch, G. (2018). Boosting black box variational inference. In Advances in Neural Information Processing Systems (Vol. 31). Montreal, Canada: Neural Information Processing Systems Foundation.' chicago: Locatello, Francesco, Gideon Dresdner, Rajiv Khanna, Isabel Valera, and Gunnar Rätsch. “Boosting Black Box Variational Inference.” In Advances in Neural Information Processing Systems, Vol. 31. Neural Information Processing Systems Foundation, 2018. ieee: F. Locatello, G. Dresdner, R. Khanna, I. Valera, and G. Rätsch, “Boosting black box variational inference,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. 31. ista: 'Locatello F, Dresdner G, Khanna R, Valera I, Rätsch G. 2018. Boosting black box variational inference. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 31.' mla: Locatello, Francesco, et al. “Boosting Black Box Variational Inference.” Advances in Neural Information Processing Systems, vol. 31, Neural Information Processing Systems Foundation, 2018. short: F. Locatello, G. Dresdner, R. Khanna, I. Valera, G. Rätsch, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2018. conference: end_date: 2018-12-08 location: Montreal, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2018-12-03 date_created: 2023-08-22T14:15:40Z date_published: 2018-06-06T00:00:00Z date_updated: 2023-09-13T07:38:24Z day: '06' department: - _id: FrLo extern: '1' external_id: arxiv: - '1806.02185' intvolume: ' 31' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1806.02185 month: '06' oa: 1 oa_version: Preprint publication: Advances in Neural Information Processing Systems publication_identifier: eissn: - 1049-5258 isbn: - '9781510884472' publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Boosting black box variational inference type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 31 year: '2018' ... --- _id: '14201' abstract: - lang: eng text: "Variational inference is a popular technique to approximate a possibly\r\nintractable Bayesian posterior with a more tractable one. Recently, boosting\r\nvariational inference has been proposed as a new paradigm to approximate the\r\nposterior by a mixture of densities by greedily adding components to the\r\nmixture. However, as is the case with many other variational inference\r\nalgorithms, its theoretical properties have not been studied. In the present\r\nwork, we study the convergence properties of this approach from a modern\r\noptimization viewpoint by establishing connections to the classic Frank-Wolfe\r\nalgorithm. Our analyses yields novel theoretical insights regarding the\r\nsufficient conditions for convergence, explicit rates, and algorithmic\r\nsimplifications. Since a lot of focus in previous works for variational\r\ninference has been on tractability, our work is especially important as a much\r\nneeded attempt to bridge the gap between probabilistic models and their\r\ncorresponding theoretical properties." alternative_title: - PMLR article_processing_charge: No author: - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Rajiv full_name: Khanna, Rajiv last_name: Khanna - first_name: Joydeep full_name: Ghosh, Joydeep last_name: Ghosh - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch citation: ama: 'Locatello F, Khanna R, Ghosh J, Rätsch G. Boosting variational inference: An optimization perspective. In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. Vol 84. ML Research Press; 2018:464-472.' apa: 'Locatello, F., Khanna, R., Ghosh, J., & Rätsch, G. (2018). Boosting variational inference: An optimization perspective. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (Vol. 84, pp. 464–472). Playa Blanca, Lanzarote: ML Research Press.' chicago: 'Locatello, Francesco, Rajiv Khanna, Joydeep Ghosh, and Gunnar Rätsch. “Boosting Variational Inference: An Optimization Perspective.” In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, 84:464–72. ML Research Press, 2018.' ieee: 'F. Locatello, R. Khanna, J. Ghosh, and G. Rätsch, “Boosting variational inference: An optimization perspective,” in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, 2018, vol. 84, pp. 464–472.' ista: 'Locatello F, Khanna R, Ghosh J, Rätsch G. 2018. Boosting variational inference: An optimization perspective. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 84, 464–472.' mla: 'Locatello, Francesco, et al. “Boosting Variational Inference: An Optimization Perspective.” Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, vol. 84, ML Research Press, 2018, pp. 464–72.' short: F. Locatello, R. Khanna, J. Ghosh, G. Rätsch, in:, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, ML Research Press, 2018, pp. 464–472. conference: end_date: 2018-04-11 location: Playa Blanca, Lanzarote name: 'AISTATS: Conference on Artificial Intelligence and Statistics' start_date: 2018-04-09 date_created: 2023-08-22T14:15:20Z date_published: 2018-04-15T00:00:00Z date_updated: 2023-09-13T07:52:40Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '1708.01733' intvolume: ' 84' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1708.01733 month: '04' oa: 1 oa_version: Preprint page: 464-472 publication: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'Boosting variational inference: An optimization perspective' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 84 year: '2018' ... --- _id: '14198' abstract: - lang: eng text: "High-dimensional time series are common in many domains. Since human\r\ncognition is not optimized to work well in high-dimensional spaces, these areas\r\ncould benefit from interpretable low-dimensional representations. However, most\r\nrepresentation learning algorithms for time series data are difficult to\r\ninterpret. This is due to non-intuitive mappings from data features to salient\r\nproperties of the representation and non-smoothness over time. To address this\r\nproblem, we propose a new representation learning framework building on ideas\r\nfrom interpretable discrete dimensionality reduction and deep generative\r\nmodeling. This framework allows us to learn discrete representations of time\r\nseries, which give rise to smooth and interpretable embeddings with superior\r\nclustering performance. We introduce a new way to overcome the\r\nnon-differentiability in discrete representation learning and present a\r\ngradient-based version of the traditional self-organizing map algorithm that is\r\nmore performant than the original. Furthermore, to allow for a probabilistic\r\ninterpretation of our method, we integrate a Markov model in the representation\r\nspace. This model uncovers the temporal transition structure, improves\r\nclustering performance even further and provides additional explanatory\r\ninsights as well as a natural representation of uncertainty. We evaluate our\r\nmodel in terms of clustering performance and interpretability on static\r\n(Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST\r\nimages, a chaotic Lorenz attractor system with two macro states, as well as on\r\na challenging real world medical time series application on the eICU data set.\r\nOur learned representations compare favorably with competitor methods and\r\nfacilitate downstream tasks on the real world data." article_processing_charge: No author: - first_name: Vincent full_name: Fortuin, Vincent last_name: Fortuin - first_name: Matthias full_name: Hüser, Matthias last_name: Hüser - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Heiko full_name: Strathmann, Heiko last_name: Strathmann - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch citation: ama: 'Fortuin V, Hüser M, Locatello F, Strathmann H, Rätsch G. SOM-VAE: Interpretable discrete representation learning on time series. In: International Conference on Learning Representations. ; 2018.' apa: 'Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., & Rätsch, G. (2018). SOM-VAE: Interpretable discrete representation learning on time series. In International Conference on Learning Representations. New Orleans, LA, United States.' chicago: 'Fortuin, Vincent, Matthias Hüser, Francesco Locatello, Heiko Strathmann, and Gunnar Rätsch. “SOM-VAE: Interpretable Discrete Representation Learning on Time Series.” In International Conference on Learning Representations, 2018.' ieee: 'V. Fortuin, M. Hüser, F. Locatello, H. Strathmann, and G. Rätsch, “SOM-VAE: Interpretable discrete representation learning on time series,” in International Conference on Learning Representations, New Orleans, LA, United States, 2018.' ista: 'Fortuin V, Hüser M, Locatello F, Strathmann H, Rätsch G. 2018. SOM-VAE: Interpretable discrete representation learning on time series. International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: 'Fortuin, Vincent, et al. “SOM-VAE: Interpretable Discrete Representation Learning on Time Series.” International Conference on Learning Representations, 2018.' short: V. Fortuin, M. Hüser, F. Locatello, H. Strathmann, G. Rätsch, in:, International Conference on Learning Representations, 2018. conference: end_date: 2019-05-09 location: New Orleans, LA, United States name: 'ICLR: International Conference on Learning Representations' start_date: 2019-05-06 date_created: 2023-08-22T14:12:48Z date_published: 2018-06-06T00:00:00Z date_updated: 2023-09-13T06:35:12Z day: '06' department: - _id: FrLo extern: '1' external_id: arxiv: - '1806.02199' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1806.02199 month: '06' oa: 1 oa_version: Preprint publication: International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: 'SOM-VAE: Interpretable discrete representation learning on time series' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ...