--- _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' ... --- _id: '14203' abstract: - lang: eng text: We propose a conditional gradient framework for a composite convex minimization template with broad applications. Our approach combines smoothing and homotopy techniques under the CGM framework, and provably achieves the optimal O(1/k−−√) convergence rate. We demonstrate that the same rate holds if the linear subproblems are solved approximately with additive or multiplicative error. In contrast with the relevant work, we are able to characterize the convergence when the non-smooth term is an indicator function. Specific applications of our framework include the non-smooth minimization, semidefinite programming, and minimization with linear inclusion constraints over a compact domain. Numerical evidence demonstrates the benefits of our framework. alternative_title: - PMLR article_processing_charge: No author: - first_name: Alp full_name: Yurtsever, Alp last_name: Yurtsever - first_name: Olivier full_name: Fercoq, Olivier last_name: Fercoq - 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: 'Yurtsever A, Fercoq O, Locatello F, Cevher V. A conditional gradient framework for composite convex minimization with applications to semidefinite programming. In: Proceedings of the 35th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:5727-5736.' apa: 'Yurtsever, A., Fercoq, O., Locatello, F., & Cevher, V. (2018). A conditional gradient framework for composite convex minimization with applications to semidefinite programming. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 5727–5736). Stockholm, Sweden: ML Research Press.' chicago: Yurtsever, Alp, Olivier Fercoq, Francesco Locatello, and Volkan Cevher. “A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming.” In Proceedings of the 35th International Conference on Machine Learning, 80:5727–36. ML Research Press, 2018. ieee: A. Yurtsever, O. Fercoq, F. Locatello, and V. Cevher, “A conditional gradient framework for composite convex minimization with applications to semidefinite programming,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 5727–5736. ista: 'Yurtsever A, Fercoq O, Locatello F, Cevher V. 2018. A conditional gradient framework for composite convex minimization with applications to semidefinite programming. Proceedings of the 35th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 80, 5727–5736.' mla: Yurtsever, Alp, et al. “A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming.” Proceedings of the 35th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 5727–36. short: A. Yurtsever, O. Fercoq, F. Locatello, V. Cevher, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 5727–5736. conference: end_date: 2018-07-15 location: Stockholm, Sweden name: 'ICML: International Conference on Machine Learning' start_date: 2018-07-10 date_created: 2023-08-22T14:16:01Z date_published: 2018-07-15T00:00:00Z date_updated: 2023-09-13T08:13:39Z day: '15' department: - _id: FrLo extern: '1' external_id: arxiv: - '1804.08544' intvolume: ' 80' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1804.08544 month: '07' oa: 1 oa_version: Preprint page: 5727-5736 publication: Proceedings of the 35th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: A conditional gradient framework for composite convex minimization with applications to semidefinite programming type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 80 year: '2018' ... --- _id: '282' abstract: - lang: eng text: Adaptive introgression is common in nature and can be driven by selection acting on multiple, linked genes. We explore the effects of polygenic selection on introgression under the infinitesimal model with linkage. This model assumes that the introgressing block has an effectively infinite number of genes, each with an infinitesimal effect on the trait under selection. The block is assumed to introgress under directional selection within a native population that is genetically homogeneous. We use individual-based simulations and a branching process approximation to compute various statistics of the introgressing block, and explore how these depend on parameters such as the map length and initial trait value associated with the introgressing block, the genetic variability along the block, and the strength of selection. Our results show that the introgression dynamics of a block under infinitesimal selection is qualitatively different from the dynamics of neutral introgression. We also find that in the long run, surviving descendant blocks are likely to have intermediate lengths, and clarify how the length is shaped by the interplay between linkage and infinitesimal selection. Our results suggest that it may be difficult to distinguish introgression of single loci from that of genomic blocks with multiple, tightly linked and weakly selected loci. article_processing_charge: No author: - first_name: Himani full_name: Sachdeva, Himani id: 42377A0A-F248-11E8-B48F-1D18A9856A87 last_name: Sachdeva - first_name: Nicholas H full_name: Barton, Nicholas H id: 4880FE40-F248-11E8-B48F-1D18A9856A87 last_name: Barton orcid: 0000-0002-8548-5240 citation: ama: Sachdeva H, Barton NH. Introgression of a block of genome under infinitesimal selection. Genetics. 2018;209(4):1279-1303. doi:10.1534/genetics.118.301018 apa: Sachdeva, H., & Barton, N. H. (2018). Introgression of a block of genome under infinitesimal selection. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.118.301018 chicago: Sachdeva, Himani, and Nicholas H Barton. “Introgression of a Block of Genome under Infinitesimal Selection.” Genetics. Genetics Society of America, 2018. https://doi.org/10.1534/genetics.118.301018. ieee: H. Sachdeva and N. H. Barton, “Introgression of a block of genome under infinitesimal selection,” Genetics, vol. 209, no. 4. Genetics Society of America, pp. 1279–1303, 2018. ista: Sachdeva H, Barton NH. 2018. Introgression of a block of genome under infinitesimal selection. Genetics. 209(4), 1279–1303. mla: Sachdeva, Himani, and Nicholas H. Barton. “Introgression of a Block of Genome under Infinitesimal Selection.” Genetics, vol. 209, no. 4, Genetics Society of America, 2018, pp. 1279–303, doi:10.1534/genetics.118.301018. short: H. Sachdeva, N.H. Barton, Genetics 209 (2018) 1279–1303. date_created: 2018-12-11T11:45:36Z date_published: 2018-08-01T00:00:00Z date_updated: 2023-09-13T08:22:32Z day: '01' department: - _id: NiBa doi: 10.1534/genetics.118.301018 external_id: isi: - '000440014100020' intvolume: ' 209' isi: 1 issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://www.biorxiv.org/content/early/2017/11/30/227082 month: '08' oa: 1 oa_version: Submitted Version page: 1279 - 1303 publication: Genetics publication_status: published publisher: Genetics Society of America publist_id: '7617' quality_controlled: '1' scopus_import: '1' status: public title: Introgression of a block of genome under infinitesimal selection type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 209 year: '2018' ... --- _id: '108' abstract: - lang: eng text: Universal hashing found a lot of applications in computer science. In cryptography the most important fact about universal families is the so called Leftover Hash Lemma, proved by Impagliazzo, Levin and Luby. In the language of modern cryptography it states that almost universal families are good extractors. In this work we provide a somewhat surprising characterization in the opposite direction. Namely, every extractor with sufficiently good parameters yields a universal family on a noticeable fraction of its inputs. Our proof technique is based on tools from extremal graph theory applied to the \'collision graph\' induced by the extractor, and may be of independent interest. We discuss possible applications to the theory of randomness extractors and non-malleable codes. alternative_title: - ISIT Proceedings article_processing_charge: No author: - first_name: Marciej full_name: Obremski, Marciej last_name: Obremski - first_name: Maciej full_name: Skorski, Maciej id: EC09FA6A-02D0-11E9-8223-86B7C91467DD last_name: Skorski citation: ama: 'Obremski M, Skórski M. Inverted leftover hash lemma. In: Vol 2018. IEEE; 2018. doi:10.1109/ISIT.2018.8437654' apa: 'Obremski, M., & Skórski, M. (2018). Inverted leftover hash lemma (Vol. 2018). Presented at the ISIT: International Symposium on Information Theory, Vail, CO, USA: IEEE. https://doi.org/10.1109/ISIT.2018.8437654' chicago: Obremski, Marciej, and Maciej Skórski. “Inverted Leftover Hash Lemma,” Vol. 2018. IEEE, 2018. https://doi.org/10.1109/ISIT.2018.8437654. ieee: 'M. Obremski and M. Skórski, “Inverted leftover hash lemma,” presented at the ISIT: International Symposium on Information Theory, Vail, CO, USA, 2018, vol. 2018.' ista: 'Obremski M, Skórski M. 2018. Inverted leftover hash lemma. ISIT: International Symposium on Information Theory, ISIT Proceedings, vol. 2018.' mla: Obremski, Marciej, and Maciej Skórski. Inverted Leftover Hash Lemma. Vol. 2018, IEEE, 2018, doi:10.1109/ISIT.2018.8437654. short: M. Obremski, M. Skórski, in:, IEEE, 2018. conference: end_date: 2018-06-22 location: Vail, CO, USA name: 'ISIT: International Symposium on Information Theory' start_date: '2018-06-17 ' date_created: 2018-12-11T11:44:40Z date_published: 2018-08-16T00:00:00Z date_updated: 2023-09-13T08:23:18Z day: '16' department: - _id: KrPi doi: 10.1109/ISIT.2018.8437654 external_id: isi: - '000448139300368' intvolume: ' 2018' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://eprint.iacr.org/2017/507 month: '08' oa: 1 oa_version: Submitted Version publication_status: published publisher: IEEE publist_id: '7946' quality_controlled: '1' scopus_import: '1' status: public title: Inverted leftover hash lemma type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2018 year: '2018' ... --- _id: '14204' abstract: - lang: eng text: Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers a generalized notion of directions. Exploiting the connection between the two algorithms, we present a unified analysis of both, providing affine invariant sublinear O(1/t) rates on smooth objectives and linear convergence on strongly convex objectives. As a byproduct of our affine invariant analysis of matching pursuit, our rates for steepest coordinate descent are the tightest known. Furthermore, we show the first accelerated convergence rate O(1/t2) for matching pursuit and steepest coordinate descent on convex objectives. 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: Anant full_name: Raj, Anant last_name: Raj - first_name: Sai Praneeth full_name: Karimireddy, Sai Praneeth last_name: Karimireddy - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Sebastian U. full_name: Stich, Sebastian U. last_name: Stich - first_name: Martin full_name: Jaggi, Martin last_name: Jaggi citation: ama: 'Locatello F, Raj A, Karimireddy SP, et al. On matching pursuit and coordinate descent. In: Proceedings of the 35th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:3198-3207.' apa: Locatello, F., Raj, A., Karimireddy, S. P., Rätsch, G., Schölkopf, B., Stich, S. U., & Jaggi, M. (2018). On matching pursuit and coordinate descent. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 3198–3207). ML Research Press. chicago: Locatello, Francesco, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, and Martin Jaggi. “On Matching Pursuit and Coordinate Descent.” In Proceedings of the 35th International Conference on Machine Learning, 80:3198–3207. ML Research Press, 2018. ieee: F. Locatello et al., “On matching pursuit and coordinate descent,” in Proceedings of the 35th International Conference on Machine Learning, 2018, vol. 80, pp. 3198–3207. ista: Locatello F, Raj A, Karimireddy SP, Rätsch G, Schölkopf B, Stich SU, Jaggi M. 2018. On matching pursuit and coordinate descent. Proceedings of the 35th International Conference on Machine Learning. , PMLR, vol. 80, 3198–3207. mla: Locatello, Francesco, et al. “On Matching Pursuit and Coordinate Descent.” Proceedings of the 35th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 3198–207. short: F. Locatello, A. Raj, S.P. Karimireddy, G. Rätsch, B. Schölkopf, S.U. Stich, M. Jaggi, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 3198–3207. date_created: 2023-08-22T14:16:25Z date_published: 2018-07-01T00:00:00Z date_updated: 2023-09-13T08:19:05Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '1803.09539' intvolume: ' 80' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1803.09539 month: '07' oa: 1 oa_version: Preprint page: 3198-3207 publication: Proceedings of the 35th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: On matching pursuit and coordinate descent type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 80 year: '2018' ... --- _id: '160' abstract: - lang: eng text: We present layered concurrent programs, a compact and expressive notation for specifying refinement proofs of concurrent programs. A layered concurrent program specifies a sequence of connected concurrent programs, from most concrete to most abstract, such that common parts of different programs are written exactly once. These programs are expressed in the ordinary syntax of imperative concurrent programs using gated atomic actions, sequencing, choice, and (recursive) procedure calls. Each concurrent program is automatically extracted from the layered program. We reduce refinement to the safety of a sequence of concurrent checker programs, one each to justify the connection between every two consecutive concurrent programs. These checker programs are also automatically extracted from the layered program. Layered concurrent programs have been implemented in the CIVL verifier which has been successfully used for the verification of several complex concurrent programs. alternative_title: - LNCS article_processing_charge: No author: - first_name: Bernhard full_name: Kragl, Bernhard id: 320FC952-F248-11E8-B48F-1D18A9856A87 last_name: Kragl orcid: 0000-0001-7745-9117 - first_name: Shaz full_name: Qadeer, Shaz last_name: Qadeer citation: ama: 'Kragl B, Qadeer S. Layered Concurrent Programs. In: Vol 10981. Springer; 2018:79-102. doi:10.1007/978-3-319-96145-3_5' apa: 'Kragl, B., & Qadeer, S. (2018). Layered Concurrent Programs (Vol. 10981, pp. 79–102). Presented at the CAV: Computer Aided Verification, Oxford, UK: Springer. https://doi.org/10.1007/978-3-319-96145-3_5' chicago: Kragl, Bernhard, and Shaz Qadeer. “Layered Concurrent Programs,” 10981:79–102. Springer, 2018. https://doi.org/10.1007/978-3-319-96145-3_5. ieee: 'B. Kragl and S. Qadeer, “Layered Concurrent Programs,” presented at the CAV: Computer Aided Verification, Oxford, UK, 2018, vol. 10981, pp. 79–102.' ista: 'Kragl B, Qadeer S. 2018. Layered Concurrent Programs. CAV: Computer Aided Verification, LNCS, vol. 10981, 79–102.' mla: Kragl, Bernhard, and Shaz Qadeer. Layered Concurrent Programs. Vol. 10981, Springer, 2018, pp. 79–102, doi:10.1007/978-3-319-96145-3_5. short: B. Kragl, S. Qadeer, in:, Springer, 2018, pp. 79–102. conference: end_date: 2018-07-17 location: Oxford, UK name: 'CAV: Computer Aided Verification' start_date: 2018-07-14 date_created: 2018-12-11T11:44:57Z date_published: 2018-07-18T00:00:00Z date_updated: 2023-09-13T08:45:09Z day: '18' ddc: - '000' department: - _id: ToHe doi: 10.1007/978-3-319-96145-3_5 external_id: isi: - '000491481600005' file: - access_level: open_access checksum: c64fff560fe5a7532ec10626ad1c215e content_type: application/pdf creator: dernst date_created: 2018-12-17T12:52:12Z date_updated: 2020-07-14T12:45:04Z file_id: '5705' file_name: 2018_LNCS_Kragl.pdf file_size: 1603844 relation: main_file file_date_updated: 2020-07-14T12:45:04Z has_accepted_license: '1' intvolume: ' 10981' isi: 1 language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '07' oa: 1 oa_version: Published Version page: 79 - 102 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize - _id: 25832EC2-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S 11407_N23 name: Rigorous Systems Engineering publication_status: published publisher: Springer publist_id: '7761' quality_controlled: '1' related_material: record: - id: '8332' relation: dissertation_contains status: public scopus_import: '1' status: public title: Layered Concurrent Programs 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: 10981 year: '2018' ...