---
_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'
...