---
_id: '6488'
abstract:
- lang: eng
text: We prove a central limit theorem for the difference of linear eigenvalue statistics
of a sample covariance matrix W˜ and its minor W. We find that the fluctuation
of this difference is much smaller than those of the individual linear statistics,
as a consequence of the strong correlation between the eigenvalues of W˜ and W.
Our result identifies the fluctuation of the spatial derivative of the approximate
Gaussian field in the recent paper by Dumitru and Paquette. Unlike in a similar
result for Wigner matrices, for sample covariance matrices, the fluctuation may
entirely vanish.
article_number: '2050006'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Giorgio
full_name: Cipolloni, Giorgio
id: 42198EFA-F248-11E8-B48F-1D18A9856A87
last_name: Cipolloni
orcid: 0000-0002-4901-7992
- first_name: László
full_name: Erdös, László
id: 4DBD5372-F248-11E8-B48F-1D18A9856A87
last_name: Erdös
orcid: 0000-0001-5366-9603
citation:
ama: 'Cipolloni G, Erdös L. Fluctuations for differences of linear eigenvalue statistics
for sample covariance matrices. Random Matrices: Theory and Application.
2020;9(3). doi:10.1142/S2010326320500069'
apa: 'Cipolloni, G., & Erdös, L. (2020). Fluctuations for differences of linear
eigenvalue statistics for sample covariance matrices. Random Matrices: Theory
and Application. World Scientific Publishing. https://doi.org/10.1142/S2010326320500069'
chicago: 'Cipolloni, Giorgio, and László Erdös. “Fluctuations for Differences of
Linear Eigenvalue Statistics for Sample Covariance Matrices.” Random Matrices:
Theory and Application. World Scientific Publishing, 2020. https://doi.org/10.1142/S2010326320500069.'
ieee: 'G. Cipolloni and L. Erdös, “Fluctuations for differences of linear eigenvalue
statistics for sample covariance matrices,” Random Matrices: Theory and Application,
vol. 9, no. 3. World Scientific Publishing, 2020.'
ista: 'Cipolloni G, Erdös L. 2020. Fluctuations for differences of linear eigenvalue
statistics for sample covariance matrices. Random Matrices: Theory and Application.
9(3), 2050006.'
mla: 'Cipolloni, Giorgio, and László Erdös. “Fluctuations for Differences of Linear
Eigenvalue Statistics for Sample Covariance Matrices.” Random Matrices: Theory
and Application, vol. 9, no. 3, 2050006, World Scientific Publishing, 2020,
doi:10.1142/S2010326320500069.'
short: 'G. Cipolloni, L. Erdös, Random Matrices: Theory and Application 9 (2020).'
date_created: 2019-05-26T21:59:14Z
date_published: 2020-07-01T00:00:00Z
date_updated: 2023-08-28T08:38:48Z
day: '01'
department:
- _id: LaEr
doi: 10.1142/S2010326320500069
ec_funded: 1
external_id:
arxiv:
- '1806.08751'
isi:
- '000547464400001'
intvolume: ' 9'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.08751
month: '07'
oa: 1
oa_version: Preprint
project:
- _id: 258DCDE6-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '338804'
name: Random matrices, universality and disordered quantum systems
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: 'Random Matrices: Theory and Application'
publication_identifier:
eissn:
- '20103271'
issn:
- '20103263'
publication_status: published
publisher: World Scientific Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Fluctuations for differences of linear eigenvalue statistics for sample covariance
matrices
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 9
year: '2020'
...
---
_id: '6563'
abstract:
- lang: eng
text: "This paper presents two algorithms. The first decides the existence of a
pointed homotopy between given simplicial maps \U0001D453,\U0001D454:\U0001D44B→\U0001D44C,
and the second computes the group [\U0001D6F4\U0001D44B,\U0001D44C]∗ of pointed
homotopy classes of maps from a suspension; in both cases, the target Y is assumed
simply connected. More generally, these algorithms work relative to \U0001D434⊆\U0001D44B."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Marek
full_name: Filakovský, Marek
id: 3E8AF77E-F248-11E8-B48F-1D18A9856A87
last_name: Filakovský
- first_name: Lukas
full_name: Vokřínek, Lukas
last_name: Vokřínek
citation:
ama: Filakovský M, Vokřínek L. Are two given maps homotopic? An algorithmic viewpoint.
Foundations of Computational Mathematics. 2020;20:311-330. doi:10.1007/s10208-019-09419-x
apa: Filakovský, M., & Vokřínek, L. (2020). Are two given maps homotopic? An
algorithmic viewpoint. Foundations of Computational Mathematics. Springer
Nature. https://doi.org/10.1007/s10208-019-09419-x
chicago: Filakovský, Marek, and Lukas Vokřínek. “Are Two given Maps Homotopic? An
Algorithmic Viewpoint.” Foundations of Computational Mathematics. Springer
Nature, 2020. https://doi.org/10.1007/s10208-019-09419-x.
ieee: M. Filakovský and L. Vokřínek, “Are two given maps homotopic? An algorithmic
viewpoint,” Foundations of Computational Mathematics, vol. 20. Springer
Nature, pp. 311–330, 2020.
ista: Filakovský M, Vokřínek L. 2020. Are two given maps homotopic? An algorithmic
viewpoint. Foundations of Computational Mathematics. 20, 311–330.
mla: Filakovský, Marek, and Lukas Vokřínek. “Are Two given Maps Homotopic? An Algorithmic
Viewpoint.” Foundations of Computational Mathematics, vol. 20, Springer
Nature, 2020, pp. 311–30, doi:10.1007/s10208-019-09419-x.
short: M. Filakovský, L. Vokřínek, Foundations of Computational Mathematics 20 (2020)
311–330.
date_created: 2019-06-16T21:59:14Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2023-08-17T13:50:44Z
day: '01'
department:
- _id: UlWa
doi: 10.1007/s10208-019-09419-x
external_id:
arxiv:
- '1312.2337'
isi:
- '000522437400004'
intvolume: ' 20'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1312.2337
month: '04'
oa: 1
oa_version: Preprint
page: 311-330
project:
- _id: 26611F5C-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P31312
name: Algorithms for Embeddings and Homotopy Theory
publication: Foundations of Computational Mathematics
publication_identifier:
eissn:
- '16153383'
issn:
- '16153375'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Are two given maps homotopic? An algorithmic viewpoint
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 20
year: '2020'
...
---
_id: '6593'
abstract:
- lang: eng
text: 'We consider the monotone variational inequality problem in a Hilbert space
and describe a projection-type method with inertial terms under the following
properties: (a) The method generates a strongly convergent iteration sequence;
(b) The method requires, at each iteration, only one projection onto the feasible
set and two evaluations of the operator; (c) The method is designed for variational
inequality for which the underline operator is monotone and uniformly continuous;
(d) The method includes an inertial term. The latter is also shown to speed up
the convergence in our numerical results. A comparison with some related methods
is given and indicates that the new method is promising.'
acknowledgement: The research of this author is supported by the ERC grant at the
IST.
article_processing_charge: No
article_type: original
author:
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
- first_name: Xiao-Huan
full_name: Li, Xiao-Huan
last_name: Li
- first_name: Qiao-Li
full_name: Dong, Qiao-Li
last_name: Dong
citation:
ama: Shehu Y, Li X-H, Dong Q-L. An efficient projection-type method for monotone
variational inequalities in Hilbert spaces. Numerical Algorithms. 2020;84:365-388.
doi:10.1007/s11075-019-00758-y
apa: Shehu, Y., Li, X.-H., & Dong, Q.-L. (2020). An efficient projection-type
method for monotone variational inequalities in Hilbert spaces. Numerical Algorithms.
Springer Nature. https://doi.org/10.1007/s11075-019-00758-y
chicago: Shehu, Yekini, Xiao-Huan Li, and Qiao-Li Dong. “An Efficient Projection-Type
Method for Monotone Variational Inequalities in Hilbert Spaces.” Numerical
Algorithms. Springer Nature, 2020. https://doi.org/10.1007/s11075-019-00758-y.
ieee: Y. Shehu, X.-H. Li, and Q.-L. Dong, “An efficient projection-type method for
monotone variational inequalities in Hilbert spaces,” Numerical Algorithms,
vol. 84. Springer Nature, pp. 365–388, 2020.
ista: Shehu Y, Li X-H, Dong Q-L. 2020. An efficient projection-type method for monotone
variational inequalities in Hilbert spaces. Numerical Algorithms. 84, 365–388.
mla: Shehu, Yekini, et al. “An Efficient Projection-Type Method for Monotone Variational
Inequalities in Hilbert Spaces.” Numerical Algorithms, vol. 84, Springer
Nature, 2020, pp. 365–88, doi:10.1007/s11075-019-00758-y.
short: Y. Shehu, X.-H. Li, Q.-L. Dong, Numerical Algorithms 84 (2020) 365–388.
date_created: 2019-06-27T20:09:33Z
date_published: 2020-05-01T00:00:00Z
date_updated: 2023-08-17T13:51:18Z
day: '01'
ddc:
- '000'
department:
- _id: VlKo
doi: 10.1007/s11075-019-00758-y
ec_funded: 1
external_id:
isi:
- '000528979000015'
file:
- access_level: open_access
checksum: bb1a1eb3ebb2df380863d0db594673ba
content_type: application/pdf
creator: kschuh
date_created: 2019-10-01T13:14:10Z
date_updated: 2020-07-14T12:47:34Z
file_id: '6927'
file_name: ExtragradientMethodPaper.pdf
file_size: 359654
relation: main_file
file_date_updated: 2020-07-14T12:47:34Z
has_accepted_license: '1'
intvolume: ' 84'
isi: 1
language:
- iso: eng
month: '05'
oa: 1
oa_version: Submitted Version
page: 365-388
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Numerical Algorithms
publication_identifier:
eissn:
- 1572-9265
issn:
- 1017-1398
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: An efficient projection-type method for monotone variational inequalities in
Hilbert spaces
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 84
year: '2020'
...
---
_id: '6649'
abstract:
- lang: eng
text: "While Hartree–Fock theory is well established as a fundamental approximation
for interacting fermions, it has been unclear how to describe corrections to it
due to many-body correlations. In this paper we start from the Hartree–Fock state
given by plane waves and introduce collective particle–hole pair excitations.
These pairs can be approximately described by a bosonic quadratic Hamiltonian.
We use Bogoliubov theory to construct a trial state yielding a rigorous Gell-Mann–Brueckner–type
upper bound to the ground state energy. Our result justifies the random-phase
approximation in the mean-field scaling regime, for repulsive, regular interaction
potentials.\r\n"
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Niels P
full_name: Benedikter, Niels P
id: 3DE6C32A-F248-11E8-B48F-1D18A9856A87
last_name: Benedikter
orcid: 0000-0002-1071-6091
- first_name: Phan Thành
full_name: Nam, Phan Thành
last_name: Nam
- first_name: Marcello
full_name: Porta, Marcello
last_name: Porta
- first_name: Benjamin
full_name: Schlein, Benjamin
last_name: Schlein
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
citation:
ama: Benedikter NP, Nam PT, Porta M, Schlein B, Seiringer R. Optimal upper bound
for the correlation energy of a Fermi gas in the mean-field regime. Communications
in Mathematical Physics. 2020;374:2097–2150. doi:10.1007/s00220-019-03505-5
apa: Benedikter, N. P., Nam, P. T., Porta, M., Schlein, B., & Seiringer, R.
(2020). Optimal upper bound for the correlation energy of a Fermi gas in the mean-field
regime. Communications in Mathematical Physics. Springer Nature. https://doi.org/10.1007/s00220-019-03505-5
chicago: Benedikter, Niels P, Phan Thành Nam, Marcello Porta, Benjamin Schlein,
and Robert Seiringer. “Optimal Upper Bound for the Correlation Energy of a Fermi
Gas in the Mean-Field Regime.” Communications in Mathematical Physics.
Springer Nature, 2020. https://doi.org/10.1007/s00220-019-03505-5.
ieee: N. P. Benedikter, P. T. Nam, M. Porta, B. Schlein, and R. Seiringer, “Optimal
upper bound for the correlation energy of a Fermi gas in the mean-field regime,”
Communications in Mathematical Physics, vol. 374. Springer Nature, pp.
2097–2150, 2020.
ista: Benedikter NP, Nam PT, Porta M, Schlein B, Seiringer R. 2020. Optimal upper
bound for the correlation energy of a Fermi gas in the mean-field regime. Communications
in Mathematical Physics. 374, 2097–2150.
mla: Benedikter, Niels P., et al. “Optimal Upper Bound for the Correlation Energy
of a Fermi Gas in the Mean-Field Regime.” Communications in Mathematical Physics,
vol. 374, Springer Nature, 2020, pp. 2097–2150, doi:10.1007/s00220-019-03505-5.
short: N.P. Benedikter, P.T. Nam, M. Porta, B. Schlein, R. Seiringer, Communications
in Mathematical Physics 374 (2020) 2097–2150.
date_created: 2019-07-18T13:30:04Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-08-17T13:51:50Z
day: '01'
ddc:
- '530'
department:
- _id: RoSe
doi: 10.1007/s00220-019-03505-5
ec_funded: 1
external_id:
arxiv:
- '1809.01902'
isi:
- '000527910700019'
file:
- access_level: open_access
checksum: f9dd6dd615a698f1d3636c4a092fed23
content_type: application/pdf
creator: dernst
date_created: 2019-07-24T07:19:10Z
date_updated: 2020-07-14T12:47:35Z
file_id: '6668'
file_name: 2019_CommMathPhysics_Benedikter.pdf
file_size: 853289
relation: main_file
file_date_updated: 2020-07-14T12:47:35Z
has_accepted_license: '1'
intvolume: ' 374'
isi: 1
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 2097–2150
project:
- _id: 3AC91DDA-15DF-11EA-824D-93A3E7B544D1
call_identifier: FWF
name: FWF Open Access Fund
- _id: 25C878CE-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27533_N27
name: Structure of the Excitation Spectrum for Many-Body Quantum Systems
- _id: 25C6DC12-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '694227'
name: Analysis of quantum many-body systems
publication: Communications in Mathematical Physics
publication_identifier:
eissn:
- 1432-0916
issn:
- 0010-3616
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Optimal upper bound for the correlation energy of a Fermi gas in the mean-field
regime
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 374
year: '2020'
...
---
_id: '6748'
abstract:
- lang: eng
text: "Fitting a function by using linear combinations of a large number N of `simple'
components is one of the most fruitful ideas in statistical learning. This idea
lies at the core of a variety of methods, from two-layer neural networks to kernel
regression, to boosting. In general, the resulting risk minimization problem is
non-convex and is solved by gradient descent or its variants. Unfortunately, little
is known about global convergence properties of these approaches.\r\nHere we consider
the problem of learning a concave function f on a compact convex domain Ω⊆ℝd,
using linear combinations of `bump-like' components (neurons). The parameters
to be fitted are the centers of N bumps, and the resulting empirical risk minimization
problem is highly non-convex. We prove that, in the limit in which the number
of neurons diverges, the evolution of gradient descent converges to a Wasserstein
gradient flow in the space of probability distributions over Ω. Further, when
the bump width δ tends to 0, this gradient flow has a limit which is a viscous
porous medium equation. Remarkably, the cost function optimized by this gradient
flow exhibits a special property known as displacement convexity, which implies
exponential convergence rates for N→∞, δ→0. Surprisingly, this asymptotic theory
appears to capture well the behavior for moderate values of δ,N. Explaining this
phenomenon, and understanding the dependence on δ,N in a quantitative manner remains
an outstanding challenge."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Adel
full_name: Javanmard, Adel
last_name: Javanmard
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Andrea
full_name: Montanari, Andrea
last_name: Montanari
citation:
ama: Javanmard A, Mondelli M, Montanari A. Analysis of a two-layer neural network
via displacement convexity. Annals of Statistics. 2020;48(6):3619-3642.
doi:10.1214/20-AOS1945
apa: Javanmard, A., Mondelli, M., & Montanari, A. (2020). Analysis of a two-layer
neural network via displacement convexity. Annals of Statistics. Institute
of Mathematical Statistics. https://doi.org/10.1214/20-AOS1945
chicago: Javanmard, Adel, Marco Mondelli, and Andrea Montanari. “Analysis of a Two-Layer
Neural Network via Displacement Convexity.” Annals of Statistics. Institute
of Mathematical Statistics, 2020. https://doi.org/10.1214/20-AOS1945.
ieee: A. Javanmard, M. Mondelli, and A. Montanari, “Analysis of a two-layer neural
network via displacement convexity,” Annals of Statistics, vol. 48, no.
6. Institute of Mathematical Statistics, pp. 3619–3642, 2020.
ista: Javanmard A, Mondelli M, Montanari A. 2020. Analysis of a two-layer neural
network via displacement convexity. Annals of Statistics. 48(6), 3619–3642.
mla: Javanmard, Adel, et al. “Analysis of a Two-Layer Neural Network via Displacement
Convexity.” Annals of Statistics, vol. 48, no. 6, Institute of Mathematical
Statistics, 2020, pp. 3619–42, doi:10.1214/20-AOS1945.
short: A. Javanmard, M. Mondelli, A. Montanari, Annals of Statistics 48 (2020) 3619–3642.
date_created: 2019-07-31T09:39:42Z
date_published: 2020-12-11T00:00:00Z
date_updated: 2024-03-06T08:28:50Z
day: '11'
department:
- _id: MaMo
doi: 10.1214/20-AOS1945
external_id:
arxiv:
- '1901.01375'
isi:
- '000598369200021'
intvolume: ' 48'
isi: 1
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1901.01375
month: '12'
oa: 1
oa_version: Preprint
page: 3619-3642
publication: Annals of Statistics
publication_identifier:
eissn:
- 1941-7330
issn:
- 1932-6157
publication_status: published
publisher: Institute of Mathematical Statistics
quality_controlled: '1'
status: public
title: Analysis of a two-layer neural network via displacement convexity
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 48
year: '2020'
...
---
_id: '6761'
abstract:
- lang: eng
text: In resource allocation games, selfish players share resources that are needed
in order to fulfill their objectives. The cost of using a resource depends on
the load on it. In the traditional setting, the players make their choices concurrently
and in one-shot. That is, a strategy for a player is a subset of the resources.
We introduce and study dynamic resource allocation games. In this setting, the
game proceeds in phases. In each phase each player chooses one resource. A scheduler
dictates the order in which the players proceed in a phase, possibly scheduling
several players to proceed concurrently. The game ends when each player has collected
a set of resources that fulfills his objective. The cost for each player then
depends on this set as well as on the load on the resources in it – we consider
both congestion and cost-sharing games. We argue that the dynamic setting is the
suitable setting for many applications in practice. We study the stability of
dynamic resource allocation games, where the appropriate notion of stability is
that of subgame perfect equilibrium, study the inefficiency incurred due to selfish
behavior, and also study problems that are particular to the dynamic setting,
like constraints on the order in which resources can be chosen or the problem
of finding a scheduler that achieves stability.
article_processing_charge: No
article_type: original
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Orna
full_name: Kupferman, Orna
last_name: Kupferman
citation:
ama: Avni G, Henzinger TA, Kupferman O. Dynamic resource allocation games. Theoretical
Computer Science. 2020;807:42-55. doi:10.1016/j.tcs.2019.06.031
apa: Avni, G., Henzinger, T. A., & Kupferman, O. (2020). Dynamic resource allocation
games. Theoretical Computer Science. Elsevier. https://doi.org/10.1016/j.tcs.2019.06.031
chicago: Avni, Guy, Thomas A Henzinger, and Orna Kupferman. “Dynamic Resource Allocation
Games.” Theoretical Computer Science. Elsevier, 2020. https://doi.org/10.1016/j.tcs.2019.06.031.
ieee: G. Avni, T. A. Henzinger, and O. Kupferman, “Dynamic resource allocation games,”
Theoretical Computer Science, vol. 807. Elsevier, pp. 42–55, 2020.
ista: Avni G, Henzinger TA, Kupferman O. 2020. Dynamic resource allocation games.
Theoretical Computer Science. 807, 42–55.
mla: Avni, Guy, et al. “Dynamic Resource Allocation Games.” Theoretical Computer
Science, vol. 807, Elsevier, 2020, pp. 42–55, doi:10.1016/j.tcs.2019.06.031.
short: G. Avni, T.A. Henzinger, O. Kupferman, Theoretical Computer Science 807 (2020)
42–55.
date_created: 2019-08-04T21:59:20Z
date_published: 2020-02-06T00:00:00Z
date_updated: 2023-08-17T13:52:49Z
day: '06'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1016/j.tcs.2019.06.031
external_id:
isi:
- '000512219400004'
file:
- access_level: open_access
checksum: e86635417f45eb2cd75778f91382f737
content_type: application/pdf
creator: dernst
date_created: 2020-10-09T06:31:22Z
date_updated: 2020-10-09T06:31:22Z
file_id: '8639'
file_name: 2020_TheoreticalCS_Avni.pdf
file_size: 1413001
relation: main_file
success: 1
file_date_updated: 2020-10-09T06:31:22Z
has_accepted_license: '1'
intvolume: ' 807'
isi: 1
language:
- iso: eng
month: '02'
oa: 1
oa_version: Submitted Version
page: 42-55
project:
- _id: 25F2ACDE-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11402-N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 264B3912-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: M02369
name: Formal Methods meets Algorithmic Game Theory
publication: Theoretical Computer Science
publication_identifier:
issn:
- '03043975'
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
record:
- id: '1341'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Dynamic resource allocation games
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 807
year: '2020'
...
---
_id: '6796'
abstract:
- lang: eng
text: Nearby grid cells have been observed to express a remarkable degree of long-rangeorder,
which is often idealized as extending potentially to infinity. Yet their strict
peri-odic firing and ensemble coherence are theoretically possible only in flat
environments, much unlike the burrows which rodents usually live in. Are the symmetrical,
coherent grid maps inferred in the lab relevant to chart their way in their natural
habitat? We consider spheres as simple models of curved environments and waiting
for the appropriate experiments to be performed, we use our adaptation model to
predict what grid maps would emerge in a network with the same type of recurrent
connections, which on the plane produce coherence among the units. We find that
on the sphere such connections distort the maps that single grid units would express
on their own, and aggregate them into clusters. When remapping to a different
spherical environment, units in each cluster maintain only partial coherence,
similar to what is observed in disordered materials, such as spin glasses.
article_processing_charge: No
article_type: original
author:
- first_name: Federico
full_name: Stella, Federico
id: 39AF1E74-F248-11E8-B48F-1D18A9856A87
last_name: Stella
orcid: 0000-0001-9439-3148
- first_name: Eugenio
full_name: Urdapilleta, Eugenio
last_name: Urdapilleta
- first_name: Yifan
full_name: Luo, Yifan
last_name: Luo
- first_name: Alessandro
full_name: Treves, Alessandro
last_name: Treves
citation:
ama: Stella F, Urdapilleta E, Luo Y, Treves A. Partial coherence and frustration
in self-organizing spherical grids. Hippocampus. 2020;30(4):302-313. doi:10.1002/hipo.23144
apa: Stella, F., Urdapilleta, E., Luo, Y., & Treves, A. (2020). Partial coherence
and frustration in self-organizing spherical grids. Hippocampus. Wiley.
https://doi.org/10.1002/hipo.23144
chicago: Stella, Federico, Eugenio Urdapilleta, Yifan Luo, and Alessandro Treves.
“Partial Coherence and Frustration in Self-Organizing Spherical Grids.” Hippocampus.
Wiley, 2020. https://doi.org/10.1002/hipo.23144.
ieee: F. Stella, E. Urdapilleta, Y. Luo, and A. Treves, “Partial coherence and frustration
in self-organizing spherical grids,” Hippocampus, vol. 30, no. 4. Wiley,
pp. 302–313, 2020.
ista: Stella F, Urdapilleta E, Luo Y, Treves A. 2020. Partial coherence and frustration
in self-organizing spherical grids. Hippocampus. 30(4), 302–313.
mla: Stella, Federico, et al. “Partial Coherence and Frustration in Self-Organizing
Spherical Grids.” Hippocampus, vol. 30, no. 4, Wiley, 2020, pp. 302–13,
doi:10.1002/hipo.23144.
short: F. Stella, E. Urdapilleta, Y. Luo, A. Treves, Hippocampus 30 (2020) 302–313.
date_created: 2019-08-11T21:59:24Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2023-08-17T13:53:14Z
day: '01'
ddc:
- '570'
department:
- _id: JoCs
doi: 10.1002/hipo.23144
external_id:
isi:
- '000477299600001'
pmid:
- '31339190'
file:
- access_level: open_access
checksum: 7b54d22bfbfc0d1188a9ea24d985bfb2
content_type: application/pdf
creator: dernst
date_created: 2019-08-12T07:53:33Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6800'
file_name: 2019_Hippocampus_Stella.pdf
file_size: 2370658
relation: main_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
intvolume: ' 30'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 302-313
pmid: 1
publication: Hippocampus
publication_identifier:
eissn:
- '10981063'
issn:
- '10509631'
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Partial coherence and frustration in self-organizing spherical grids
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 30
year: '2020'
...
---
_id: '6808'
abstract:
- lang: eng
text: Super-resolution fluorescence microscopy has become an important catalyst
for discovery in the life sciences. In STimulated Emission Depletion (STED) microscopy,
a pattern of light drives fluorophores from a signal-emitting on-state to a non-signalling
off-state. Only emitters residing in a sub-diffraction volume around an intensity
minimum are allowed to fluoresce, rendering them distinguishable from the nearby,
but dark fluorophores. STED routinely achieves resolution in the few tens of nanometers
range in biological samples and is suitable for live imaging. Here, we review
the working principle of STED and provide general guidelines for successful STED
imaging. The strive for ever higher resolution comes at the cost of increased
light burden. We discuss techniques to reduce light exposure and mitigate its
detrimental effects on the specimen. These include specialized illumination strategies
as well as protecting fluorophores from photobleaching mediated by high-intensity
STED light. This opens up the prospect of volumetric imaging in living cells and
tissues with diffraction-unlimited resolution in all three spatial dimensions.
article_processing_charge: No
article_type: original
author:
- first_name: Wiebke
full_name: Jahr, Wiebke
id: 425C1CE8-F248-11E8-B48F-1D18A9856A87
last_name: Jahr
- first_name: Philipp
full_name: Velicky, Philipp
id: 39BDC62C-F248-11E8-B48F-1D18A9856A87
last_name: Velicky
orcid: 0000-0002-2340-7431
- first_name: Johann G
full_name: Danzl, Johann G
id: 42EFD3B6-F248-11E8-B48F-1D18A9856A87
last_name: Danzl
orcid: 0000-0001-8559-3973
citation:
ama: Jahr W, Velicky P, Danzl JG. Strategies to maximize performance in STimulated
Emission Depletion (STED) nanoscopy of biological specimens. Methods. 2020;174(3):27-41.
doi:10.1016/j.ymeth.2019.07.019
apa: Jahr, W., Velicky, P., & Danzl, J. G. (2020). Strategies to maximize performance
in STimulated Emission Depletion (STED) nanoscopy of biological specimens. Methods.
Elsevier. https://doi.org/10.1016/j.ymeth.2019.07.019
chicago: Jahr, Wiebke, Philipp Velicky, and Johann G Danzl. “Strategies to Maximize
Performance in STimulated Emission Depletion (STED) Nanoscopy of Biological Specimens.”
Methods. Elsevier, 2020. https://doi.org/10.1016/j.ymeth.2019.07.019.
ieee: W. Jahr, P. Velicky, and J. G. Danzl, “Strategies to maximize performance
in STimulated Emission Depletion (STED) nanoscopy of biological specimens,” Methods,
vol. 174, no. 3. Elsevier, pp. 27–41, 2020.
ista: Jahr W, Velicky P, Danzl JG. 2020. Strategies to maximize performance in STimulated
Emission Depletion (STED) nanoscopy of biological specimens. Methods. 174(3),
27–41.
mla: Jahr, Wiebke, et al. “Strategies to Maximize Performance in STimulated Emission
Depletion (STED) Nanoscopy of Biological Specimens.” Methods, vol. 174,
no. 3, Elsevier, 2020, pp. 27–41, doi:10.1016/j.ymeth.2019.07.019.
short: W. Jahr, P. Velicky, J.G. Danzl, Methods 174 (2020) 27–41.
date_created: 2019-08-12T16:36:32Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-08-17T13:59:57Z
day: '01'
department:
- _id: JoDa
doi: 10.1016/j.ymeth.2019.07.019
external_id:
isi:
- '000525860400005'
pmid:
- '31344404'
intvolume: ' 174'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100895/
month: '03'
oa: 1
oa_version: Submitted Version
page: 27-41
pmid: 1
project:
- _id: 265CB4D0-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I03600
name: Optical control of synaptic function via adhesion molecules
- _id: 2668BFA0-B435-11E9-9278-68D0E5697425
grant_number: LT00057
name: High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration
publication: Methods
publication_identifier:
issn:
- 1046-2023
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Strategies to maximize performance in STimulated Emission Depletion (STED)
nanoscopy of biological specimens
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 174
year: '2020'
...
---
_id: '6906'
abstract:
- lang: eng
text: We consider systems of bosons trapped in a box, in the Gross–Pitaevskii regime.
We show that low-energy states exhibit complete Bose–Einstein condensation with
an optimal bound on the number of orthogonal excitations. This extends recent
results obtained in Boccato et al. (Commun Math Phys 359(3):975–1026, 2018), removing
the assumption of small interaction potential.
acknowledgement: "We would like to thank P. T. Nam and R. Seiringer for several useful
discussions and\r\nfor suggesting us to use the localization techniques from [9].
C. Boccato has received funding from the\r\nEuropean Research Council (ERC) under
the programme Horizon 2020 (Grant Agreement 694227). B. Schlein gratefully acknowledges
support from the NCCR SwissMAP and from the Swiss National Foundation of Science
(Grant No. 200020_1726230) through the SNF Grant “Dynamical and energetic properties
of Bose–Einstein condensates”."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Chiara
full_name: Boccato, Chiara
id: 342E7E22-F248-11E8-B48F-1D18A9856A87
last_name: Boccato
- first_name: Christian
full_name: Brennecke, Christian
last_name: Brennecke
- first_name: Serena
full_name: Cenatiempo, Serena
last_name: Cenatiempo
- first_name: Benjamin
full_name: Schlein, Benjamin
last_name: Schlein
citation:
ama: Boccato C, Brennecke C, Cenatiempo S, Schlein B. Optimal rate for Bose-Einstein
condensation in the Gross-Pitaevskii regime. Communications in Mathematical
Physics. 2020;376:1311-1395. doi:10.1007/s00220-019-03555-9
apa: Boccato, C., Brennecke, C., Cenatiempo, S., & Schlein, B. (2020). Optimal
rate for Bose-Einstein condensation in the Gross-Pitaevskii regime. Communications
in Mathematical Physics. Springer. https://doi.org/10.1007/s00220-019-03555-9
chicago: Boccato, Chiara, Christian Brennecke, Serena Cenatiempo, and Benjamin Schlein.
“Optimal Rate for Bose-Einstein Condensation in the Gross-Pitaevskii Regime.”
Communications in Mathematical Physics. Springer, 2020. https://doi.org/10.1007/s00220-019-03555-9.
ieee: C. Boccato, C. Brennecke, S. Cenatiempo, and B. Schlein, “Optimal rate for
Bose-Einstein condensation in the Gross-Pitaevskii regime,” Communications
in Mathematical Physics, vol. 376. Springer, pp. 1311–1395, 2020.
ista: Boccato C, Brennecke C, Cenatiempo S, Schlein B. 2020. Optimal rate for Bose-Einstein
condensation in the Gross-Pitaevskii regime. Communications in Mathematical Physics.
376, 1311–1395.
mla: Boccato, Chiara, et al. “Optimal Rate for Bose-Einstein Condensation in the
Gross-Pitaevskii Regime.” Communications in Mathematical Physics, vol.
376, Springer, 2020, pp. 1311–95, doi:10.1007/s00220-019-03555-9.
short: C. Boccato, C. Brennecke, S. Cenatiempo, B. Schlein, Communications in Mathematical
Physics 376 (2020) 1311–1395.
date_created: 2019-09-24T17:30:59Z
date_published: 2020-06-01T00:00:00Z
date_updated: 2024-02-22T13:33:02Z
day: '01'
department:
- _id: RoSe
doi: 10.1007/s00220-019-03555-9
ec_funded: 1
external_id:
arxiv:
- '1812.03086'
isi:
- '000536053300012'
intvolume: ' 376'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1812.03086
month: '06'
oa: 1
oa_version: Preprint
page: 1311-1395
project:
- _id: 25C6DC12-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '694227'
name: Analysis of quantum many-body systems
publication: Communications in Mathematical Physics
publication_identifier:
eissn:
- 1432-0916
issn:
- 0010-3616
publication_status: published
publisher: Springer
quality_controlled: '1'
scopus_import: '1'
status: public
title: Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 376
year: '2020'
...
---
_id: '10012'
abstract:
- lang: eng
text: We prove that in the absence of topological changes, the notion of BV solutions
to planar multiphase mean curvature flow does not allow for a mechanism for (unphysical)
non-uniqueness. Our approach is based on the local structure of the energy landscape
near a classical evolution by mean curvature. Mean curvature flow being the gradient
flow of the surface energy functional, we develop a gradient-flow analogue of
the notion of calibrations. Just like the existence of a calibration guarantees
that one has reached a global minimum in the energy landscape, the existence of
a "gradient flow calibration" ensures that the route of steepest descent in the
energy landscape is unique and stable.
acknowledgement: Parts of the paper were written during the visit of the authors to
the Hausdorff Research Institute for Mathematics (HIM), University of Bonn, in the
framework of the trimester program “Evolution of Interfaces”. The support and the
hospitality of HIM are gratefully acknowledged. This project has received funding
from the European Union’s Horizon 2020 research and innovation programme under the
Marie Sklodowska-Curie Grant Agreement No. 665385.
article_number: '2003.05478'
article_processing_charge: No
arxiv: 1
author:
- first_name: Julian L
full_name: Fischer, Julian L
id: 2C12A0B0-F248-11E8-B48F-1D18A9856A87
last_name: Fischer
orcid: 0000-0002-0479-558X
- first_name: Sebastian
full_name: Hensel, Sebastian
id: 4D23B7DA-F248-11E8-B48F-1D18A9856A87
last_name: Hensel
orcid: 0000-0001-7252-8072
- first_name: Tim
full_name: Laux, Tim
last_name: Laux
- first_name: Thilo
full_name: Simon, Thilo
last_name: Simon
citation:
ama: 'Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy landscape
in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions.
arXiv.'
apa: 'Fischer, J. L., Hensel, S., Laux, T., & Simon, T. (n.d.). The local structure
of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness
and stability of evolutions. arXiv.'
chicago: 'Fischer, Julian L, Sebastian Hensel, Tim Laux, and Thilo Simon. “The Local
Structure of the Energy Landscape in Multiphase Mean Curvature Flow: Weak-Strong
Uniqueness and Stability of Evolutions.” ArXiv, n.d.'
ieee: 'J. L. Fischer, S. Hensel, T. Laux, and T. Simon, “The local structure of
the energy landscape in multiphase mean curvature flow: weak-strong uniqueness
and stability of evolutions,” arXiv. .'
ista: 'Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy
landscape in multiphase mean curvature flow: weak-strong uniqueness and stability
of evolutions. arXiv, 2003.05478.'
mla: 'Fischer, Julian L., et al. “The Local Structure of the Energy Landscape in
Multiphase Mean Curvature Flow: Weak-Strong Uniqueness and Stability of Evolutions.”
ArXiv, 2003.05478.'
short: J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.).
date_created: 2021-09-13T12:17:11Z
date_published: 2020-03-11T00:00:00Z
date_updated: 2023-09-07T13:30:45Z
day: '11'
department:
- _id: JuFi
ec_funded: 1
external_id:
arxiv:
- '2003.05478'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2003.05478
month: '03'
oa: 1
oa_version: Preprint
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: arXiv
publication_status: submitted
related_material:
record:
- id: '10007'
relation: dissertation_contains
status: public
status: public
title: 'The local structure of the energy landscape in multiphase mean curvature flow:
weak-strong uniqueness and stability of evolutions'
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '10022'
abstract:
- lang: eng
text: We consider finite-volume approximations of Fokker-Planck equations on bounded
convex domains in R^d and study the corresponding gradient flow structures. We
reprove the convergence of the discrete to continuous Fokker-Planck equation via
the method of Evolutionary Γ-convergence, i.e., we pass to the limit at the level
of the gradient flow structures, generalising the one-dimensional result obtained
by Disser and Liero. The proof is of variational nature and relies on a Mosco
convergence result for functionals in the discrete-to-continuum limit that is
of independent interest. Our results apply to arbitrary regular meshes, even though
the associated discrete transport distances may fail to converge to the Wasserstein
distance in this generality.
acknowledgement: This work is supported by the European Research Council (ERC) under
the European Union’s Horizon 2020 research and innovation programme (grant agreement
No 716117) and by the Austrian Science Fund (FWF), grants No F65 and W1245.
article_number: '2008.10962'
article_processing_charge: No
arxiv: 1
author:
- first_name: Dominik L
full_name: Forkert, Dominik L
id: 35C79D68-F248-11E8-B48F-1D18A9856A87
last_name: Forkert
- first_name: Jan
full_name: Maas, Jan
id: 4C5696CE-F248-11E8-B48F-1D18A9856A87
last_name: Maas
orcid: 0000-0002-0845-1338
- first_name: Lorenzo
full_name: Portinale, Lorenzo
id: 30AD2CBC-F248-11E8-B48F-1D18A9856A87
last_name: Portinale
citation:
ama: Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient
flow structures for Fokker-Planck equations in multiple dimensions. arXiv.
apa: Forkert, D. L., Maas, J., & Portinale, L. (n.d.). Evolutionary Γ-convergence
of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions.
arXiv.
chicago: Forkert, Dominik L, Jan Maas, and Lorenzo Portinale. “Evolutionary Γ-Convergence
of Entropic Gradient Flow Structures for Fokker-Planck Equations in Multiple Dimensions.”
ArXiv, n.d.
ieee: D. L. Forkert, J. Maas, and L. Portinale, “Evolutionary Γ-convergence of entropic
gradient flow structures for Fokker-Planck equations in multiple dimensions,”
arXiv. .
ista: Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient
flow structures for Fokker-Planck equations in multiple dimensions. arXiv, 2008.10962.
mla: Forkert, Dominik L., et al. “Evolutionary Γ-Convergence of Entropic Gradient
Flow Structures for Fokker-Planck Equations in Multiple Dimensions.” ArXiv,
2008.10962.
short: D.L. Forkert, J. Maas, L. Portinale, ArXiv (n.d.).
date_created: 2021-09-17T10:57:27Z
date_published: 2020-08-25T00:00:00Z
date_updated: 2023-09-07T13:31:05Z
day: '25'
department:
- _id: JaMa
ec_funded: 1
external_id:
arxiv:
- '2008.10962'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2008.10962
month: '08'
oa: 1
oa_version: Preprint
page: '33'
project:
- _id: 256E75B8-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '716117'
name: Optimal Transport and Stochastic Dynamics
- _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2
grant_number: F6504
name: Taming Complexity in Partial Differential Systems
publication: arXiv
publication_status: submitted
related_material:
record:
- id: '11739'
relation: later_version
status: public
- id: '10030'
relation: dissertation_contains
status: public
status: public
title: Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck
equations in multiple dimensions
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '10328'
abstract:
- lang: eng
text: We discus noise channels in coherent electro-optic up-conversion between microwave
and optical fields, in particular due to optical heating. We also report on a
novel configuration, which promises to be flexible and highly efficient.
alternative_title:
- OSA Technical Digest
article_number: QTu8A.1
article_processing_charge: No
author:
- first_name: Nicholas J.
full_name: Lambert, Nicholas J.
last_name: Lambert
- first_name: Sonia
full_name: Mobassem, Sonia
last_name: Mobassem
- first_name: Alfredo R
full_name: Rueda Sanchez, Alfredo R
id: 3B82B0F8-F248-11E8-B48F-1D18A9856A87
last_name: Rueda Sanchez
orcid: 0000-0001-6249-5860
- first_name: Harald G.L.
full_name: Schwefel, Harald G.L.
last_name: Schwefel
citation:
ama: 'Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. New designs and noise
channels in electro-optic microwave to optical up-conversion. In: OSA Quantum
2.0 Conference. Optica Publishing Group; 2020. doi:10.1364/QUANTUM.2020.QTu8A.1'
apa: 'Lambert, N. J., Mobassem, S., Rueda Sanchez, A. R., & Schwefel, H. G.
L. (2020). New designs and noise channels in electro-optic microwave to optical
up-conversion. In OSA Quantum 2.0 Conference. Washington, DC, United States:
Optica Publishing Group. https://doi.org/10.1364/QUANTUM.2020.QTu8A.1'
chicago: Lambert, Nicholas J., Sonia Mobassem, Alfredo R Rueda Sanchez, and Harald
G.L. Schwefel. “New Designs and Noise Channels in Electro-Optic Microwave to Optical
up-Conversion.” In OSA Quantum 2.0 Conference. Optica Publishing Group,
2020. https://doi.org/10.1364/QUANTUM.2020.QTu8A.1.
ieee: N. J. Lambert, S. Mobassem, A. R. Rueda Sanchez, and H. G. L. Schwefel, “New
designs and noise channels in electro-optic microwave to optical up-conversion,”
in OSA Quantum 2.0 Conference, Washington, DC, United States, 2020.
ista: 'Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. 2020. New designs
and noise channels in electro-optic microwave to optical up-conversion. OSA Quantum
2.0 Conference. OSA: Optical Society of America, OSA Technical Digest, , QTu8A.1.'
mla: Lambert, Nicholas J., et al. “New Designs and Noise Channels in Electro-Optic
Microwave to Optical up-Conversion.” OSA Quantum 2.0 Conference, QTu8A.1,
Optica Publishing Group, 2020, doi:10.1364/QUANTUM.2020.QTu8A.1.
short: N.J. Lambert, S. Mobassem, A.R. Rueda Sanchez, H.G.L. Schwefel, in:, OSA
Quantum 2.0 Conference, Optica Publishing Group, 2020.
conference:
end_date: 2020-09-17
location: Washington, DC, United States
name: 'OSA: Optical Society of America'
start_date: 2020-09-14
date_created: 2021-11-21T23:01:31Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2023-10-18T08:32:34Z
day: '01'
department:
- _id: JoFi
doi: 10.1364/QUANTUM.2020.QTu8A.1
language:
- iso: eng
month: '01'
oa_version: None
publication: OSA Quantum 2.0 Conference
publication_identifier:
isbn:
- 9-781-5575-2820-9
publication_status: published
publisher: Optica Publishing Group
quality_controlled: '1'
scopus_import: '1'
status: public
title: New designs and noise channels in electro-optic microwave to optical up-conversion
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '10556'
abstract:
- lang: eng
text: In this paper, we present the first Asynchronous Distributed Key Generation
(ADKG) algorithm which is also the first distributed key generation algorithm
that can generate cryptographic keys with a dual (f,2f+1)-threshold (where f is
the number of faulty parties). As a result, using our ADKG we remove the trusted
setup assumption that the most scalable consensus algorithms make. In order to
create a DKG with a dual (f,2f+1)- threshold we first answer in the affirmative
the open question posed by Cachin et al. [7] on how to create an Asynchronous
Verifiable Secret Sharing (AVSS) protocol with a reconstruction threshold of f+1Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications
Security. Association for Computing Machinery; 2020:1751–1767. doi:10.1145/3372297.3423364'
apa: 'Kokoris Kogias, E., Malkhi, D., & Spiegelman, A. (2020). Asynchronous
distributed key generation for computationally-secure randomness, consensus, and
threshold signatures. In Proceedings of the 2020 ACM SIGSAC Conference on Computer
and Communications Security (pp. 1751–1767). Virtual, United States: Association
for Computing Machinery. https://doi.org/10.1145/3372297.3423364'
chicago: Kokoris Kogias, Eleftherios, Dahlia Malkhi, and Alexander Spiegelman. “Asynchronous
Distributed Key Generation for Computationally-Secure Randomness, Consensus, and
Threshold Signatures.” In Proceedings of the 2020 ACM SIGSAC Conference on
Computer and Communications Security, 1751–1767. Association for Computing
Machinery, 2020. https://doi.org/10.1145/3372297.3423364.
ieee: E. Kokoris Kogias, D. Malkhi, and A. Spiegelman, “Asynchronous distributed
key generation for computationally-secure randomness, consensus, and threshold
signatures,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and
Communications Security, Virtual, United States, 2020, pp. 1751–1767.
ista: 'Kokoris Kogias E, Malkhi D, Spiegelman A. 2020. Asynchronous distributed
key generation for computationally-secure randomness, consensus, and threshold
signatures. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications
Security. CCS: Computer and Communications Security, 1751–1767.'
mla: Kokoris Kogias, Eleftherios, et al. “Asynchronous Distributed Key Generation
for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” Proceedings
of the 2020 ACM SIGSAC Conference on Computer and Communications Security,
Association for Computing Machinery, 2020, pp. 1751–1767, doi:10.1145/3372297.3423364.
short: E. Kokoris Kogias, D. Malkhi, A. Spiegelman, in:, Proceedings of the 2020
ACM SIGSAC Conference on Computer and Communications Security, Association for
Computing Machinery, 2020, pp. 1751–1767.
conference:
end_date: 2020-11-13
location: Virtual, United States
name: 'CCS: Computer and Communications Security'
start_date: 2020-11-09
date_created: 2021-12-16T13:23:27Z
date_published: 2020-10-30T00:00:00Z
date_updated: 2024-02-22T13:10:45Z
day: '30'
department:
- _id: ElKo
doi: 10.1145/3372297.3423364
external_id:
isi:
- '000768470400104'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://eprint.iacr.org/2019/1015
month: '10'
oa: 1
oa_version: Preprint
page: 1751–1767
publication: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications
Security
publication_identifier:
isbn:
- 978-1-4503-7089-9
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Asynchronous distributed key generation for computationally-secure randomness,
consensus, and threshold signatures
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '10557'
abstract:
- lang: eng
text: Data storage and retrieval systems, methods, and computer-readable media utilize
a cryptographically verifiable data structure that facilitates verification of
a transaction in a decentralized peer-to-peer environment using multi-hop backwards
and forwards links. Backward links are cryptographic hashes of past records. Forward
links are cryptographic signatures of future records that are added retroactively
to records once the target block has been appended to the data structure.
applicant:
- Ecole Polytechnique Federale de Lausanne
application_date: 2017-06-09
article_processing_charge: No
author:
- first_name: Bryan
full_name: Ford, Bryan
last_name: Ford
- first_name: Linus
full_name: Gasse, Linus
last_name: Gasse
- first_name: Eleftherios
full_name: Kokoris Kogias, Eleftherios
id: f5983044-d7ef-11ea-ac6d-fd1430a26d30
last_name: Kokoris Kogias
- first_name: Philipp
full_name: Jovanovic, Philipp
last_name: Jovanovic
citation:
ama: Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. Cryptographically verifiable
data structure having multi-hop forward and backwards links and associated systems
and methods. 2020.
apa: Ford, B., Gasse, L., Kokoris Kogias, E., & Jovanovic, P. (2020). Cryptographically
verifiable data structure having multi-hop forward and backwards links and associated
systems and methods.
chicago: Ford, Bryan, Linus Gasse, Eleftherios Kokoris Kogias, and Philipp Jovanovic.
“Cryptographically Verifiable Data Structure Having Multi-Hop Forward and Backwards
Links and Associated Systems and Methods,” 2020.
ieee: B. Ford, L. Gasse, E. Kokoris Kogias, and P. Jovanovic, “Cryptographically
verifiable data structure having multi-hop forward and backwards links and associated
systems and methods.” 2020.
ista: Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. 2020. Cryptographically verifiable
data structure having multi-hop forward and backwards links and associated systems
and methods.
mla: Ford, Bryan, et al. Cryptographically Verifiable Data Structure Having Multi-Hop
Forward and Backwards Links and Associated Systems and Methods. 2020.
short: B. Ford, L. Gasse, E. Kokoris Kogias, P. Jovanovic, (2020).
date_created: 2021-12-16T13:28:59Z
date_published: 2020-03-03T00:00:00Z
date_updated: 2021-12-21T10:04:50Z
day: '03'
department:
- _id: ElKo
extern: '1'
ipc: ' H04L9/3247 ; G06Q20/29 ; G06Q20/382 ; H04L9/3236'
ipn: '10581613'
main_file_link:
- open_access: '1'
url: https://patents.google.com/patent/US10581613B2/en
month: '03'
oa: 1
oa_version: Published Version
publication_date: 2020-03-03
related_material:
link:
- relation: earlier_version
url: https://patents.google.com/patent/US20180359096A1/en
status: public
title: Cryptographically verifiable data structure having multi-hop forward and backwards
links and associated systems and methods
type: patent
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '10672'
abstract:
- lang: eng
text: The family of feedback alignment (FA) algorithms aims to provide a more biologically
motivated alternative to backpropagation (BP), by substituting the computations
that are unrealistic to be implemented in physical brains. While FA algorithms
have been shown to work well in practice, there is a lack of rigorous theory proofing
their learning capabilities. Here we introduce the first feedback alignment algorithm
with provable learning guarantees. In contrast to existing work, we do not require
any assumption about the size or depth of the network except that it has a single
output neuron, i.e., such as for binary classification tasks. We show that our
FA algorithm can deliver its theoretical promises in practice, surpassing the
learning performance of existing FA methods and matching backpropagation in binary
classification tasks. Finally, we demonstrate the limits of our FA variant when
the number of output neurons grows beyond a certain quantity.
acknowledgement: "This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23\r\n(Wittgenstein Award).\r\n"
article_processing_charge: No
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
citation:
ama: 'Lechner M. Learning representations for binary-classification without backpropagation.
In: 8th International Conference on Learning Representations. ICLR; 2020.'
apa: 'Lechner, M. (2020). Learning representations for binary-classification without
backpropagation. In 8th International Conference on Learning Representations.
Virtual ; Addis Ababa, Ethiopia: ICLR.'
chicago: Lechner, Mathias. “Learning Representations for Binary-Classification without
Backpropagation.” In 8th International Conference on Learning Representations.
ICLR, 2020.
ieee: M. Lechner, “Learning representations for binary-classification without backpropagation,”
in 8th International Conference on Learning Representations, Virtual ;
Addis Ababa, Ethiopia, 2020.
ista: 'Lechner M. 2020. Learning representations for binary-classification without
backpropagation. 8th International Conference on Learning Representations. ICLR:
International Conference on Learning Representations.'
mla: Lechner, Mathias. “Learning Representations for Binary-Classification without
Backpropagation.” 8th International Conference on Learning Representations,
ICLR, 2020.
short: M. Lechner, in:, 8th International Conference on Learning Representations,
ICLR, 2020.
conference:
end_date: 2020-05-01
location: Virtual ; Addis Ababa, Ethiopia
name: 'ICLR: International Conference on Learning Representations'
start_date: 2020-04-26
date_created: 2022-01-25T15:50:00Z
date_published: 2020-03-11T00:00:00Z
date_updated: 2023-04-03T07:33:40Z
day: '11'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
checksum: ea13d42dd4541ddb239b6a75821fd6c9
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:35:17Z
date_updated: 2022-01-26T07:35:17Z
file_id: '10677'
file_name: iclr_2020.pdf
file_size: 249431
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:35:17Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/3.0/
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=Bke61krFvS
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 8th International Conference on Learning Representations
publication_status: published
publisher: ICLR
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning representations for binary-classification without backpropagation
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '10673'
abstract:
- lang: eng
text: We propose a neural information processing system obtained by re-purposing
the function of a biological neural circuit model to govern simulated and real-world
control tasks. Inspired by the structure of the nervous system of the soil-worm,
C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model
of biological neural circuits reparameterized for the control of alternative tasks.
We first demonstrate that ONCs realize networks with higher maximum flow compared
to arbitrary wired networks. We then learn instances of ONCs to control a series
of robotic tasks, including the autonomous parking of a real-world rover robot.
For reconfiguration of the purpose of the neural circuit, we adopt a search-based
optimization algorithm. Ordinary neural circuits perform on par and, in some cases,
significantly surpass the performance of contemporary deep learning models. ONC
networks are compact, 77% sparser than their counterpart neural controllers, and
their neural dynamics are fully interpretable at the cell-level.
acknowledgement: "RH and RG are partially supported by Horizon-2020 ECSEL Project
grant No. 783163 (iDev40), Productive 4.0, and ATBMBFW CPS-IoT Ecosystem. ML was
supported in part by the Austrian Science Fund (FWF) under grant Z211-N23\r\n(Wittgenstein
Award). AA is supported by the National Science Foundation (NSF) Graduate Research
Fellowship\r\nProgram. RH and DR are partially supported by The Boeing Company and
JP Morgan Chase. This research work is\r\npartially drawn from the PhD dissertation
of RH.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. A natural lottery ticket winner:
Reinforcement learning with ordinary neural circuits. In: Proceedings of the
37th International Conference on Machine Learning. PMLR. ; 2020:4082-4093.'
apa: 'Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2020). A natural
lottery ticket winner: Reinforcement learning with ordinary neural circuits. In
Proceedings of the 37th International Conference on Machine Learning (pp.
4082–4093). Virtual.'
chicago: 'Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu
Grosu. “A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary
Neural Circuits.” In Proceedings of the 37th International Conference on Machine
Learning, 4082–93. PMLR, 2020.'
ieee: 'R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “A natural lottery
ticket winner: Reinforcement learning with ordinary neural circuits,” in Proceedings
of the 37th International Conference on Machine Learning, Virtual, 2020, pp.
4082–4093.'
ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2020. A natural lottery ticket
winner: Reinforcement learning with ordinary neural circuits. Proceedings of the
37th International Conference on Machine Learning. ML: Machine LearningPMLR, PMLR,
, 4082–4093.'
mla: 'Hasani, Ramin, et al. “A Natural Lottery Ticket Winner: Reinforcement Learning
with Ordinary Neural Circuits.” Proceedings of the 37th International Conference
on Machine Learning, 2020, pp. 4082–93.'
short: R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the
37th International Conference on Machine Learning, 2020, pp. 4082–4093.
conference:
end_date: 2020-07-18
location: Virtual
name: 'ML: Machine Learning'
start_date: 2020-07-12
date_created: 2022-01-25T15:50:34Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2022-01-26T11:14:27Z
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
checksum: c9a4a29161777fc1a89ef451c040e3b1
content_type: application/pdf
creator: cchlebak
date_created: 2022-01-26T11:08:51Z
date_updated: 2022-01-26T11:08:51Z
file_id: '10691'
file_name: 2020_PMLR_Hasani.pdf
file_size: 2329798
relation: main_file
success: 1
file_date_updated: 2022-01-26T11:08:51Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://proceedings.mlr.press/v119/hasani20a.html
oa: 1
oa_version: Published Version
page: 4082-4093
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Proceedings of the 37th International Conference on Machine Learning
publication_identifier:
issn:
- 2640-3498
publication_status: published
quality_controlled: '1'
scopus_import: '1'
series_title: PMLR
status: public
title: 'A natural lottery ticket winner: Reinforcement learning with ordinary neural
circuits'
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '9415'
abstract:
- lang: eng
text: 'Optimizing convolutional neural networks for fast inference has recently
become an extremely active area of research. One of the go-to solutions in this
context is weight pruning, which aims to reduce computational and memory footprint
by removing large subsets of the connections in a neural network. Surprisingly,
much less attention has been given to exploiting sparsity in the activation maps,
which tend to be naturally sparse in many settings thanks to the structure of
rectified linear (ReLU) activation functions. In this paper, we present an in-depth
analysis of methods for maximizing the sparsity of the activations in a trained
neural network, and show that, when coupled with an efficient sparse-input convolution
algorithm, we can leverage this sparsity for significant performance gains. To
induce highly sparse activation maps without accuracy loss, we introduce a new
regularization technique, coupled with a new threshold-based sparsification method
based on a parameterized activation function called Forced-Activation-Threshold
Rectified Linear Unit (FATReLU). We examine the impact of our methods on popular
image classification models, showing that most architectures can adapt to significantly
sparser activation maps without any accuracy loss. Our second contribution is
showing that these these compression gains can be translated into inference speedups:
we provide a new algorithm to enable fast convolution operations over networks
with sparse activations, and show that it can enable significant speedups for
end-to-end inference on a range of popular models on the large-scale ImageNet
image classification task on modern Intel CPUs, with little or no retraining cost. '
article_processing_charge: No
author:
- first_name: Mark
full_name: Kurtz, Mark
last_name: Kurtz
- first_name: Justin
full_name: Kopinsky, Justin
last_name: Kopinsky
- first_name: Rati
full_name: Gelashvili, Rati
last_name: Gelashvili
- first_name: Alexander
full_name: Matveev, Alexander
last_name: Matveev
- first_name: John
full_name: Carr, John
last_name: Carr
- first_name: Michael
full_name: Goin, Michael
last_name: Goin
- first_name: William
full_name: Leiserson, William
last_name: Leiserson
- first_name: Sage
full_name: Moore, Sage
last_name: Moore
- first_name: Bill
full_name: Nell, Bill
last_name: Nell
- first_name: Nir
full_name: Shavit, Nir
last_name: Shavit
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation
sparsity for fast neural network inference. In: 37th International Conference
on Machine Learning, ICML 2020. Vol 119. ; 2020:5533-5543.'
apa: Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M., …
Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural
network inference. In 37th International Conference on Machine Learning, ICML
2020 (Vol. 119, pp. 5533–5543). Online.
chicago: Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John
Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation
Sparsity for Fast Neural Network Inference.” In 37th International Conference
on Machine Learning, ICML 2020, 119:5533–43, 2020.
ieee: M. Kurtz et al., “Inducing and exploiting activation sparsity for fast
neural network inference,” in 37th International Conference on Machine Learning,
ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
ista: 'Kurtz M, Kopinsky J, Gelashvili R, Matveev A, Carr J, Goin M, Leiserson W,
Moore S, Nell B, Shavit N, Alistarh D-A. 2020. Inducing and exploiting activation
sparsity for fast neural network inference. 37th International Conference on Machine
Learning, ICML 2020. ICML: International Conference on Machine Learning vol. 119,
5533–5543.'
mla: Kurtz, Mark, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural
Network Inference.” 37th International Conference on Machine Learning, ICML
2020, vol. 119, 2020, pp. 5533–43.
short: M. Kurtz, J. Kopinsky, R. Gelashvili, A. Matveev, J. Carr, M. Goin, W. Leiserson,
S. Moore, B. Nell, N. Shavit, D.-A. Alistarh, in:, 37th International Conference
on Machine Learning, ICML 2020, 2020, pp. 5533–5543.
conference:
end_date: 2020-07-18
location: Online
name: 'ICML: International Conference on Machine Learning'
start_date: 2020-07-12
date_created: 2021-05-23T22:01:45Z
date_published: 2020-07-12T00:00:00Z
date_updated: 2023-02-23T13:57:24Z
day: '12'
ddc:
- '000'
department:
- _id: DaAl
file:
- access_level: open_access
checksum: 2aaaa7d7226e49161311d91627cf783b
content_type: application/pdf
creator: kschuh
date_created: 2021-05-25T09:51:36Z
date_updated: 2021-05-25T09:51:36Z
file_id: '9421'
file_name: 2020_PMLR_Kurtz.pdf
file_size: 741899
relation: main_file
success: 1
file_date_updated: 2021-05-25T09:51:36Z
has_accepted_license: '1'
intvolume: ' 119'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 5533-5543
publication: 37th International Conference on Machine Learning, ICML 2020
publication_identifier:
issn:
- 2640-3498
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inducing and exploiting activation sparsity for fast neural network inference
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 119
year: '2020'
...
---
_id: '9526'
abstract:
- lang: eng
text: DNA methylation and histone H1 mediate transcriptional silencing of genes
and transposable elements, but how they interact is unclear. In plants and animals
with mosaic genomic methylation, functionally mysterious methylation is also common
within constitutively active housekeeping genes. Here, we show that H1 is enriched
in methylated sequences, including genes, of Arabidopsis thaliana, yet this enrichment
is independent of DNA methylation. Loss of H1 disperses heterochromatin, globally
alters nucleosome organization, and activates H1-bound genes, but only weakly
de-represses transposable elements. However, H1 loss strongly activates transposable
elements hypomethylated through mutation of DNA methyltransferase MET1. Hypomethylation
of genes also activates antisense transcription, which is modestly enhanced by
H1 loss. Our results demonstrate that H1 and DNA methylation jointly maintain
transcriptional homeostasis by silencing transposable elements and aberrant intragenic
transcripts. Such functionality plausibly explains why DNA methylation, a well-known
mutagen, has been maintained within coding sequences of crucial plant and animal
genes.
article_processing_charge: No
article_type: original
author:
- first_name: Jaemyung
full_name: Choi, Jaemyung
last_name: Choi
- first_name: David B.
full_name: Lyons, David B.
last_name: Lyons
- first_name: M. Yvonne
full_name: Kim, M. Yvonne
last_name: Kim
- first_name: Jonathan D.
full_name: Moore, Jonathan D.
last_name: Moore
- first_name: Daniel
full_name: Zilberman, Daniel
id: 6973db13-dd5f-11ea-814e-b3e5455e9ed1
last_name: Zilberman
orcid: 0000-0002-0123-8649
citation:
ama: Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. DNA methylation and histone
H1 jointly repress transposable elements and aberrant intragenic transcripts.
Molecular Cell. 2020;77(2):310-323.e7. doi:10.1016/j.molcel.2019.10.011
apa: Choi, J., Lyons, D. B., Kim, M. Y., Moore, J. D., & Zilberman, D. (2020).
DNA methylation and histone H1 jointly repress transposable elements and aberrant
intragenic transcripts. Molecular Cell. Elsevier. https://doi.org/10.1016/j.molcel.2019.10.011
chicago: Choi, Jaemyung, David B. Lyons, M. Yvonne Kim, Jonathan D. Moore, and Daniel
Zilberman. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements
and Aberrant Intragenic Transcripts.” Molecular Cell. Elsevier, 2020. https://doi.org/10.1016/j.molcel.2019.10.011.
ieee: J. Choi, D. B. Lyons, M. Y. Kim, J. D. Moore, and D. Zilberman, “DNA methylation
and histone H1 jointly repress transposable elements and aberrant intragenic transcripts,”
Molecular Cell, vol. 77, no. 2. Elsevier, p. 310–323.e7, 2020.
ista: Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. 2020. DNA methylation and
histone H1 jointly repress transposable elements and aberrant intragenic transcripts.
Molecular Cell. 77(2), 310–323.e7.
mla: Choi, Jaemyung, et al. “DNA Methylation and Histone H1 Jointly Repress Transposable
Elements and Aberrant Intragenic Transcripts.” Molecular Cell, vol. 77,
no. 2, Elsevier, 2020, p. 310–323.e7, doi:10.1016/j.molcel.2019.10.011.
short: J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77
(2020) 310–323.e7.
date_created: 2021-06-08T06:37:09Z
date_published: 2020-01-16T00:00:00Z
date_updated: 2021-12-14T07:51:15Z
day: '16'
department:
- _id: DaZi
doi: 10.1016/j.molcel.2019.10.011
extern: '1'
external_id:
pmid:
- '31732458'
intvolume: ' 77'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.molcel.2019.10.011
month: '01'
oa: 1
oa_version: Published Version
page: 310-323.e7
pmid: 1
publication: Molecular Cell
publication_identifier:
eissn:
- 1097-4164
issn:
- 1097-2765
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: DNA methylation and histone H1 jointly repress transposable elements and aberrant
intragenic transcripts
type: journal_article
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 77
year: '2020'
...
---
_id: '9630'
abstract:
- lang: eng
text: Various kinds of data are routinely represented as discrete probability distributions.
Examples include text documents summarized by histograms of word occurrences and
images represented as histograms of oriented gradients. Viewing a discrete probability
distribution as a point in the standard simplex of the appropriate dimension,
we can understand collections of such objects in geometric and topological terms. Importantly,
instead of using the standard Euclidean distance, we look into dissimilarity measures
with information-theoretic justification, and we develop the theory needed for
applying topological data analysis in this setting. In doing so, we emphasize
constructions that enable the usage of existing computational topology software
in this context.
acknowledgement: This research is partially supported by the Office of Naval Research,
through grant no. N62909-18-1-2038, and the DFG Collaborative Research Center TRR
109, ‘Discretization in Geometry and Dynamics’, through grant no. I02979-N35 of
the Austrian Science Fund (FWF).
article_processing_charge: Yes
article_type: original
author:
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
- first_name: Ziga
full_name: Virk, Ziga
id: 2E36B656-F248-11E8-B48F-1D18A9856A87
last_name: Virk
- first_name: Hubert
full_name: Wagner, Hubert
id: 379CA8B8-F248-11E8-B48F-1D18A9856A87
last_name: Wagner
citation:
ama: Edelsbrunner H, Virk Z, Wagner H. Topological data analysis in information
space. Journal of Computational Geometry. 2020;11(2):162-182. doi:10.20382/jocg.v11i2a7
apa: Edelsbrunner, H., Virk, Z., & Wagner, H. (2020). Topological data analysis
in information space. Journal of Computational Geometry. Carleton University.
https://doi.org/10.20382/jocg.v11i2a7
chicago: Edelsbrunner, Herbert, Ziga Virk, and Hubert Wagner. “Topological Data
Analysis in Information Space.” Journal of Computational Geometry. Carleton
University, 2020. https://doi.org/10.20382/jocg.v11i2a7.
ieee: H. Edelsbrunner, Z. Virk, and H. Wagner, “Topological data analysis in information
space,” Journal of Computational Geometry, vol. 11, no. 2. Carleton University,
pp. 162–182, 2020.
ista: Edelsbrunner H, Virk Z, Wagner H. 2020. Topological data analysis in information
space. Journal of Computational Geometry. 11(2), 162–182.
mla: Edelsbrunner, Herbert, et al. “Topological Data Analysis in Information Space.”
Journal of Computational Geometry, vol. 11, no. 2, Carleton University,
2020, pp. 162–82, doi:10.20382/jocg.v11i2a7.
short: H. Edelsbrunner, Z. Virk, H. Wagner, Journal of Computational Geometry 11
(2020) 162–182.
date_created: 2021-07-04T22:01:26Z
date_published: 2020-12-14T00:00:00Z
date_updated: 2021-08-11T12:26:34Z
day: '14'
ddc:
- '510'
- '000'
department:
- _id: HeEd
doi: 10.20382/jocg.v11i2a7
file:
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checksum: f02d0b2b3838e7891a6c417fc34ffdcd
content_type: application/pdf
creator: asandaue
date_created: 2021-08-11T11:55:11Z
date_updated: 2021-08-11T11:55:11Z
file_id: '9882'
file_name: 2020_JournalOfComputationalGeometry_Edelsbrunner.pdf
file_size: 1449234
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success: 1
file_date_updated: 2021-08-11T11:55:11Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '2'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/3.0/
month: '12'
oa: 1
oa_version: Published Version
page: 162-182
project:
- _id: 0aa4bc98-070f-11eb-9043-e6fff9c6a316
grant_number: I4887
name: Discretization in Geometry and Dynamics
publication: Journal of Computational Geometry
publication_identifier:
eissn:
- 1920180X
publication_status: published
publisher: Carleton University
quality_controlled: '1'
scopus_import: '1'
status: public
title: Topological data analysis in information space
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
short: CC BY (3.0)
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 11
year: '2020'
...
---
_id: '9631'
abstract:
- lang: eng
text: The ability to leverage large-scale hardware parallelism has been one of the
key enablers of the accelerated recent progress in machine learning. Consequently,
there has been considerable effort invested into developing efficient parallel
variants of classic machine learning algorithms. However, despite the wealth of
knowledge on parallelization, some classic machine learning algorithms often prove
hard to parallelize efficiently while maintaining convergence. In this paper,
we focus on efficient parallel algorithms for the key machine learning task of
inference on graphical models, in particular on the fundamental belief propagation
algorithm. We address the challenge of efficiently parallelizing this classic
paradigm by showing how to leverage scalable relaxed schedulers in this context.
We present an extensive empirical study, showing that our approach outperforms
previous parallel belief propagation implementations both in terms of scalability
and in terms of wall-clock convergence time, on a range of practical applications.
acknowledgement: "We thank Marco Mondelli for discussions related to LDPC decoding,
and Giorgi Nadiradze for discussions on analysis of relaxed schedulers. This project
has received funding from the European Research Council (ERC) under the European\r\nUnion’s
Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML)."
article_processing_charge: No
arxiv: 1
author:
- first_name: Vitaly
full_name: Aksenov, Vitaly
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: Janne
full_name: Korhonen, Janne
id: C5402D42-15BC-11E9-A202-CA2BE6697425
last_name: Korhonen
citation:
ama: 'Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed
scheduling. In: Advances in Neural Information Processing Systems. Vol
33. Curran Associates; 2020:22361-22372.'
apa: 'Aksenov, V., Alistarh, D.-A., & Korhonen, J. (2020). Scalable belief propagation
via relaxed scheduling. In Advances in Neural Information Processing Systems
(Vol. 33, pp. 22361–22372). Vancouver, Canada: Curran Associates.'
chicago: Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief
Propagation via Relaxed Scheduling.” In Advances in Neural Information Processing
Systems, 33:22361–72. Curran Associates, 2020.
ieee: V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation
via relaxed scheduling,” in Advances in Neural Information Processing Systems,
Vancouver, Canada, 2020, vol. 33, pp. 22361–22372.
ista: 'Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via
relaxed scheduling. Advances in Neural Information Processing Systems. NeurIPS:
Conference on Neural Information Processing Systems vol. 33, 22361–22372.'
mla: Aksenov, Vitaly, et al. “Scalable Belief Propagation via Relaxed Scheduling.”
Advances in Neural Information Processing Systems, vol. 33, Curran Associates,
2020, pp. 22361–72.
short: V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information
Processing Systems, Curran Associates, 2020, pp. 22361–22372.
conference:
end_date: 2020-12-12
location: Vancouver, Canada
name: 'NeurIPS: Conference on Neural Information Processing Systems'
start_date: 2020-12-06
date_created: 2021-07-04T22:01:26Z
date_published: 2020-12-06T00:00:00Z
date_updated: 2023-02-23T14:03:03Z
day: '06'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2002.11505'
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
page: 22361-22372
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Advances in Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713829546'
issn:
- '10495258'
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable belief propagation via relaxed scheduling
type: conference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 33
year: '2020'
...