---
_id: '9197'
abstract:
- lang: eng
text: In this paper we introduce and study all-pay bidding games, a class of two
player, zero-sum games on graphs. The game proceeds as follows. We place a token
on some vertex in the graph and assign budgets to the two players. Each turn,
each player submits a sealed legal bid (non-negative and below their remaining
budget), which is deducted from their budget and the highest bidder moves the
token onto an adjacent vertex. The game ends once a sink is reached, and Player
1 pays Player 2 the outcome that is associated with the sink. The players attempt
to maximize their expected outcome. Our games model settings where effort (of
no inherent value) needs to be invested in an ongoing and stateful manner. On
the negative side, we show that even in simple games on DAGs, optimal strategies
may require a distribution over bids with infinite support. A central quantity
in bidding games is the ratio of the players budgets. On the positive side, we
show a simple FPTAS for DAGs, that, for each budget ratio, outputs an approximation
for the optimal strategy for that ratio. We also implement it, show that it performs
well, and suggests interesting properties of these games. Then, given an outcome
c, we show an algorithm for finding the necessary and sufficient initial ratio
for guaranteeing outcome c with probability 1 and a strategy ensuring such. Finally,
while the general case has not previously been studied, solving the specific game
in which Player 1 wins iff he wins the first two auctions, has been long stated
as an open question, which we solve.
acknowledgement: This research was supported by the Austrian Science Fund (FWF) under
grants S11402-N23 (RiSE/SHiNE), Z211-N23 (Wittgenstein Award), and M 2369-N33 (Meitner
fellowship).
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Rasmus
full_name: Ibsen-Jensen, Rasmus
id: 3B699956-F248-11E8-B48F-1D18A9856A87
last_name: Ibsen-Jensen
orcid: 0000-0003-4783-0389
- first_name: Josef
full_name: Tkadlec, Josef
id: 3F24CCC8-F248-11E8-B48F-1D18A9856A87
last_name: Tkadlec
orcid: 0000-0002-1097-9684
citation:
ama: Avni G, Ibsen-Jensen R, Tkadlec J. All-pay bidding games on graphs. Proceedings
of the AAAI Conference on Artificial Intelligence. 2020;34(02):1798-1805.
doi:10.1609/aaai.v34i02.5546
apa: 'Avni, G., Ibsen-Jensen, R., & Tkadlec, J. (2020). All-pay bidding games
on graphs. Proceedings of the AAAI Conference on Artificial Intelligence.
New York, NY, United States: Association for the Advancement of Artificial Intelligence.
https://doi.org/10.1609/aaai.v34i02.5546'
chicago: Avni, Guy, Rasmus Ibsen-Jensen, and Josef Tkadlec. “All-Pay Bidding Games
on Graphs.” Proceedings of the AAAI Conference on Artificial Intelligence.
Association for the Advancement of Artificial Intelligence, 2020. https://doi.org/10.1609/aaai.v34i02.5546.
ieee: G. Avni, R. Ibsen-Jensen, and J. Tkadlec, “All-pay bidding games on graphs,”
Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34,
no. 02. Association for the Advancement of Artificial Intelligence, pp. 1798–1805,
2020.
ista: Avni G, Ibsen-Jensen R, Tkadlec J. 2020. All-pay bidding games on graphs.
Proceedings of the AAAI Conference on Artificial Intelligence. 34(02), 1798–1805.
mla: Avni, Guy, et al. “All-Pay Bidding Games on Graphs.” Proceedings of the
AAAI Conference on Artificial Intelligence, vol. 34, no. 02, Association for
the Advancement of Artificial Intelligence, 2020, pp. 1798–805, doi:10.1609/aaai.v34i02.5546.
short: G. Avni, R. Ibsen-Jensen, J. Tkadlec, Proceedings of the AAAI Conference
on Artificial Intelligence 34 (2020) 1798–1805.
conference:
end_date: 2020-02-12
location: New York, NY, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2020-02-07
date_created: 2021-02-25T09:05:18Z
date_published: 2020-04-03T00:00:00Z
date_updated: 2023-09-05T12:40:00Z
day: '03'
department:
- _id: ToHe
- _id: KrCh
doi: 10.1609/aaai.v34i02.5546
external_id:
arxiv:
- '1911.08360'
intvolume: ' 34'
issue: '02'
language:
- iso: eng
month: '04'
oa_version: Preprint
page: 1798-1805
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: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- '9781577358350'
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: All-pay bidding games on graphs
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 34
year: '2020'
...
---
_id: '9198'
abstract:
- lang: eng
text: "The optimization of multilayer neural networks typically leads to a solution\r\nwith
zero training error, yet the landscape can exhibit spurious local minima\r\nand
the minima can be disconnected. In this paper, we shed light on this\r\nphenomenon:
we show that the combination of stochastic gradient descent (SGD)\r\nand over-parameterization
makes the landscape of multilayer neural networks\r\napproximately connected and
thus more favorable to optimization. More\r\nspecifically, we prove that SGD solutions
are connected via a piecewise linear\r\npath, and the increase in loss along this
path vanishes as the number of\r\nneurons grows large. This result is a consequence
of the fact that the\r\nparameters found by SGD are increasingly dropout stable
as the network becomes\r\nwider. We show that, if we remove part of the neurons
(and suitably rescale the\r\nremaining ones), the change in loss is independent
of the total number of\r\nneurons, and it depends only on how many neurons are
left. Our results exhibit\r\na mild dependence on the input dimension: they are
dimension-free for two-layer\r\nnetworks and depend linearly on the dimension
for multilayer networks. We\r\nvalidate our theoretical findings with numerical
experiments for different\r\narchitectures and classification tasks."
acknowledgement: M. Mondelli was partially supported by the 2019 LopezLoreta Prize.
The authors thank Phan-Minh Nguyen for helpful discussions and the IST Distributed
Algorithms and Systems Lab for providing computational resources.
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Shevchenko, Alexander
last_name: Shevchenko
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Shevchenko A, Mondelli M. Landscape connectivity and dropout stability of
SGD solutions for over-parameterized neural networks. In: Proceedings of the
37th International Conference on Machine Learning. Vol 119. ML Research Press;
2020:8773-8784.'
apa: Shevchenko, A., & Mondelli, M. (2020). Landscape connectivity and dropout
stability of SGD solutions for over-parameterized neural networks. In Proceedings
of the 37th International Conference on Machine Learning (Vol. 119, pp. 8773–8784).
ML Research Press.
chicago: Shevchenko, Alexander, and Marco Mondelli. “Landscape Connectivity and
Dropout Stability of SGD Solutions for Over-Parameterized Neural Networks.” In
Proceedings of the 37th International Conference on Machine Learning, 119:8773–84.
ML Research Press, 2020.
ieee: A. Shevchenko and M. Mondelli, “Landscape connectivity and dropout stability
of SGD solutions for over-parameterized neural networks,” in Proceedings of
the 37th International Conference on Machine Learning, 2020, vol. 119, pp.
8773–8784.
ista: Shevchenko A, Mondelli M. 2020. Landscape connectivity and dropout stability
of SGD solutions for over-parameterized neural networks. Proceedings of the 37th
International Conference on Machine Learning. vol. 119, 8773–8784.
mla: Shevchenko, Alexander, and Marco Mondelli. “Landscape Connectivity and Dropout
Stability of SGD Solutions for Over-Parameterized Neural Networks.” Proceedings
of the 37th International Conference on Machine Learning, vol. 119, ML Research
Press, 2020, pp. 8773–84.
short: A. Shevchenko, M. Mondelli, in:, Proceedings of the 37th International Conference
on Machine Learning, ML Research Press, 2020, pp. 8773–8784.
date_created: 2021-02-25T09:36:22Z
date_published: 2020-07-13T00:00:00Z
date_updated: 2024-09-10T13:03:19Z
day: '13'
ddc:
- '000'
department:
- _id: MaMo
external_id:
arxiv:
- '1912.10095'
file:
- access_level: open_access
checksum: f042c8d4316bd87c6361aa76f1fbdbbe
content_type: application/pdf
creator: dernst
date_created: 2021-03-02T15:38:14Z
date_updated: 2021-03-02T15:38:14Z
file_id: '9217'
file_name: 2020_PMLR_Shevchenko.pdf
file_size: 5336380
relation: main_file
success: 1
file_date_updated: 2021-03-02T15:38:14Z
has_accepted_license: '1'
intvolume: ' 119'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 8773-8784
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Proceedings of the 37th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Landscape connectivity and dropout stability of SGD solutions for over-parameterized
neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 119
year: '2020'
...
---
_id: '9202'
abstract:
- lang: eng
text: We propose a novel hybridization method for stability analysis that over-approximates
nonlinear dynamical systems by switched systems with linear inclusion dynamics.
We observe that existing hybridization techniques for safety analysis that over-approximate
nonlinear dynamical systems by switched affine inclusion dynamics and provide
fixed approximation error, do not suffice for stability analysis. Hence, we propose
a hybridization method that provides a state-dependent error which converges to
zero as the state tends to the equilibrium point. The crux of our hybridization
computation is an elegant recursive algorithm that uses partial derivatives of
a given function to obtain upper and lower bound matrices for the over-approximating
linear inclusion. We illustrate our method on some examples to demonstrate the
application of the theory for stability analysis. In particular, our method is
able to establish stability of a nonlinear system which does not admit a polynomial
Lyapunov function.
acknowledgement: Miriam Garc´ıa Soto was partially supported by the Austrian Science
Fund (FWF) under grant Z211-N23 (Wittgenstein Award). Pavithra Prabhakar was partially
supported by NSF CAREER Award No. 1552668, NSF Award No. 2008957 and ONR YIP Award
No. N000141712577.
article_processing_charge: No
author:
- first_name: Miriam
full_name: Garcia Soto, Miriam
id: 4B3207F6-F248-11E8-B48F-1D18A9856A87
last_name: Garcia Soto
orcid: 0000-0003-2936-5719
- first_name: Pavithra
full_name: Prabhakar, Pavithra
last_name: Prabhakar
citation:
ama: 'Garcia Soto M, Prabhakar P. Hybridization for stability verification of nonlinear
switched systems. In: 2020 IEEE Real-Time Systems Symposium. IEEE; 2020:244-256.
doi:10.1109/RTSS49844.2020.00031'
apa: 'Garcia Soto, M., & Prabhakar, P. (2020). Hybridization for stability verification
of nonlinear switched systems. In 2020 IEEE Real-Time Systems Symposium
(pp. 244–256). Houston, TX, USA : IEEE. https://doi.org/10.1109/RTSS49844.2020.00031'
chicago: Garcia Soto, Miriam, and Pavithra Prabhakar. “Hybridization for Stability
Verification of Nonlinear Switched Systems.” In 2020 IEEE Real-Time Systems
Symposium, 244–56. IEEE, 2020. https://doi.org/10.1109/RTSS49844.2020.00031.
ieee: M. Garcia Soto and P. Prabhakar, “Hybridization for stability verification
of nonlinear switched systems,” in 2020 IEEE Real-Time Systems Symposium,
Houston, TX, USA , 2020, pp. 244–256.
ista: 'Garcia Soto M, Prabhakar P. 2020. Hybridization for stability verification
of nonlinear switched systems. 2020 IEEE Real-Time Systems Symposium. RTTS: Real-Time
Systems Symposium, 244–256.'
mla: Garcia Soto, Miriam, and Pavithra Prabhakar. “Hybridization for Stability Verification
of Nonlinear Switched Systems.” 2020 IEEE Real-Time Systems Symposium,
IEEE, 2020, pp. 244–56, doi:10.1109/RTSS49844.2020.00031.
short: M. Garcia Soto, P. Prabhakar, in:, 2020 IEEE Real-Time Systems Symposium,
IEEE, 2020, pp. 244–256.
conference:
end_date: 2020-12-04
location: 'Houston, TX, USA '
name: 'RTTS: Real-Time Systems Symposium'
start_date: 2020-12-01
date_created: 2021-02-26T16:38:24Z
date_published: 2020-12-01T00:00:00Z
date_updated: 2024-02-22T13:25:19Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1109/RTSS49844.2020.00031
external_id:
isi:
- '000680435100021'
file:
- access_level: open_access
checksum: 8f97f229316c3b3a6f0cf99297aa0941
content_type: application/pdf
creator: mgarcias
date_created: 2021-02-26T16:38:14Z
date_updated: 2021-02-26T16:38:14Z
file_id: '9203'
file_name: main.pdf
file_size: 1125794
relation: main_file
file_date_updated: 2021-02-26T16:38:14Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '12'
oa: 1
oa_version: Submitted Version
page: 244-256
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 2020 IEEE Real-Time Systems Symposium
publication_identifier:
eisbn:
- '9781728183244'
eissn:
- 2576-3172
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Hybridization for stability verification of nonlinear switched systems
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '9208'
abstract:
- lang: eng
text: 'Bending-active structures are able to efficiently produce complex curved
shapes from flat panels. The desired deformation of the panels derives from the
proper selection of their elastic properties. Optimized panels, called FlexMaps,
are designed such that, once they are bent and assembled, the resulting static
equilibrium configuration matches a desired input 3D shape. The FlexMaps elastic
properties are controlled by locally varying spiraling geometric mesostructures,
which are optimized in size and shape to match specific bending requests, namely
the global curvature of the target shape. The design pipeline starts from a quad
mesh representing the input 3D shape, which defines the edge size and the total
amount of spirals: every quad will embed one spiral. Then, an optimization algorithm
tunes the geometry of the spirals by using a simplified pre-computed rod model.
This rod model is derived from a non-linear regression algorithm which approximates
the non-linear behavior of solid FEM spiral models subject to hundreds of load
combinations. This innovative pipeline has been applied to the project of a lightweight
plywood pavilion named FlexMaps Pavilion, which is a single-layer piecewise twisted
arch that fits a bounding box of 3.90x3.96x3.25 meters. This case study serves
to test the applicability of this methodology at the architectural scale. The
structure is validated via FE analyses and the fabrication of the full scale prototype.'
acknowledgement: 'The FlexMaps Pavilion has been awarded First Prize at the “Competition
and Exhibition of innovative lightweight structures” organized by the IASS Working
Group 21 within the FORM and FORCE, joint international conference of IASS Symposium
2019 and Structural Membranes 2019 (Barcelona, 7-11 October 2019) with the following
motivation: “for its structural innovation of bending-twisting system, connection
constructability and exquisite craftmanship”[20]. The authors would like to acknowledge
the Visual Computing Lab Staff of ISTI - CNR, in particular Thomas Alderighi, Marco
Callieri, Paolo Pingi; Antonio Rizzo of IPCF - CNR; and the Administrative Staff
of ISTI - CNR. This research was partially funded by the EU H2020 Programme EVOCATION:
Advanced Visual and Geometric Computing for 3D Capture, Display, and Fabrication
(grant no. 813170).'
article_number: '1505'
article_processing_charge: No
article_type: original
author:
- first_name: Francesco
full_name: Laccone, Francesco
last_name: Laccone
- first_name: Luigi
full_name: Malomo, Luigi
last_name: Malomo
- first_name: Jesus
full_name: Perez Rodriguez, Jesus
id: 2DC83906-F248-11E8-B48F-1D18A9856A87
last_name: Perez Rodriguez
- first_name: Nico
full_name: Pietroni, Nico
last_name: Pietroni
- first_name: Federico
full_name: Ponchio, Federico
last_name: Ponchio
- first_name: Bernd
full_name: Bickel, Bernd
id: 49876194-F248-11E8-B48F-1D18A9856A87
last_name: Bickel
orcid: 0000-0001-6511-9385
- first_name: Paolo
full_name: Cignoni, Paolo
last_name: Cignoni
citation:
ama: 'Laccone F, Malomo L, Perez Rodriguez J, et al. A bending-active twisted-arch
plywood structure: Computational design and fabrication of the FlexMaps Pavilion.
SN Applied Sciences. 2020;2(9). doi:10.1007/s42452-020-03305-w'
apa: 'Laccone, F., Malomo, L., Perez Rodriguez, J., Pietroni, N., Ponchio, F., Bickel,
B., & Cignoni, P. (2020). A bending-active twisted-arch plywood structure:
Computational design and fabrication of the FlexMaps Pavilion. SN Applied Sciences.
Springer Nature. https://doi.org/10.1007/s42452-020-03305-w'
chicago: 'Laccone, Francesco, Luigi Malomo, Jesus Perez Rodriguez, Nico Pietroni,
Federico Ponchio, Bernd Bickel, and Paolo Cignoni. “A Bending-Active Twisted-Arch
Plywood Structure: Computational Design and Fabrication of the FlexMaps Pavilion.”
SN Applied Sciences. Springer Nature, 2020. https://doi.org/10.1007/s42452-020-03305-w.'
ieee: 'F. Laccone et al., “A bending-active twisted-arch plywood structure:
Computational design and fabrication of the FlexMaps Pavilion,” SN Applied
Sciences, vol. 2, no. 9. Springer Nature, 2020.'
ista: 'Laccone F, Malomo L, Perez Rodriguez J, Pietroni N, Ponchio F, Bickel B,
Cignoni P. 2020. A bending-active twisted-arch plywood structure: Computational
design and fabrication of the FlexMaps Pavilion. SN Applied Sciences. 2(9), 1505.'
mla: 'Laccone, Francesco, et al. “A Bending-Active Twisted-Arch Plywood Structure:
Computational Design and Fabrication of the FlexMaps Pavilion.” SN Applied
Sciences, vol. 2, no. 9, 1505, Springer Nature, 2020, doi:10.1007/s42452-020-03305-w.'
short: F. Laccone, L. Malomo, J. Perez Rodriguez, N. Pietroni, F. Ponchio, B. Bickel,
P. Cignoni, SN Applied Sciences 2 (2020).
date_created: 2021-02-28T23:01:25Z
date_published: 2020-09-01T00:00:00Z
date_updated: 2021-03-03T09:43:14Z
day: '01'
department:
- _id: BeBi
doi: 10.1007/s42452-020-03305-w
intvolume: ' 2'
issue: '9'
language:
- iso: eng
month: '09'
oa_version: None
publication: SN Applied Sciences
publication_identifier:
eissn:
- '25233971'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'A bending-active twisted-arch plywood structure: Computational design and
fabrication of the FlexMaps Pavilion'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2
year: '2020'
...
---
_id: '9221'
abstract:
- lang: eng
text: "Recent works have shown that gradient descent can find a global minimum for
over-parameterized neural networks where the widths of all the hidden layers scale
polynomially with N (N being the number of training samples). In this paper, we
prove that, for deep networks, a single layer of width N following the input layer
suffices to ensure a similar guarantee. In particular, all the remaining layers
are allowed to have constant widths, and form a pyramidal topology. We show an
application of our result to the widely used LeCun’s initialization and obtain
an over-parameterization requirement for the single wide layer of order N2.\r\n"
acknowledgement: The authors would like to thank Jan Maas, Mahdi Soltanolkotabi, and
Daniel Soudry for the helpful discussions, Marius Kloft, Matthias Hein and Quoc
Dinh Tran for proofreading portions of a prior version of this paper, and James
Martens for a clarification concerning LeCun’s initialization. M. Mondelli was partially
supported by the 2019 Lopez-Loreta Prize. Q. Nguyen was partially supported by the
German Research Foundation (DFG) award KL 2698/2-1.
article_processing_charge: No
arxiv: 1
author:
- first_name: Quynh
full_name: Nguyen, Quynh
last_name: Nguyen
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Nguyen Q, Mondelli M. Global convergence of deep networks with one wide layer
followed by pyramidal topology. In: 34th Conference on Neural Information Processing
Systems. Vol 33. Curran Associates; 2020:11961–11972.'
apa: 'Nguyen, Q., & Mondelli, M. (2020). Global convergence of deep networks
with one wide layer followed by pyramidal topology. In 34th Conference on Neural
Information Processing Systems (Vol. 33, pp. 11961–11972). Vancouver, Canada:
Curran Associates.'
chicago: Nguyen, Quynh, and Marco Mondelli. “Global Convergence of Deep Networks
with One Wide Layer Followed by Pyramidal Topology.” In 34th Conference on
Neural Information Processing Systems, 33:11961–11972. Curran Associates,
2020.
ieee: Q. Nguyen and M. Mondelli, “Global convergence of deep networks with one wide
layer followed by pyramidal topology,” in 34th Conference on Neural Information
Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 11961–11972.
ista: 'Nguyen Q, Mondelli M. 2020. Global convergence of deep networks with one
wide layer followed by pyramidal topology. 34th Conference on Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems vol. 33, 11961–11972.'
mla: Nguyen, Quynh, and Marco Mondelli. “Global Convergence of Deep Networks with
One Wide Layer Followed by Pyramidal Topology.” 34th Conference on Neural Information
Processing Systems, vol. 33, Curran Associates, 2020, pp. 11961–11972.
short: Q. Nguyen, M. Mondelli, in:, 34th Conference on Neural Information Processing
Systems, Curran Associates, 2020, pp. 11961–11972.
conference:
end_date: 2020-12-12
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2020-12-06
date_created: 2021-03-03T12:06:02Z
date_published: 2020-07-07T00:00:00Z
date_updated: 2024-09-10T13:03:17Z
day: '07'
department:
- _id: MaMo
external_id:
arxiv:
- '2002.07867'
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2002.07867
month: '07'
oa: 1
oa_version: Preprint
page: 11961–11972
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 34th Conference on Neural Information Processing Systems
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
status: public
title: Global convergence of deep networks with one wide layer followed by pyramidal
topology
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 33
year: '2020'
...
---
_id: '9222'
article_processing_charge: No
author:
- first_name: Georgios
full_name: Katsaros, Georgios
id: 38DB5788-F248-11E8-B48F-1D18A9856A87
last_name: Katsaros
orcid: 0000-0001-8342-202X
citation:
ama: 'Katsaros G. Transport data for: Site‐controlled uniform Ge/Si Hut wires with
electrically tunable spin–orbit coupling. 2020. doi:10.15479/AT:ISTA:9222'
apa: 'Katsaros, G. (2020). Transport data for: Site‐controlled uniform Ge/Si Hut
wires with electrically tunable spin–orbit coupling. Institute of Science and
Technology Austria. https://doi.org/10.15479/AT:ISTA:9222'
chicago: 'Katsaros, Georgios. “Transport Data for: Site‐controlled Uniform Ge/Si
Hut Wires with Electrically Tunable Spin–Orbit Coupling.” Institute of Science
and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:9222.'
ieee: 'G. Katsaros, “Transport data for: Site‐controlled uniform Ge/Si Hut wires
with electrically tunable spin–orbit coupling.” Institute of Science and Technology
Austria, 2020.'
ista: 'Katsaros G. 2020. Transport data for: Site‐controlled uniform Ge/Si Hut wires
with electrically tunable spin–orbit coupling, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:9222.'
mla: 'Katsaros, Georgios. Transport Data for: Site‐controlled Uniform Ge/Si Hut
Wires with Electrically Tunable Spin–Orbit Coupling. Institute of Science
and Technology Austria, 2020, doi:10.15479/AT:ISTA:9222.'
short: G. Katsaros, (2020).
contributor:
- contributor_type: research_group
first_name: Georgios
id: 38DB5788-F248-11E8-B48F-1D18A9856A87
last_name: Katsaros
date_created: 2021-03-05T18:00:47Z
date_published: 2020-03-16T00:00:00Z
date_updated: 2024-02-21T12:42:13Z
day: '16'
ddc:
- '530'
department:
- _id: GeKa
doi: 10.15479/AT:ISTA:9222
file:
- access_level: open_access
checksum: 41b66e195ed3dbd73077feee77b05652
content_type: application/x-zip-compressed
creator: gkatsaro
date_created: 2021-03-05T17:50:45Z
date_updated: 2021-03-05T17:50:45Z
file_id: '9223'
file_name: DOI_SiteControlledHWs.zip
file_size: 13317557
relation: main_file
- access_level: open_access
checksum: a1dc5f710ba4b3bb7f248195ba754ab2
content_type: text/plain
creator: dernst
date_created: 2021-03-10T07:31:50Z
date_updated: 2021-03-10T07:31:50Z
file_id: '9233'
file_name: Readme.txt
file_size: 3515
relation: main_file
success: 1
file_date_updated: 2021-03-10T07:31:50Z
has_accepted_license: '1'
month: '03'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7541'
relation: used_in_publication
status: public
status: public
title: 'Transport data for: Site‐controlled uniform Ge/Si Hut wires with electrically
tunable spin–orbit coupling'
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '9249'
abstract:
- lang: eng
text: Rhombic dodecahedron is a space filling polyhedron which represents the close
packing of spheres in 3D space and the Voronoi structures of the face centered
cubic (FCC) lattice. In this paper, we describe a new coordinate system where
every 3-integer coordinates grid point corresponds to a rhombic dodecahedron centroid.
In order to illustrate the interest of the new coordinate system, we propose the
characterization of 3D digital plane with its topological features, such as the
interrelation between the thickness of the digital plane and the separability
constraint we aim to obtain. We also present the characterization of 3D digital
lines and study it as the intersection of multiple digital planes. Characterization
of 3D digital sphere with relevant topological features is proposed as well along
with the 48-symmetry appearing in the new coordinate system.
acknowledgement: "This work has been partially supported by the European Research
Council (ERC) under\r\nthe European Union’s Horizon 2020 research and innovation
programme, grant no. 788183, and the DFG Collaborative Research Center TRR 109,
‘Discretization in Geometry and Dynamics’, Austrian Science Fund (FWF), grant no.
I 02979-N35. "
article_processing_charge: No
article_type: original
author:
- first_name: Ranita
full_name: Biswas, Ranita
id: 3C2B033E-F248-11E8-B48F-1D18A9856A87
last_name: Biswas
orcid: 0000-0002-5372-7890
- first_name: Gaëlle
full_name: Largeteau-Skapin, Gaëlle
last_name: Largeteau-Skapin
- first_name: Rita
full_name: Zrour, Rita
last_name: Zrour
- first_name: Eric
full_name: Andres, Eric
last_name: Andres
citation:
ama: Biswas R, Largeteau-Skapin G, Zrour R, Andres E. Digital objects in rhombic
dodecahedron grid. Mathematical Morphology - Theory and Applications. 2020;4(1):143-158.
doi:10.1515/mathm-2020-0106
apa: Biswas, R., Largeteau-Skapin, G., Zrour, R., & Andres, E. (2020). Digital
objects in rhombic dodecahedron grid. Mathematical Morphology - Theory and
Applications. De Gruyter. https://doi.org/10.1515/mathm-2020-0106
chicago: Biswas, Ranita, Gaëlle Largeteau-Skapin, Rita Zrour, and Eric Andres. “Digital
Objects in Rhombic Dodecahedron Grid.” Mathematical Morphology - Theory and
Applications. De Gruyter, 2020. https://doi.org/10.1515/mathm-2020-0106.
ieee: R. Biswas, G. Largeteau-Skapin, R. Zrour, and E. Andres, “Digital objects
in rhombic dodecahedron grid,” Mathematical Morphology - Theory and Applications,
vol. 4, no. 1. De Gruyter, pp. 143–158, 2020.
ista: Biswas R, Largeteau-Skapin G, Zrour R, Andres E. 2020. Digital objects in
rhombic dodecahedron grid. Mathematical Morphology - Theory and Applications.
4(1), 143–158.
mla: Biswas, Ranita, et al. “Digital Objects in Rhombic Dodecahedron Grid.” Mathematical
Morphology - Theory and Applications, vol. 4, no. 1, De Gruyter, 2020, pp.
143–58, doi:10.1515/mathm-2020-0106.
short: R. Biswas, G. Largeteau-Skapin, R. Zrour, E. Andres, Mathematical Morphology
- Theory and Applications 4 (2020) 143–158.
date_created: 2021-03-16T08:55:19Z
date_published: 2020-11-17T00:00:00Z
date_updated: 2021-03-22T09:01:50Z
day: '17'
ddc:
- '510'
department:
- _id: HeEd
doi: 10.1515/mathm-2020-0106
ec_funded: 1
file:
- access_level: open_access
checksum: 4a1043fa0548a725d464017fe2483ce0
content_type: application/pdf
creator: dernst
date_created: 2021-03-22T08:56:37Z
date_updated: 2021-03-22T08:56:37Z
file_id: '9272'
file_name: 2020_MathMorpholTheoryAppl_Biswas.pdf
file_size: 3668725
relation: main_file
success: 1
file_date_updated: 2021-03-22T08:56:37Z
has_accepted_license: '1'
intvolume: ' 4'
issue: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 143-158
project:
- _id: 266A2E9E-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '788183'
name: Alpha Shape Theory Extended
- _id: 2561EBF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I02979-N35
name: Persistence and stability of geometric complexes
publication: Mathematical Morphology - Theory and Applications
publication_identifier:
issn:
- 2353-3390
publication_status: published
publisher: De Gruyter
quality_controlled: '1'
status: public
title: Digital objects in rhombic dodecahedron grid
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2020'
...
---
_id: '9299'
abstract:
- lang: eng
text: We call a multigraph non-homotopic if it can be drawn in the plane in such
a way that no two edges connecting the same pair of vertices can be continuously
transformed into each other without passing through a vertex, and no loop can
be shrunk to its end-vertex in the same way. It is easy to see that a non-homotopic
multigraph on n>1 vertices can have arbitrarily many edges. We prove that the
number of crossings between the edges of a non-homotopic multigraph with n vertices
and m>4n edges is larger than cm2n for some constant c>0 , and that this
bound is tight up to a polylogarithmic factor. We also show that the lower bound
is not asymptotically sharp as n is fixed and m⟶∞ .
acknowledgement: Supported by the National Research, Development and Innovation Office,
NKFIH, KKP-133864, K-131529, K-116769, K-132696, by the Higher Educational Institutional
Excellence Program 2019 NKFIH-1158-6/2019, the Austrian Science Fund (FWF), grant
Z 342-N31, by the Ministry of Education and Science of the Russian Federation MegaGrant
No. 075-15-2019-1926, and by the ERC Synergy Grant “Dynasnet” No. 810115. A full
version can be found at https://arxiv.org/abs/2006.14908.
article_processing_charge: No
arxiv: 1
author:
- first_name: János
full_name: Pach, János
id: E62E3130-B088-11EA-B919-BF823C25FEA4
last_name: Pach
- first_name: Gábor
full_name: Tardos, Gábor
last_name: Tardos
- first_name: Géza
full_name: Tóth, Géza
last_name: Tóth
citation:
ama: 'Pach J, Tardos G, Tóth G. Crossings between non-homotopic edges. In: 28th
International Symposium on Graph Drawing and Network Visualization. Vol 12590.
LNCS. Springer Nature; 2020:359-371. doi:10.1007/978-3-030-68766-3_28'
apa: 'Pach, J., Tardos, G., & Tóth, G. (2020). Crossings between non-homotopic
edges. In 28th International Symposium on Graph Drawing and Network Visualization
(Vol. 12590, pp. 359–371). Virtual, Online: Springer Nature. https://doi.org/10.1007/978-3-030-68766-3_28'
chicago: Pach, János, Gábor Tardos, and Géza Tóth. “Crossings between Non-Homotopic
Edges.” In 28th International Symposium on Graph Drawing and Network Visualization,
12590:359–71. LNCS. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-68766-3_28.
ieee: J. Pach, G. Tardos, and G. Tóth, “Crossings between non-homotopic edges,”
in 28th International Symposium on Graph Drawing and Network Visualization,
Virtual, Online, 2020, vol. 12590, pp. 359–371.
ista: 'Pach J, Tardos G, Tóth G. 2020. Crossings between non-homotopic edges. 28th
International Symposium on Graph Drawing and Network Visualization. GD: Graph
Drawing and Network VisualizationLNCS vol. 12590, 359–371.'
mla: Pach, János, et al. “Crossings between Non-Homotopic Edges.” 28th International
Symposium on Graph Drawing and Network Visualization, vol. 12590, Springer
Nature, 2020, pp. 359–71, doi:10.1007/978-3-030-68766-3_28.
short: J. Pach, G. Tardos, G. Tóth, in:, 28th International Symposium on Graph Drawing
and Network Visualization, Springer Nature, 2020, pp. 359–371.
conference:
end_date: 2020-09-18
location: Virtual, Online
name: 'GD: Graph Drawing and Network Visualization'
start_date: 2020-09-16
date_created: 2021-03-28T22:01:44Z
date_published: 2020-09-20T00:00:00Z
date_updated: 2021-04-06T11:32:32Z
day: '20'
department:
- _id: HeEd
doi: 10.1007/978-3-030-68766-3_28
external_id:
arxiv:
- '2006.14908'
intvolume: ' 12590'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2006.14908
month: '09'
oa: 1
oa_version: Preprint
page: 359-371
project:
- _id: 268116B8-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00342
name: The Wittgenstein Prize
publication: 28th International Symposium on Graph Drawing and Network Visualization
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783030687656'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
series_title: LNCS
status: public
title: Crossings between non-homotopic edges
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 12590
year: '2020'
...
---
_id: '9308'
acknowledgement: This research was carried out with the support of the Russian Foundation
for Basic Research(grant no. 19-01-00169)
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Sergey
full_name: Avvakumov, Sergey
id: 3827DAC8-F248-11E8-B48F-1D18A9856A87
last_name: Avvakumov
- first_name: Uli
full_name: Wagner, Uli
id: 36690CA2-F248-11E8-B48F-1D18A9856A87
last_name: Wagner
orcid: 0000-0002-1494-0568
- first_name: Isaac
full_name: Mabillard, Isaac
id: 32BF9DAA-F248-11E8-B48F-1D18A9856A87
last_name: Mabillard
- first_name: A. B.
full_name: Skopenkov, A. B.
last_name: Skopenkov
citation:
ama: Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. Eliminating higher-multiplicity
intersections, III. Codimension 2. Russian Mathematical Surveys. 2020;75(6):1156-1158.
doi:10.1070/RM9943
apa: Avvakumov, S., Wagner, U., Mabillard, I., & Skopenkov, A. B. (2020). Eliminating
higher-multiplicity intersections, III. Codimension 2. Russian Mathematical
Surveys. IOP Publishing. https://doi.org/10.1070/RM9943
chicago: Avvakumov, Sergey, Uli Wagner, Isaac Mabillard, and A. B. Skopenkov. “Eliminating
Higher-Multiplicity Intersections, III. Codimension 2.” Russian Mathematical
Surveys. IOP Publishing, 2020. https://doi.org/10.1070/RM9943.
ieee: S. Avvakumov, U. Wagner, I. Mabillard, and A. B. Skopenkov, “Eliminating higher-multiplicity
intersections, III. Codimension 2,” Russian Mathematical Surveys, vol.
75, no. 6. IOP Publishing, pp. 1156–1158, 2020.
ista: Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. 2020. Eliminating higher-multiplicity
intersections, III. Codimension 2. Russian Mathematical Surveys. 75(6), 1156–1158.
mla: Avvakumov, Sergey, et al. “Eliminating Higher-Multiplicity Intersections, III.
Codimension 2.” Russian Mathematical Surveys, vol. 75, no. 6, IOP Publishing,
2020, pp. 1156–58, doi:10.1070/RM9943.
short: S. Avvakumov, U. Wagner, I. Mabillard, A.B. Skopenkov, Russian Mathematical
Surveys 75 (2020) 1156–1158.
date_created: 2021-04-04T22:01:22Z
date_published: 2020-12-01T00:00:00Z
date_updated: 2023-08-14T11:43:54Z
day: '01'
department:
- _id: UlWa
doi: 10.1070/RM9943
external_id:
arxiv:
- '1511.03501'
isi:
- '000625983100001'
intvolume: ' 75'
isi: 1
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1511.03501
month: '12'
oa: 1
oa_version: Preprint
page: 1156-1158
publication: Russian Mathematical Surveys
publication_identifier:
issn:
- 0036-0279
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
related_material:
record:
- id: '8183'
relation: earlier_version
status: public
- id: '10220'
relation: later_version
status: public
scopus_import: '1'
status: public
title: Eliminating higher-multiplicity intersections, III. Codimension 2
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 75
year: '2020'
...
---
_id: '9326'
abstract:
- lang: eng
text: The mitochondrial respiratory chain, formed by five protein complexes, utilizes
energy from catabolic processes to synthesize ATP. Complex I, the first and the
largest protein complex of the chain, harvests electrons from NADH to reduce quinone,
while pumping protons across the mitochondrial membrane. Detailed knowledge of
the working principle of such coupled charge-transfer processes remains, however,
fragmentary due to bottlenecks in understanding redox-driven conformational transitions
and their interplay with the hydrated proton pathways. Complex I from Thermus
thermophilus encases 16 subunits with nine iron–sulfur clusters, reduced by electrons
from NADH. Here, employing the latest crystal structure of T. thermophilus complex
I, we have used microsecond-scale molecular dynamics simulations to study the
chemo-mechanical coupling between redox changes of the iron–sulfur clusters and
conformational transitions across complex I. First, we identify the redox switches
within complex I, which allosterically couple the dynamics of the quinone binding
pocket to the site of NADH reduction. Second, our free-energy calculations reveal
that the affinity of the quinone, specifically menaquinone, for the binding-site
is higher than that of its reduced, menaquinol forma design essential for menaquinol
release. Remarkably, the barriers to diffusive menaquinone dynamics are lesser
than that of the more ubiquitous ubiquinone, and the naphthoquinone headgroup
of the former furnishes stronger binding interactions with the pocket, favoring
menaquinone for charge transport in T. thermophilus. Our computations are consistent
with experimentally validated mutations and hierarchize the key residues into
three functional classes, identifying new mutation targets. Third, long-range
hydrogen-bond networks connecting the quinone-binding site to the transmembrane
subunits are found to be responsible for proton pumping. Put together, the simulations
reveal the molecular design principles linking redox reactions to quinone turnover
to proton translocation in complex I.
article_processing_charge: No
author:
- first_name: Chitrak
full_name: Gupta, Chitrak
last_name: Gupta
- first_name: Umesh
full_name: Khaniya, Umesh
last_name: Khaniya
- first_name: Chun
full_name: Chan, Chun
last_name: Chan
- first_name: Francois
full_name: Dehez, Francois
last_name: Dehez
- first_name: Mrinal
full_name: Shekhar, Mrinal
last_name: Shekhar
- first_name: M. R.
full_name: Gunner, M. R.
last_name: Gunner
- first_name: Leonid A
full_name: Sazanov, Leonid A
id: 338D39FE-F248-11E8-B48F-1D18A9856A87
last_name: Sazanov
orcid: 0000-0002-0977-7989
- first_name: Christophe
full_name: Chipot, Christophe
last_name: Chipot
- first_name: Abhishek
full_name: Singharoy, Abhishek
last_name: Singharoy
citation:
ama: Gupta C, Khaniya U, Chan C, et al. Charge transfer and chemo-mechanical coupling
in respiratory complex I. 2020. doi:10.1021/jacs.9b13450.s002
apa: Gupta, C., Khaniya, U., Chan, C., Dehez, F., Shekhar, M., Gunner, M. R., …
Singharoy, A. (2020). Charge transfer and chemo-mechanical coupling in respiratory
complex I. American Chemical Society. https://doi.org/10.1021/jacs.9b13450.s002
chicago: Gupta, Chitrak, Umesh Khaniya, Chun Chan, Francois Dehez, Mrinal Shekhar,
M. R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Charge
Transfer and Chemo-Mechanical Coupling in Respiratory Complex I.” American Chemical
Society, 2020. https://doi.org/10.1021/jacs.9b13450.s002.
ieee: C. Gupta et al., “Charge transfer and chemo-mechanical coupling in
respiratory complex I.” American Chemical Society, 2020.
ista: Gupta C, Khaniya U, Chan C, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot
C, Singharoy A. 2020. Charge transfer and chemo-mechanical coupling in respiratory
complex I, American Chemical Society, 10.1021/jacs.9b13450.s002.
mla: Gupta, Chitrak, et al. Charge Transfer and Chemo-Mechanical Coupling in
Respiratory Complex I. American Chemical Society, 2020, doi:10.1021/jacs.9b13450.s002.
short: C. Gupta, U. Khaniya, C. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov,
C. Chipot, A. Singharoy, (2020).
date_created: 2021-04-14T12:05:20Z
date_published: 2020-05-20T00:00:00Z
date_updated: 2023-08-22T07:49:37Z
day: '20'
department:
- _id: LeSa
doi: 10.1021/jacs.9b13450.s002
license: https://creativecommons.org/licenses/by-nc/4.0/
main_file_link:
- open_access: '1'
month: '05'
oa: 1
oa_version: Published Version
publisher: American Chemical Society
related_material:
record:
- id: '8040'
relation: used_in_publication
status: public
status: public
title: Charge transfer and chemo-mechanical coupling in respiratory complex I
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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
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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:
- access_level: open_access
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
relation: main_file
success: 1
file_date_updated: 2021-08-11T11:55:11Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '2'
language:
- iso: eng
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'
...
---
_id: '9632'
abstract:
- lang: eng
text: "Second-order information, in the form of Hessian- or Inverse-Hessian-vector
products, is a fundamental tool for solving optimization problems. Recently, there
has been significant interest in utilizing this information in the context of
deep\r\nneural networks; however, relatively little is known about the quality
of existing approximations in this context. Our work examines this question, identifies
issues with existing approaches, and proposes a method called WoodFisher to compute
a faithful and efficient estimate of the inverse Hessian. Our main application
is to neural network compression, where we build on the classic Optimal Brain
Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms
popular state-of-the-art methods for oneshot pruning. Further, even when iterative,
gradual pruning is allowed, our method results in a gain in test accuracy over
the state-of-the-art approaches, for standard image classification datasets such
as ImageNet ILSVRC. We examine how our method can be extended to take into account
first-order information, as well as\r\nillustrate its ability to automatically
set layer-wise pruning thresholds and perform compression in the limited-data
regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher."
acknowledgement: This project has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 805223 ScaleML). Also, we would like to thank Alexander Shevchenko,
Alexandra Peste, and other members of the group for fruitful discussions.
article_processing_charge: No
arxiv: 1
author:
- first_name: Sidak Pal
full_name: Singh, Sidak Pal
id: DD138E24-D89D-11E9-9DC0-DEF6E5697425
last_name: Singh
- 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: 'Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for
neural network compression. In: Advances in Neural Information Processing Systems.
Vol 33. Curran Associates; 2020:18098-18109.'
apa: 'Singh, S. P., & Alistarh, D.-A. (2020). WoodFisher: Efficient second-order
approximation for neural network compression. In Advances in Neural Information
Processing Systems (Vol. 33, pp. 18098–18109). Vancouver, Canada: Curran Associates.'
chicago: 'Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order
Approximation for Neural Network Compression.” In Advances in Neural Information
Processing Systems, 33:18098–109. Curran Associates, 2020.'
ieee: 'S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation
for neural network compression,” in Advances in Neural Information Processing
Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.'
ista: 'Singh SP, Alistarh D-A. 2020. WoodFisher: Efficient second-order approximation
for neural network compression. Advances in Neural Information Processing Systems.
NeurIPS: Conference on Neural Information Processing Systems vol. 33, 18098–18109.'
mla: 'Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order
Approximation for Neural Network Compression.” Advances in Neural Information
Processing Systems, vol. 33, Curran Associates, 2020, pp. 18098–109.'
short: S.P. Singh, D.-A. Alistarh, in:, Advances in Neural Information Processing
Systems, Curran Associates, 2020, pp. 18098–18109.
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: 2023-02-23T14:03:06Z
day: '06'
department:
- _id: DaAl
- _id: ToHe
ec_funded: 1
external_id:
arxiv:
- '2004.14340'
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
page: 18098-18109
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: 'WoodFisher: Efficient second-order approximation for neural network compression'
type: conference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 33
year: '2020'
...
---
_id: '9633'
abstract:
- lang: eng
text: The search for biologically faithful synaptic plasticity rules has resulted
in a large body of models. They are usually inspired by – and fitted to – experimental
data, but they rarely produce neural dynamics that serve complex functions. These
failures suggest that current plasticity models are still under-constrained by
existing data. Here, we present an alternative approach that uses meta-learning
to discover plausible synaptic plasticity rules. Instead of experimental data,
the rules are constrained by the functions they implement and the structure they
are meant to produce. Briefly, we parameterize synaptic plasticity rules by a
Volterra expansion and then use supervised learning methods (gradient descent
or evolutionary strategies) to minimize a problem-dependent loss function that
quantifies how effectively a candidate plasticity rule transforms an initially
random network into one with the desired function. We first validate our approach
by re-discovering previously described plasticity rules, starting at the single-neuron
level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction
of most variability of inputs to a neuron (i.e., the first principal component).
We expand the problem to the network level and ask the framework to find Oja’s
rule together with an anti-Hebbian rule such that an initially random two-layer
firing-rate network will recover several principal components of the input space
after learning. Next, we move to networks of integrate-and-fire neurons with plastic
inhibitory afferents. We train for rules that achieve a target firing rate by
countering tuned excitation. Our algorithm discovers a specific subset of the
manifold of rules that can solve this task. Our work is a proof of principle of
an automated and unbiased approach to unveil synaptic plasticity rules that obey
biological constraints and can solve complex functions.
acknowledgement: We would like to thank Chaitanya Chintaluri, Georgia Christodoulou,
Bill Podlaski and Merima Šabanovic for useful discussions and comments. This work
was supported by a Wellcome Trust ´ Senior Research Fellowship (214316/Z/18/Z),
a BBSRC grant (BB/N019512/1), an ERC consolidator Grant (SYNAPSEEK), a Leverhulme
Trust Project Grant (RPG-2016-446), and funding from École Polytechnique, Paris.
article_processing_charge: No
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
- first_name: Timothy
full_name: Lillicrap, Timothy
last_name: Lillicrap
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: 'Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. A meta-learning
approach to (re)discover plasticity rules that carve a desired function into a
neural network. In: Advances in Neural Information Processing Systems.
Vol 33. ; 2020:16398-16408.'
apa: Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., & Vogels, T.
P. (2020). A meta-learning approach to (re)discover plasticity rules that carve
a desired function into a neural network. In Advances in Neural Information
Processing Systems (Vol. 33, pp. 16398–16408). Vancouver, Canada.
chicago: Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap,
and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That
Carve a Desired Function into a Neural Network.” In Advances in Neural Information
Processing Systems, 33:16398–408, 2020.
ieee: B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A
meta-learning approach to (re)discover plasticity rules that carve a desired function
into a neural network,” in Advances in Neural Information Processing Systems,
Vancouver, Canada, 2020, vol. 33, pp. 16398–16408.
ista: 'Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning
approach to (re)discover plasticity rules that carve a desired function into a
neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference
on Neural Information Processing Systems vol. 33, 16398–16408.'
mla: Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity
Rules That Carve a Desired Function into a Neural Network.” Advances in Neural
Information Processing Systems, vol. 33, 2020, pp. 16398–408.
short: B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances
in Neural Information Processing Systems, 2020, pp. 16398–16408.
conference:
end_date: 2020-12-12
location: Vancouver, Canada
name: 'NeurIPS: Conference on Neural Information Processing Systems'
start_date: 2020-12-06
date_created: 2021-07-04T22:01:27Z
date_published: 2020-12-06T00:00:00Z
date_updated: 2023-10-18T09:20:55Z
day: '06'
department:
- _id: TiVo
ec_funded: 1
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
page: 16398-16408
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Advances in Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
quality_controlled: '1'
related_material:
link:
- relation: is_continued_by
url: https://doi.org/10.1101/2020.10.24.353409
record:
- id: '14422'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: A meta-learning approach to (re)discover plasticity rules that carve a desired
function into a neural network
type: conference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 33
year: '2020'
...
---
_id: '9706'
abstract:
- lang: eng
text: 'Additional file 2: Supplementary Tables. The association of pre-adjusted
protein levels with biological and technical covariates. Protein levels were adjusted
for age, sex, array plate and four genetic principal components (population structure)
prior to analyses. Significant associations are emboldened. (Table S1). pQTLs
associated with inflammatory biomarker levels from Bayesian penalised regression
model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated
with inflammatory biomarker levels from ordinary least squares regression model
(P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary
least squares GWAS and EWAS performed on inflammatory protein levels (n = 70)
in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs
associated with inflammatory biomarker levels from ordinary least squares regression
model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary
least squares and Bayesian penalised regression models for concordantly identified
SNPs. (Table S6). Estimate of heritability for blood protein levels as well as
proportion of variance explained attributable to different prior mixtures. (Table
S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood)
and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant
SNPs identified by linear model and Bayesian penalised regression and whether
they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation
for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression
are putatively associated with circulating inflammatory proteins that harbour
pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified
by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table
S12). CpGs associated with inflammatory protein biomarkers as identified by linear
model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory
protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10.
(Table S14). Estimate of variance explained for blood protein levels by DNA methylation
as well as proportion of explained attributable to different prior mixtures -
BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide
DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression
model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker
levels explained by common genetic and methylation data (joint and conditional
estimates from BayesR+). Ordered by combined variance explained by genetic and
epigenetic data - smallest to largest. Significant results from t-tests comparing
distributions for variance explained by methylation or genetics alone versus combined
estimate are emboldened. (Table S17). Genetic and epigenetic factors identified
by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian
Randomisation analyses to assess whether proteins with concordantly identified
genetic signals are causally associated with Alzheimer’s disease risk. (Table
S19).'
article_processing_charge: No
author:
- first_name: Robert F.
full_name: Hillary, Robert F.
last_name: Hillary
- first_name: Daniel
full_name: Trejo-Banos, Daniel
last_name: Trejo-Banos
- first_name: Athanasios
full_name: Kousathanas, Athanasios
last_name: Kousathanas
- first_name: Daniel L.
full_name: McCartney, Daniel L.
last_name: McCartney
- first_name: Sarah E.
full_name: Harris, Sarah E.
last_name: Harris
- first_name: Anna J.
full_name: Stevenson, Anna J.
last_name: Stevenson
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Sven Erik
full_name: Ojavee, Sven Erik
last_name: Ojavee
- first_name: Qian
full_name: Zhang, Qian
last_name: Zhang
- first_name: David C.
full_name: Liewald, David C.
last_name: Liewald
- first_name: Craig W.
full_name: Ritchie, Craig W.
last_name: Ritchie
- first_name: Kathryn L.
full_name: Evans, Kathryn L.
last_name: Evans
- first_name: Elliot M.
full_name: Tucker-Drob, Elliot M.
last_name: Tucker-Drob
- first_name: Naomi R.
full_name: Wray, Naomi R.
last_name: Wray
- first_name: 'Allan F. '
full_name: 'McRae, Allan F. '
last_name: McRae
- first_name: Peter M.
full_name: Visscher, Peter M.
last_name: Visscher
- first_name: Ian J.
full_name: Deary, Ian J.
last_name: Deary
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
- first_name: 'Riccardo E. '
full_name: 'Marioni, Riccardo E. '
last_name: Marioni
citation:
ama: Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method
genome- and epigenome-wide studies of inflammatory protein levels in healthy older
adults. 2020. doi:10.6084/m9.figshare.12629697.v1
apa: Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris,
S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method
genome- and epigenome-wide studies of inflammatory protein levels in healthy older
adults. Springer Nature. https://doi.org/10.6084/m9.figshare.12629697.v1
chicago: Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel
L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional
File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein
Levels in Healthy Older Adults.” Springer Nature, 2020. https://doi.org/10.6084/m9.figshare.12629697.v1.
ieee: R. F. Hillary et al., “Additional file 2 of multi-method genome- and
epigenome-wide studies of inflammatory protein levels in healthy older adults.”
Springer Nature, 2020.
ista: Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson
AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob
EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional
file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein
levels in healthy older adults, Springer Nature, 10.6084/m9.figshare.12629697.v1.
mla: Hillary, Robert F., et al. Additional File 2 of Multi-Method Genome- and
Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.
Springer Nature, 2020, doi:10.6084/m9.figshare.12629697.v1.
short: R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. McCartney, S.E. Harris,
A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie,
K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. McRae, P.M. Visscher, I.J. Deary,
M.R. Robinson, R.E. Marioni, (2020).
date_created: 2021-07-23T08:59:15Z
date_published: 2020-07-09T00:00:00Z
date_updated: 2023-08-22T07:55:36Z
day: '09'
department:
- _id: MaRo
doi: 10.6084/m9.figshare.12629697.v1
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.6084/m9.figshare.12629697.v1
month: '07'
oa: 1
oa_version: Published Version
other_data_license: CC0 + CC BY (4.0)
publisher: Springer Nature
related_material:
record:
- id: '8133'
relation: used_in_publication
status: public
status: public
title: Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory
protein levels in healthy older adults
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: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9708'
abstract:
- lang: eng
text: This research data supports 'Hard antinodal gap revealed by quantum oscillations
in the pseudogap regime of underdoped high-Tc superconductors'. A Readme file
for plotting each figure is provided.
article_processing_charge: No
author:
- first_name: Mate
full_name: Hartstein, Mate
last_name: Hartstein
- first_name: Yu-Te
full_name: Hsu, Yu-Te
last_name: Hsu
- first_name: Kimberly A
full_name: Modic, Kimberly A
id: 13C26AC0-EB69-11E9-87C6-5F3BE6697425
last_name: Modic
orcid: 0000-0001-9760-3147
- first_name: Juan
full_name: Porras, Juan
last_name: Porras
- first_name: Toshinao
full_name: Loew, Toshinao
last_name: Loew
- first_name: Matthieu
full_name: Le Tacon, Matthieu
last_name: Le Tacon
- first_name: Huakun
full_name: Zuo, Huakun
last_name: Zuo
- first_name: Jinhua
full_name: Wang, Jinhua
last_name: Wang
- first_name: Zengwei
full_name: Zhu, Zengwei
last_name: Zhu
- first_name: Mun
full_name: Chan, Mun
last_name: Chan
- first_name: Ross
full_name: McDonald, Ross
last_name: McDonald
- first_name: Gilbert
full_name: Lonzarich, Gilbert
last_name: Lonzarich
- first_name: Bernhard
full_name: Keimer, Bernhard
last_name: Keimer
- first_name: Suchitra
full_name: Sebastian, Suchitra
last_name: Sebastian
- first_name: Neil
full_name: Harrison, Neil
last_name: Harrison
citation:
ama: Hartstein M, Hsu Y-T, Modic KA, et al. Accompanying dataset for “Hard antinodal
gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc
superconductors.” 2020. doi:10.17863/cam.50169
apa: Hartstein, M., Hsu, Y.-T., Modic, K. A., Porras, J., Loew, T., Le Tacon, M.,
… Harrison, N. (2020). Accompanying dataset for “Hard antinodal gap revealed by
quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.”
Apollo - University of Cambridge. https://doi.org/10.17863/cam.50169
chicago: Hartstein, Mate, Yu-Te Hsu, Kimberly A Modic, Juan Porras, Toshinao Loew,
Matthieu Le Tacon, Huakun Zuo, et al. “Accompanying Dataset for ‘Hard Antinodal
Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc
Superconductors.’” Apollo - University of Cambridge, 2020. https://doi.org/10.17863/cam.50169.
ieee: M. Hartstein et al., “Accompanying dataset for ‘Hard antinodal gap
revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc
superconductors.’” Apollo - University of Cambridge, 2020.
ista: Hartstein M, Hsu Y-T, Modic KA, Porras J, Loew T, Le Tacon M, Zuo H, Wang
J, Zhu Z, Chan M, McDonald R, Lonzarich G, Keimer B, Sebastian S, Harrison N.
2020. Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations
in the pseudogap regime of underdoped high-Tc superconductors’, Apollo - University
of Cambridge, 10.17863/cam.50169.
mla: Hartstein, Mate, et al. Accompanying Dataset for “Hard Antinodal Gap Revealed
by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.”
Apollo - University of Cambridge, 2020, doi:10.17863/cam.50169.
short: M. Hartstein, Y.-T. Hsu, K.A. Modic, J. Porras, T. Loew, M. Le Tacon, H.
Zuo, J. Wang, Z. Zhu, M. Chan, R. McDonald, G. Lonzarich, B. Keimer, S. Sebastian,
N. Harrison, (2020).
date_created: 2021-07-23T10:00:35Z
date_published: 2020-05-29T00:00:00Z
date_updated: 2023-08-21T07:06:48Z
day: '29'
department:
- _id: KiMo
doi: 10.17863/cam.50169
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.17863/CAM.50169
month: '05'
oa: 1
oa_version: Published Version
publisher: Apollo - University of Cambridge
related_material:
record:
- id: '7942'
relation: used_in_publication
status: public
status: public
title: Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations
in the pseudogap regime of underdoped high-Tc superconductors'
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: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9713'
abstract:
- lang: eng
text: Additional analyses of the trajectories
article_processing_charge: No
author:
- first_name: Chitrak
full_name: Gupta, Chitrak
last_name: Gupta
- first_name: Umesh
full_name: Khaniya, Umesh
last_name: Khaniya
- first_name: Chun Kit
full_name: Chan, Chun Kit
last_name: Chan
- first_name: Francois
full_name: Dehez, Francois
last_name: Dehez
- first_name: Mrinal
full_name: Shekhar, Mrinal
last_name: Shekhar
- first_name: M.R.
full_name: Gunner, M.R.
last_name: Gunner
- first_name: Leonid A
full_name: Sazanov, Leonid A
id: 338D39FE-F248-11E8-B48F-1D18A9856A87
last_name: Sazanov
orcid: 0000-0002-0977-7989
- first_name: Christophe
full_name: Chipot, Christophe
last_name: Chipot
- first_name: Abhishek
full_name: Singharoy, Abhishek
last_name: Singharoy
citation:
ama: Gupta C, Khaniya U, Chan CK, et al. Supporting information. 2020. doi:10.1021/jacs.9b13450.s001
apa: Gupta, C., Khaniya, U., Chan, C. K., Dehez, F., Shekhar, M., Gunner, M. R.,
… Singharoy, A. (2020). Supporting information. American Chemical Society . https://doi.org/10.1021/jacs.9b13450.s001
chicago: Gupta, Chitrak, Umesh Khaniya, Chun Kit Chan, Francois Dehez, Mrinal Shekhar,
M.R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Supporting
Information.” American Chemical Society , 2020. https://doi.org/10.1021/jacs.9b13450.s001.
ieee: C. Gupta et al., “Supporting information.” American Chemical Society
, 2020.
ista: Gupta C, Khaniya U, Chan CK, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot
C, Singharoy A. 2020. Supporting information, American Chemical Society , 10.1021/jacs.9b13450.s001.
mla: Gupta, Chitrak, et al. Supporting Information. American Chemical Society
, 2020, doi:10.1021/jacs.9b13450.s001.
short: C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A.
Sazanov, C. Chipot, A. Singharoy, (2020).
date_created: 2021-07-23T12:02:39Z
date_published: 2020-05-20T00:00:00Z
date_updated: 2023-08-22T07:49:38Z
day: '20'
department:
- _id: LeSa
doi: 10.1021/jacs.9b13450.s001
month: '05'
oa_version: Published Version
publisher: 'American Chemical Society '
related_material:
record:
- id: '8040'
relation: used_in_publication
status: public
status: public
title: Supporting information
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9750'
abstract:
- lang: eng
text: Tension of the actomyosin cell cortex plays a key role in determining cell-cell
contact growth and size. The level of cortical tension outside of the cell-cell
contact, when pulling at the contact edge, scales with the total size to which
a cell-cell contact can grow1,2. Here we show in zebrafish primary germ layer
progenitor cells that this monotonic relationship only applies to a narrow range
of cortical tension increase, and that above a critical threshold, contact size
inversely scales with cortical tension. This switch from cortical tension increasing
to decreasing progenitor cell-cell contact size is caused by cortical tension
promoting E-cadherin anchoring to the actomyosin cytoskeleton, thereby increasing
clustering and stability of E-cadherin at the contact. Once tension-mediated E-cadherin
stabilization at the contact exceeds a critical threshold level, the rate by which
the contact expands in response to pulling forces from the cortex sharply drops,
leading to smaller contacts at physiologically relevant timescales of contact
formation. Thus, the activity of cortical tension in expanding cell-cell contact
size is limited by tension stabilizing E-cadherin-actin complexes at the contact.
acknowledged_ssus:
- _id: Bio
- _id: EM-Fac
- _id: SSU
acknowledgement: We would like to thank Edouard Hannezo for discussions, Shayan Shami
Pour and Daniel Capek for help with data analysis, Vanessa Barone and other members
of the Heisenberg laboratory for thoughtful discussions and comments on the manuscript.
We also thank Jack Merrin for preparing the microwells, and the Scientific Service
Units at IST Austria, specifically Bioimaging and Electron Microscopy, and the Zebrafish
Facility for continuous support. We acknowledge Hitoshi Morita for the kind gift
of VinculinB-GFP plasmid. This research was supported by an ERC Advanced Grant (MECSPEC)
to C.-P.H, EMBO Long Term grant (ALTF 187-2013) to M.S and IST Fellow Marie-Curie
COFUND No. P_IST_EU01 to J.S.
article_processing_charge: No
author:
- first_name: Jana
full_name: Slovakova, Jana
id: 30F3F2F0-F248-11E8-B48F-1D18A9856A87
last_name: Slovakova
- first_name: Mateusz K
full_name: Sikora, Mateusz K
id: 2F74BCDE-F248-11E8-B48F-1D18A9856A87
last_name: Sikora
- first_name: Silvia
full_name: Caballero Mancebo, Silvia
id: 2F1E1758-F248-11E8-B48F-1D18A9856A87
last_name: Caballero Mancebo
orcid: 0000-0002-5223-3346
- first_name: Gabriel
full_name: Krens, Gabriel
id: 2B819732-F248-11E8-B48F-1D18A9856A87
last_name: Krens
orcid: 0000-0003-4761-5996
- first_name: Walter
full_name: Kaufmann, Walter
id: 3F99E422-F248-11E8-B48F-1D18A9856A87
last_name: Kaufmann
orcid: 0000-0001-9735-5315
- first_name: Karla
full_name: Huljev, Karla
id: 44C6F6A6-F248-11E8-B48F-1D18A9856A87
last_name: Huljev
- first_name: Carl-Philipp J
full_name: Heisenberg, Carl-Philipp J
id: 39427864-F248-11E8-B48F-1D18A9856A87
last_name: Heisenberg
orcid: 0000-0002-0912-4566
citation:
ama: Slovakova J, Sikora MK, Caballero Mancebo S, et al. Tension-dependent stabilization
of E-cadherin limits cell-cell contact expansion. bioRxiv. 2020. doi:10.1101/2020.11.20.391284
apa: Slovakova, J., Sikora, M. K., Caballero Mancebo, S., Krens, G., Kaufmann, W.,
Huljev, K., & Heisenberg, C.-P. J. (2020). Tension-dependent stabilization
of E-cadherin limits cell-cell contact expansion. bioRxiv. Cold Spring
Harbor Laboratory. https://doi.org/10.1101/2020.11.20.391284
chicago: Slovakova, Jana, Mateusz K Sikora, Silvia Caballero Mancebo, Gabriel Krens,
Walter Kaufmann, Karla Huljev, and Carl-Philipp J Heisenberg. “Tension-Dependent
Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” BioRxiv.
Cold Spring Harbor Laboratory, 2020. https://doi.org/10.1101/2020.11.20.391284.
ieee: J. Slovakova et al., “Tension-dependent stabilization of E-cadherin
limits cell-cell contact expansion,” bioRxiv. Cold Spring Harbor Laboratory,
2020.
ista: Slovakova J, Sikora MK, Caballero Mancebo S, Krens G, Kaufmann W, Huljev K,
Heisenberg C-PJ. 2020. Tension-dependent stabilization of E-cadherin limits cell-cell
contact expansion. bioRxiv, 10.1101/2020.11.20.391284.
mla: Slovakova, Jana, et al. “Tension-Dependent Stabilization of E-Cadherin Limits
Cell-Cell Contact Expansion.” BioRxiv, Cold Spring Harbor Laboratory, 2020,
doi:10.1101/2020.11.20.391284.
short: J. Slovakova, M.K. Sikora, S. Caballero Mancebo, G. Krens, W. Kaufmann, K.
Huljev, C.-P.J. Heisenberg, BioRxiv (2020).
date_created: 2021-07-29T11:29:50Z
date_published: 2020-11-20T00:00:00Z
date_updated: 2024-03-25T23:30:10Z
day: '20'
department:
- _id: CaHe
- _id: EM-Fac
- _id: Bio
doi: 10.1101/2020.11.20.391284
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2020.11.20.391284
month: '11'
oa: 1
oa_version: Preprint
page: '41'
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
- _id: 260F1432-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '742573'
name: Interaction and feedback between cell mechanics and fate specification in
vertebrate gastrulation
- _id: 2521E28E-B435-11E9-9278-68D0E5697425
grant_number: 187-2013
name: Modulation of adhesion function in cell-cell contact formation by cortical
tension
publication: bioRxiv
publication_status: published
publisher: Cold Spring Harbor Laboratory
related_material:
record:
- id: '10766'
relation: later_version
status: public
- id: '9623'
relation: dissertation_contains
status: public
status: public
title: Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...