---
_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: '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
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'
...
---
_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: '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: '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'
...
---
_id: '9776'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Supporting information. 2020. doi:10.1371/journal.pcbi.1007642.s001
apa: Grah, R., & Friedlander, T. (2020). Supporting information. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1007642.s001
chicago: Grah, Rok, and Tamar Friedlander. “Supporting Information.” Public Library
of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s001.
ieee: R. Grah and T. Friedlander, “Supporting information.” Public Library of Science,
2020.
ista: Grah R, Friedlander T. 2020. Supporting information, Public Library of Science,
10.1371/journal.pcbi.1007642.s001.
mla: Grah, Rok, and Tamar Friedlander. Supporting Information. Public Library
of Science, 2020, doi:10.1371/journal.pcbi.1007642.s001.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:15:04Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-08-18T06:47:47Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s001
month: '02'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '7569'
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: '9777'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Maximizing crosstalk. 2020. doi:10.1371/journal.pcbi.1007642.s002
apa: Grah, R., & Friedlander, T. (2020). Maximizing crosstalk. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1007642.s002
chicago: Grah, Rok, and Tamar Friedlander. “Maximizing Crosstalk.” Public Library
of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s002.
ieee: R. Grah and T. Friedlander, “Maximizing crosstalk.” Public Library of Science,
2020.
ista: Grah R, Friedlander T. 2020. Maximizing crosstalk, Public Library of Science,
10.1371/journal.pcbi.1007642.s002.
mla: Grah, Rok, and Tamar Friedlander. Maximizing Crosstalk. Public Library
of Science, 2020, doi:10.1371/journal.pcbi.1007642.s002.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:21:51Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-09-12T11:02:25Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s002
main_file_link:
- open_access: '1'
url: https://doi.org/10.1371/journal.pcbi.1007642.s002
month: '02'
oa: 1
oa_version: None
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: used_in_publication
status: public
status: public
title: Maximizing crosstalk
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '9779'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Distribution of crosstalk values. 2020. doi:10.1371/journal.pcbi.1007642.s003
apa: Grah, R., & Friedlander, T. (2020). Distribution of crosstalk values. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1007642.s003
chicago: Grah, Rok, and Tamar Friedlander. “Distribution of Crosstalk Values.” Public
Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s003.
ieee: R. Grah and T. Friedlander, “Distribution of crosstalk values.” Public Library
of Science, 2020.
ista: Grah R, Friedlander T. 2020. Distribution of crosstalk values, Public Library
of Science, 10.1371/journal.pcbi.1007642.s003.
mla: Grah, Rok, and Tamar Friedlander. Distribution of Crosstalk Values.
Public Library of Science, 2020, doi:10.1371/journal.pcbi.1007642.s003.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:24:37Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-08-18T06:47:47Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s003
month: '02'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: research_data
status: public
status: public
title: Distribution of crosstalk values
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9780'
abstract:
- lang: eng
text: "PADREV : 4,4'-dimethoxy[1,1'-biphenyl]-2,2',5,5'-tetrol\r\nSpace Group: C
2 (5), Cell: a 24.488(16)Å b 5.981(4)Å c 3.911(3)Å, α 90° β 91.47(3)° γ 90°"
article_processing_charge: No
author:
- first_name: Werner
full_name: Schlemmer, Werner
last_name: Schlemmer
- first_name: Philipp
full_name: Nothdurft, Philipp
last_name: Nothdurft
- first_name: Alina
full_name: Petzold, Alina
last_name: Petzold
- first_name: Gisbert
full_name: Riess, Gisbert
last_name: Riess
- first_name: Philipp
full_name: Frühwirt, Philipp
last_name: Frühwirt
- first_name: Max
full_name: Schmallegger, Max
last_name: Schmallegger
- first_name: Georg
full_name: Gescheidt-Demner, Georg
last_name: Gescheidt-Demner
- first_name: Roland
full_name: Fischer, Roland
last_name: Fischer
- first_name: Stefan Alexander
full_name: Freunberger, Stefan Alexander
id: A8CA28E6-CE23-11E9-AD2D-EC27E6697425
last_name: Freunberger
orcid: 0000-0003-2902-5319
- first_name: Wolfgang
full_name: Kern, Wolfgang
last_name: Kern
- first_name: Stefan
full_name: Spirk, Stefan
last_name: Spirk
citation:
ama: 'Schlemmer W, Nothdurft P, Petzold A, et al. CCDC 1991959: Experimental Crystal
Structure Determination. 2020. doi:10.5517/ccdc.csd.cc24vsrk'
apa: 'Schlemmer, W., Nothdurft, P., Petzold, A., Riess, G., Frühwirt, P., Schmallegger,
M., … Spirk, S. (2020). CCDC 1991959: Experimental Crystal Structure Determination.
CCDC. https://doi.org/10.5517/ccdc.csd.cc24vsrk'
chicago: 'Schlemmer, Werner, Philipp Nothdurft, Alina Petzold, Gisbert Riess, Philipp
Frühwirt, Max Schmallegger, Georg Gescheidt-Demner, et al. “CCDC 1991959: Experimental
Crystal Structure Determination.” CCDC, 2020. https://doi.org/10.5517/ccdc.csd.cc24vsrk.'
ieee: 'W. Schlemmer et al., “CCDC 1991959: Experimental Crystal Structure
Determination.” CCDC, 2020.'
ista: 'Schlemmer W, Nothdurft P, Petzold A, Riess G, Frühwirt P, Schmallegger M,
Gescheidt-Demner G, Fischer R, Freunberger SA, Kern W, Spirk S. 2020. CCDC 1991959:
Experimental Crystal Structure Determination, CCDC, 10.5517/ccdc.csd.cc24vsrk.'
mla: 'Schlemmer, Werner, et al. CCDC 1991959: Experimental Crystal Structure
Determination. CCDC, 2020, doi:10.5517/ccdc.csd.cc24vsrk.'
short: W. Schlemmer, P. Nothdurft, A. Petzold, G. Riess, P. Frühwirt, M. Schmallegger,
G. Gescheidt-Demner, R. Fischer, S.A. Freunberger, W. Kern, S. Spirk, (2020).
date_created: 2021-08-06T07:41:07Z
date_published: 2020-03-22T00:00:00Z
date_updated: 2023-09-05T16:03:47Z
day: '22'
department:
- _id: StFr
doi: 10.5517/ccdc.csd.cc24vsrk
main_file_link:
- open_access: '1'
url: https://dx.doi.org/10.5517/ccdc.csd.cc24vsrk
month: '03'
oa: 1
oa_version: Published Version
publisher: CCDC
related_material:
record:
- id: '8329'
relation: used_in_publication
status: public
status: public
title: 'CCDC 1991959: Experimental Crystal Structure Determination'
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9781'
abstract:
- lang: eng
text: We consider the Pekar functional on a ball in ℝ3. We prove uniqueness of minimizers,
and a quadratic lower bound in terms of the distance to the minimizer. The latter
follows from nondegeneracy of the Hessian at the minimum.
acknowledgement: We are grateful for the hospitality at the Mittag-Leffler Institute,
where part of this work has been done. The work of the authors was supported by
the European Research Council (ERC)under the European Union's Horizon 2020 research
and innovation programme grant 694227.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Dario
full_name: Feliciangeli, Dario
id: 41A639AA-F248-11E8-B48F-1D18A9856A87
last_name: Feliciangeli
orcid: 0000-0003-0754-8530
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
citation:
ama: Feliciangeli D, Seiringer R. Uniqueness and nondegeneracy of minimizers of
the Pekar functional on a ball. SIAM Journal on Mathematical Analysis.
2020;52(1):605-622. doi:10.1137/19m126284x
apa: Feliciangeli, D., & Seiringer, R. (2020). Uniqueness and nondegeneracy
of minimizers of the Pekar functional on a ball. SIAM Journal on Mathematical
Analysis. Society for Industrial & Applied Mathematics . https://doi.org/10.1137/19m126284x
chicago: Feliciangeli, Dario, and Robert Seiringer. “Uniqueness and Nondegeneracy
of Minimizers of the Pekar Functional on a Ball.” SIAM Journal on Mathematical
Analysis. Society for Industrial & Applied Mathematics , 2020. https://doi.org/10.1137/19m126284x.
ieee: D. Feliciangeli and R. Seiringer, “Uniqueness and nondegeneracy of minimizers
of the Pekar functional on a ball,” SIAM Journal on Mathematical Analysis,
vol. 52, no. 1. Society for Industrial & Applied Mathematics , pp. 605–622,
2020.
ista: Feliciangeli D, Seiringer R. 2020. Uniqueness and nondegeneracy of minimizers
of the Pekar functional on a ball. SIAM Journal on Mathematical Analysis. 52(1),
605–622.
mla: Feliciangeli, Dario, and Robert Seiringer. “Uniqueness and Nondegeneracy of
Minimizers of the Pekar Functional on a Ball.” SIAM Journal on Mathematical
Analysis, vol. 52, no. 1, Society for Industrial & Applied Mathematics
, 2020, pp. 605–22, doi:10.1137/19m126284x.
short: D. Feliciangeli, R. Seiringer, SIAM Journal on Mathematical Analysis 52 (2020)
605–622.
date_created: 2021-08-06T07:34:16Z
date_published: 2020-02-12T00:00:00Z
date_updated: 2023-09-07T13:30:11Z
day: '12'
ddc:
- '510'
department:
- _id: RoSe
doi: 10.1137/19m126284x
ec_funded: 1
external_id:
arxiv:
- '1904.08647 '
isi:
- '000546967700022'
has_accepted_license: '1'
intvolume: ' 52'
isi: 1
issue: '1'
keyword:
- Applied Mathematics
- Computational Mathematics
- Analysis
language:
- iso: eng
main_file_link:
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url: https://arxiv.org/abs/1904.08647
month: '02'
oa: 1
oa_version: Preprint
page: 605-622
project:
- _id: 25C6DC12-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '694227'
name: Analysis of quantum many-body systems
publication: SIAM Journal on Mathematical Analysis
publication_identifier:
eissn:
- 1095-7154
issn:
- 0036-1410
publication_status: published
publisher: 'Society for Industrial & Applied Mathematics '
quality_controlled: '1'
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relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
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volume: 52
year: '2020'
...
---
_id: '9798'
abstract:
- lang: eng
text: Fitness interactions between mutations can influence a population’s evolution
in many different ways. While epistatic effects are difficult to measure precisely,
important information is captured by the mean and variance of log fitnesses for
individuals carrying different numbers of mutations. We derive predictions for
these quantities from a class of simple fitness landscapes, based on models of
optimizing selection on quantitative traits. We also explore extensions to the
models, including modular pleiotropy, variable effect sizes, mutational bias and
maladaptation of the wild type. We illustrate our approach by reanalysing a large
dataset of mutant effects in a yeast snoRNA. Though characterized by some large
epistatic effects, these data give a good overall fit to the non-epistatic null
model, suggesting that epistasis might have limited influence on the evolutionary
dynamics in this system. We also show how the amount of epistasis depends on both
the underlying fitness landscape and the distribution of mutations, and so is
expected to vary in consistent ways between new mutations, standing variation
and fixed mutations.
article_processing_charge: No
author:
- first_name: Christelle
full_name: Fraisse, Christelle
id: 32DF5794-F248-11E8-B48F-1D18A9856A87
last_name: Fraisse
orcid: 0000-0001-8441-5075
- first_name: John J.
full_name: Welch, John J.
last_name: Welch
citation:
ama: Fraisse C, Welch JJ. Simulation code for Fig S2 from the distribution of epistasis
on simple fitness landscapes. 2020. doi:10.6084/m9.figshare.7957472.v1
apa: Fraisse, C., & Welch, J. J. (2020). Simulation code for Fig S2 from the
distribution of epistasis on simple fitness landscapes. Royal Society of London.
https://doi.org/10.6084/m9.figshare.7957472.v1
chicago: Fraisse, Christelle, and John J. Welch. “Simulation Code for Fig S2 from
the Distribution of Epistasis on Simple Fitness Landscapes.” Royal Society of
London, 2020. https://doi.org/10.6084/m9.figshare.7957472.v1.
ieee: C. Fraisse and J. J. Welch, “Simulation code for Fig S2 from the distribution
of epistasis on simple fitness landscapes.” Royal Society of London, 2020.
ista: Fraisse C, Welch JJ. 2020. Simulation code for Fig S2 from the distribution
of epistasis on simple fitness landscapes, Royal Society of London, 10.6084/m9.figshare.7957472.v1.
mla: Fraisse, Christelle, and John J. Welch. Simulation Code for Fig S2 from
the Distribution of Epistasis on Simple Fitness Landscapes. Royal Society
of London, 2020, doi:10.6084/m9.figshare.7957472.v1.
short: C. Fraisse, J.J. Welch, (2020).
date_created: 2021-08-06T11:18:15Z
date_published: 2020-10-15T00:00:00Z
date_updated: 2023-08-25T10:34:41Z
day: '15'
department:
- _id: BeVi
- _id: NiBa
doi: 10.6084/m9.figshare.7957472.v1
main_file_link:
- open_access: '1'
url: https://doi.org/10.6084/m9.figshare.7957472.v1
month: '10'
oa: 1
oa_version: Published Version
publisher: Royal Society of London
related_material:
record:
- id: '6467'
relation: used_in_publication
status: public
status: public
title: Simulation code for Fig S2 from the distribution of epistasis on simple fitness
landscapes
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9799'
abstract:
- lang: eng
text: Fitness interactions between mutations can influence a population’s evolution
in many different ways. While epistatic effects are difficult to measure precisely,
important information is captured by the mean and variance of log fitnesses for
individuals carrying different numbers of mutations. We derive predictions for
these quantities from a class of simple fitness landscapes, based on models of
optimizing selection on quantitative traits. We also explore extensions to the
models, including modular pleiotropy, variable effect sizes, mutational bias and
maladaptation of the wild type. We illustrate our approach by reanalysing a large
dataset of mutant effects in a yeast snoRNA. Though characterized by some large
epistatic effects, these data give a good overall fit to the non-epistatic null
model, suggesting that epistasis might have limited influence on the evolutionary
dynamics in this system. We also show how the amount of epistasis depends on both
the underlying fitness landscape and the distribution of mutations, and so is
expected to vary in consistent ways between new mutations, standing variation
and fixed mutations.
article_processing_charge: No
author:
- first_name: Christelle
full_name: Fraisse, Christelle
id: 32DF5794-F248-11E8-B48F-1D18A9856A87
last_name: Fraisse
orcid: 0000-0001-8441-5075
- first_name: John J.
full_name: Welch, John J.
last_name: Welch
citation:
ama: Fraisse C, Welch JJ. Simulation code for Fig S1 from the distribution of epistasis
on simple fitness landscapes. 2020. doi:10.6084/m9.figshare.7957469.v1
apa: Fraisse, C., & Welch, J. J. (2020). Simulation code for Fig S1 from the
distribution of epistasis on simple fitness landscapes. Royal Society of London.
https://doi.org/10.6084/m9.figshare.7957469.v1
chicago: Fraisse, Christelle, and John J. Welch. “Simulation Code for Fig S1 from
the Distribution of Epistasis on Simple Fitness Landscapes.” Royal Society of
London, 2020. https://doi.org/10.6084/m9.figshare.7957469.v1.
ieee: C. Fraisse and J. J. Welch, “Simulation code for Fig S1 from the distribution
of epistasis on simple fitness landscapes.” Royal Society of London, 2020.
ista: Fraisse C, Welch JJ. 2020. Simulation code for Fig S1 from the distribution
of epistasis on simple fitness landscapes, Royal Society of London, 10.6084/m9.figshare.7957469.v1.
mla: Fraisse, Christelle, and John J. Welch. Simulation Code for Fig S1 from
the Distribution of Epistasis on Simple Fitness Landscapes. Royal Society
of London, 2020, doi:10.6084/m9.figshare.7957469.v1.
short: C. Fraisse, J.J. Welch, (2020).
date_created: 2021-08-06T11:26:57Z
date_published: 2020-10-15T00:00:00Z
date_updated: 2023-08-25T10:34:41Z
day: '15'
department:
- _id: BeVi
- _id: NiBa
doi: 10.6084/m9.figshare.7957469.v1
main_file_link:
- open_access: '1'
url: https://doi.org/10.6084/m9.figshare.7957469.v1
month: '10'
oa: 1
oa_version: Published Version
publisher: Royal Society of London
related_material:
record:
- id: '6467'
relation: used_in_publication
status: public
status: public
title: Simulation code for Fig S1 from the distribution of epistasis on simple fitness
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type: research_data_reference
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...