---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '9583'
abstract:
- lang: eng
text: We show that for any n divisible by 3, almost all order-n Steiner triple systems
admit a decomposition of almost all their triples into disjoint perfect matchings
(that is, almost all Steiner triple systems are almost resolvable).
article_number: e39
article_processing_charge: No
article_type: original
author:
- first_name: Asaf
full_name: Ferber, Asaf
last_name: Ferber
- first_name: Matthew Alan
full_name: Kwan, Matthew Alan
id: 5fca0887-a1db-11eb-95d1-ca9d5e0453b3
last_name: Kwan
orcid: 0000-0002-4003-7567
citation:
ama: Ferber A, Kwan MA. Almost all Steiner triple systems are almost resolvable.
Forum of Mathematics. 2020;8. doi:10.1017/fms.2020.29
apa: Ferber, A., & Kwan, M. A. (2020). Almost all Steiner triple systems are
almost resolvable. Forum of Mathematics. Cambridge University Press. https://doi.org/10.1017/fms.2020.29
chicago: Ferber, Asaf, and Matthew Alan Kwan. “Almost All Steiner Triple Systems
Are Almost Resolvable.” Forum of Mathematics. Cambridge University Press,
2020. https://doi.org/10.1017/fms.2020.29.
ieee: A. Ferber and M. A. Kwan, “Almost all Steiner triple systems are almost resolvable,”
Forum of Mathematics, vol. 8. Cambridge University Press, 2020.
ista: Ferber A, Kwan MA. 2020. Almost all Steiner triple systems are almost resolvable.
Forum of Mathematics. 8, e39.
mla: Ferber, Asaf, and Matthew Alan Kwan. “Almost All Steiner Triple Systems Are
Almost Resolvable.” Forum of Mathematics, vol. 8, e39, Cambridge University
Press, 2020, doi:10.1017/fms.2020.29.
short: A. Ferber, M.A. Kwan, Forum of Mathematics 8 (2020).
date_created: 2021-06-22T09:12:23Z
date_published: 2020-11-03T00:00:00Z
date_updated: 2024-10-16T12:26:40Z
day: '03'
ddc:
- '510'
doi: 10.1017/fms.2020.29
extern: '1'
external_id:
pmid:
- '1907.06744'
file:
- access_level: open_access
checksum: 5553c596bb4db0f38226a56bee9c87a1
content_type: application/pdf
creator: asandaue
date_created: 2021-06-22T09:23:59Z
date_updated: 2021-06-22T09:23:59Z
file_id: '9584'
file_name: 2020_CambridgeUniversityPress_Ferber.pdf
file_size: 601516
relation: main_file
success: 1
file_date_updated: 2021-06-22T09:23:59Z
has_accepted_license: '1'
intvolume: ' 8'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
pmid: 1
publication: Forum of Mathematics
publication_identifier:
eissn:
- 2050-5094
publication_status: published
publisher: Cambridge University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Almost all Steiner triple systems are almost resolvable
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: 0043cee0-e5fc-11ee-9736-f83bc23afbf0
volume: 8
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
orcid: 0009-0009-9111-8429
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.
corr_author: '1'
date_created: 2021-07-04T22:01:26Z
date_published: 2020-12-14T00:00:00Z
date_updated: 2026-04-02T14:35:31Z
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: Persistent Homology, Algorithms and Stochastic Geometry
publication: Journal of Computational Geometry
publication_identifier:
eissn:
- 1920-180X
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
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)."
alternative_title:
- Advances in Neural Information Processing Systems
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: Vol 33. Neural Information Processing Systems Foundation; 2020:22361-22372.'
apa: 'Aksenov, V., Alistarh, D.-A., & Korhonen, J. (2020). Scalable belief propagation
via relaxed scheduling (Vol. 33, pp. 22361–22372). Presented at the NeurIPS: Conference
on Neural Information Processing Systems, Vancouver, Canada: Neural Information
Processing Systems Foundation.'
chicago: Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief
Propagation via Relaxed Scheduling,” 33:22361–72. Neural Information Processing
Systems Foundation, 2020.
ieee: 'V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation
via relaxed scheduling,” presented at the NeurIPS: Conference on 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. NeurIPS: Conference on Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 33, 22361–22372.'
mla: Aksenov, Vitaly, et al. Scalable Belief Propagation via Relaxed Scheduling.
Vol. 33, Neural Information Processing Systems Foundation, 2020, pp. 22361–72.
short: V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Neural Information Processing
Systems Foundation, 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
corr_author: '1'
date_created: 2021-07-04T22:01:26Z
date_published: 2020-12-06T00:00:00Z
date_updated: 2026-06-18T19:54:53Z
day: '06'
ddc:
- '000'
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_identifier:
isbn:
- '9781713829546'
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable belief propagation via relaxed scheduling
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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.
alternative_title:
- Advances in Neural Information Processing Systems
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: Vol 33. Neural Information Processing Systems
Foundation; 2020:18098-18109.'
apa: 'Singh, S. P., & Alistarh, D.-A. (2020). WoodFisher: Efficient second-order
approximation for neural network compression (Vol. 33, pp. 18098–18109). Presented
at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver,
Canada: Neural Information Processing Systems Foundation.'
chicago: 'Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order
Approximation for Neural Network Compression,” 33:18098–109. Neural Information
Processing Systems Foundation, 2020.'
ieee: 'S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation
for neural network compression,” presented at the NeurIPS: Conference on 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. NeurIPS: Conference on Neural Information Processing
Systems, Advances in 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. Vol. 33, Neural Information
Processing Systems Foundation, 2020, pp. 18098–109.'
short: S.P. Singh, D.-A. Alistarh, in:, Neural Information Processing Systems Foundation,
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
corr_author: '1'
date_created: 2021-07-04T22:01:26Z
date_published: 2020-12-06T00:00:00Z
date_updated: 2026-06-18T19:55:12Z
day: '06'
ddc:
- '000'
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_identifier:
isbn:
- '9781713829546'
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'WoodFisher: Efficient second-order approximation for neural network compression'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 33
year: '2020'
...
---
_id: '9658'
abstract:
- lang: eng
text: Macroscopic models of nucleation provide powerful tools for understanding
activated phase transition processes. These models do not provide atomistic insights
and can thus sometimes lack material-specific descriptions. Here, we provide a
comprehensive framework for constructing a continuum picture from an atomistic
simulation of homogeneous nucleation. We use this framework to determine the equilibrium
shape of the solid nucleus that forms inside bulk liquid for a Lennard-Jones potential.
From this shape, we then extract the anisotropy of the solid-liquid interfacial
free energy, by performing a reverse Wulff construction in the space of spherical
harmonic expansions. We find that the shape of the nucleus is nearly spherical
and that its anisotropy can be perfectly described using classical models.
article_number: '044103'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Michele
full_name: Ceriotti, Michele
last_name: Ceriotti
- first_name: Gareth A.
full_name: Tribello, Gareth A.
last_name: Tribello
citation:
ama: Cheng B, Ceriotti M, Tribello GA. Classical nucleation theory predicts the
shape of the nucleus in homogeneous solidification. The Journal of Chemical
Physics. 2020;152(4). doi:10.1063/1.5134461
apa: Cheng, B., Ceriotti, M., & Tribello, G. A. (2020). Classical nucleation
theory predicts the shape of the nucleus in homogeneous solidification. The
Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/1.5134461
chicago: Cheng, Bingqing, Michele Ceriotti, and Gareth A. Tribello. “Classical Nucleation
Theory Predicts the Shape of the Nucleus in Homogeneous Solidification.” The
Journal of Chemical Physics. AIP Publishing, 2020. https://doi.org/10.1063/1.5134461.
ieee: B. Cheng, M. Ceriotti, and G. A. Tribello, “Classical nucleation theory predicts
the shape of the nucleus in homogeneous solidification,” The Journal of Chemical
Physics, vol. 152, no. 4. AIP Publishing, 2020.
ista: Cheng B, Ceriotti M, Tribello GA. 2020. Classical nucleation theory predicts
the shape of the nucleus in homogeneous solidification. The Journal of Chemical
Physics. 152(4), 044103.
mla: Cheng, Bingqing, et al. “Classical Nucleation Theory Predicts the Shape of
the Nucleus in Homogeneous Solidification.” The Journal of Chemical Physics,
vol. 152, no. 4, 044103, AIP Publishing, 2020, doi:10.1063/1.5134461.
short: B. Cheng, M. Ceriotti, G.A. Tribello, The Journal of Chemical Physics 152
(2020).
date_created: 2021-07-15T07:22:24Z
date_published: 2020-01-31T00:00:00Z
date_updated: 2023-02-23T14:03:55Z
day: '31'
doi: 10.1063/1.5134461
extern: '1'
external_id:
arxiv:
- '1910.13481'
pmid:
- '32007057'
intvolume: ' 152'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://pure.qub.ac.uk/en/publications/classical-nucleation-theory-predicts-the-shape-of-the-nucleus-in-homogeneous-solidification(56af848b-eee8-4e9b-93cf-667373e4a49b).html
month: '01'
oa: 1
oa_version: Submitted Version
pmid: 1
publication: The Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Classical nucleation theory predicts the shape of the nucleus in homogeneous
solidification
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 152
year: '2020'
...
---
_id: '9664'
abstract:
- lang: eng
text: Equilibrium molecular dynamics simulations, in combination with the Green-Kubo
(GK) method, have been extensively used to compute the thermal conductivity of
liquids. However, the GK method relies on an ambiguous definition of the microscopic
heat flux, which depends on how one chooses to distribute energies over atoms.
This ambiguity makes it problematic to employ the GK method for systems with nonpairwise
interactions. In this work, we show that the hydrodynamic description of thermally
driven density fluctuations can be used to obtain the thermal conductivity of
a bulk fluid unambiguously, thereby bypassing the need to define the heat flux.
We verify that, for a model fluid with only pairwise interactions, our method
yields estimates of thermal conductivity consistent with the GK approach. We apply
our approach to compute the thermal conductivity of a nonpairwise additive water
model at supercritical conditions, and of a liquid hydrogen system described by
a machine-learning interatomic potential, at 33 GPa and 2000 K.
article_number: '130602'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Daan
full_name: Frenkel, Daan
last_name: Frenkel
citation:
ama: Cheng B, Frenkel D. Computing the heat conductivity of fluids from density
fluctuations. Physical Review Letters. 2020;125(13). doi:10.1103/physrevlett.125.130602
apa: Cheng, B., & Frenkel, D. (2020). Computing the heat conductivity of fluids
from density fluctuations. Physical Review Letters. American Physical Society.
https://doi.org/10.1103/physrevlett.125.130602
chicago: Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of
Fluids from Density Fluctuations.” Physical Review Letters. American Physical
Society, 2020. https://doi.org/10.1103/physrevlett.125.130602.
ieee: B. Cheng and D. Frenkel, “Computing the heat conductivity of fluids from density
fluctuations,” Physical Review Letters, vol. 125, no. 13. American Physical
Society, 2020.
ista: Cheng B, Frenkel D. 2020. Computing the heat conductivity of fluids from density
fluctuations. Physical Review Letters. 125(13), 130602.
mla: Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of Fluids
from Density Fluctuations.” Physical Review Letters, vol. 125, no. 13,
130602, American Physical Society, 2020, doi:10.1103/physrevlett.125.130602.
short: B. Cheng, D. Frenkel, Physical Review Letters 125 (2020).
date_created: 2021-07-15T12:15:14Z
date_published: 2020-09-25T00:00:00Z
date_updated: 2021-08-09T12:35:58Z
day: '25'
doi: 10.1103/physrevlett.125.130602
extern: '1'
external_id:
arxiv:
- '2005.07562'
pmid:
- '33034481'
intvolume: ' 125'
issue: '13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2005.07562
month: '09'
oa: 1
oa_version: Preprint
pmid: 1
publication: Physical Review Letters
publication_identifier:
eissn:
- 1079-7114
issn:
- 0031-9007
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Computing the heat conductivity of fluids from density fluctuations
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 125
year: '2020'
...
---
OA_place: publisher
OA_type: hybrid
_id: '9666'
abstract:
- lang: eng
text: Predicting phase stabilities of crystal polymorphs is central to computational
materials science and chemistry. Such predictions are challenging because they
first require searching for potential energy minima and then performing arduous
free-energy calculations to account for entropic effects at finite temperatures.
Here, we develop a framework that facilitates such predictions by exploiting all
the information obtained from random searches of crystal structures. This framework
combines automated clustering, classification and visualisation of crystal structures
with machine-learning estimation of their enthalpy and entropy. We demonstrate
the framework on the technologically important system of TiO2, which has many
polymorphs, without relying on prior knowledge of known phases. We find a number
of new phases and predict the phase diagram and metastabilities of crystal polymorphs
at 1600 K, benchmarking the results against full free-energy calculations.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Aleks
full_name: Reinhardt, Aleks
last_name: Reinhardt
- first_name: Chris J.
full_name: Pickard, Chris J.
last_name: Pickard
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Reinhardt A, Pickard CJ, Cheng B. Predicting the phase diagram of titanium
dioxide with random search and pattern recognition. Physical Chemistry Chemical
Physics. 2020;22(22):12697-12705. doi:10.1039/d0cp02513e
apa: Reinhardt, A., Pickard, C. J., & Cheng, B. (2020). Predicting the phase
diagram of titanium dioxide with random search and pattern recognition. Physical
Chemistry Chemical Physics. Royal Society of Chemistry. https://doi.org/10.1039/d0cp02513e
chicago: Reinhardt, Aleks, Chris J. Pickard, and Bingqing Cheng. “Predicting the
Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.”
Physical Chemistry Chemical Physics. Royal Society of Chemistry, 2020.
https://doi.org/10.1039/d0cp02513e.
ieee: A. Reinhardt, C. J. Pickard, and B. Cheng, “Predicting the phase diagram of
titanium dioxide with random search and pattern recognition,” Physical Chemistry
Chemical Physics, vol. 22, no. 22. Royal Society of Chemistry, pp. 12697–12705,
2020.
ista: Reinhardt A, Pickard CJ, Cheng B. 2020. Predicting the phase diagram of titanium
dioxide with random search and pattern recognition. Physical Chemistry Chemical
Physics. 22(22), 12697–12705.
mla: Reinhardt, Aleks, et al. “Predicting the Phase Diagram of Titanium Dioxide
with Random Search and Pattern Recognition.” Physical Chemistry Chemical Physics,
vol. 22, no. 22, Royal Society of Chemistry, 2020, pp. 12697–705, doi:10.1039/d0cp02513e.
short: A. Reinhardt, C.J. Pickard, B. Cheng, Physical Chemistry Chemical Physics
22 (2020) 12697–12705.
date_created: 2021-07-15T12:37:27Z
date_published: 2020-06-14T00:00:00Z
date_updated: 2024-10-16T12:29:54Z
day: '14'
ddc:
- '530'
doi: 10.1039/d0cp02513e
extern: '1'
external_id:
arxiv:
- '1909.08934'
pmid:
- '32459228'
file:
- access_level: open_access
checksum: 0a6872972b1b2e60f9095d39b01753fa
content_type: application/pdf
creator: asandaue
date_created: 2021-07-15T12:43:51Z
date_updated: 2021-07-15T12:43:51Z
file_id: '9667'
file_name: 202_PhysicalChemistryChemicalPhysics_Reinhardt.pdf
file_size: 3151206
relation: main_file
success: 1
file_date_updated: 2021-07-15T12:43:51Z
has_accepted_license: '1'
intvolume: ' 22'
issue: '22'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 12697-12705
pmid: 1
publication: Physical Chemistry Chemical Physics
publication_identifier:
eissn:
- 1463-9084
issn:
- 1463-9076
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predicting the phase diagram of titanium dioxide with random search and pattern
recognition
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: 0043cee0-e5fc-11ee-9736-f83bc23afbf0
volume: 22
year: '2020'
...
---
_id: '9671'
abstract:
- lang: eng
text: Water molecules can arrange into a liquid with complex hydrogen-bond networks
and at least 17 experimentally confirmed ice phases with enormous structural diversity.
It remains a puzzle how or whether this multitude of arrangements in different
phases of water are related. Here we investigate the structural similarities between
liquid water and a comprehensive set of 54 ice phases in simulations, by directly
comparing their local environments using general atomic descriptors, and also
by demonstrating that a machine-learning potential trained on liquid water alone
can predict the densities, lattice energies, and vibrational properties of the
ices. The finding that the local environments characterising the different ice
phases are found in water sheds light on the phase behavior of water, and rationalizes
the transferability of water models between different phases.
article_number: '5757'
article_processing_charge: No
article_type: original
author:
- first_name: Bartomeu
full_name: Monserrat, Bartomeu
last_name: Monserrat
- first_name: Jan Gerit
full_name: Brandenburg, Jan Gerit
last_name: Brandenburg
- first_name: Edgar A.
full_name: Engel, Edgar A.
last_name: Engel
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Monserrat B, Brandenburg JG, Engel EA, Cheng B. Liquid water contains the building
blocks of diverse ice phases. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-19606-y
apa: Monserrat, B., Brandenburg, J. G., Engel, E. A., & Cheng, B. (2020). Liquid
water contains the building blocks of diverse ice phases. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-020-19606-y
chicago: Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing
Cheng. “Liquid Water Contains the Building Blocks of Diverse Ice Phases.” Nature
Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-19606-y.
ieee: B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Liquid water
contains the building blocks of diverse ice phases,” Nature Communications,
vol. 11, no. 1. Springer Nature, 2020.
ista: Monserrat B, Brandenburg JG, Engel EA, Cheng B. 2020. Liquid water contains
the building blocks of diverse ice phases. Nature Communications. 11(1), 5757.
mla: Monserrat, Bartomeu, et al. “Liquid Water Contains the Building Blocks of Diverse
Ice Phases.” Nature Communications, vol. 11, no. 1, 5757, Springer Nature,
2020, doi:10.1038/s41467-020-19606-y.
short: B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, Nature Communications
11 (2020).
date_created: 2021-07-15T14:01:35Z
date_published: 2020-11-13T00:00:00Z
date_updated: 2023-02-23T14:04:25Z
day: '13'
ddc:
- '530'
- '540'
doi: 10.1038/s41467-020-19606-y
extern: '1'
file:
- access_level: open_access
checksum: 1edd9b6d8fa791f8094d87bd6453955b
content_type: application/pdf
creator: asandaue
date_created: 2021-07-15T14:05:45Z
date_updated: 2021-07-15T14:05:45Z
file_id: '9672'
file_name: 2020_NatureCommunications_Monserrat.pdf
file_size: 1385954
relation: main_file
success: 1
file_date_updated: 2021-07-15T14:05:45Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Liquid water contains the building blocks of diverse ice phases
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: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 11
year: '2020'
...
---
_id: '9675'
abstract:
- lang: eng
text: The visualization of data is indispensable in scientific research, from the
early stages when human insight forms to the final step of communicating results.
In computational physics, chemistry and materials science, it can be as simple
as making a scatter plot or as straightforward as looking through the snapshots
of atomic positions manually. However, as a result of the "big data" revolution,
these conventional approaches are often inadequate. The widespread adoption of
high-throughput computation for materials discovery and the associated community-wide
repositories have given rise to data sets that contain an enormous number of compounds
and atomic configurations. A typical data set contains thousands to millions of
atomic structures, along with a diverse range of properties such as formation
energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven
and automated framework for visualizing and analyzing such structural data sets.
The key idea is to construct a low-dimensional representation of the data, which
facilitates navigation, reveals underlying patterns, and helps to identify data
points with unusual attributes. Such data-intensive maps, often employing machine
learning methods, are appearing more and more frequently in the literature. However,
to the wider community, it is not always transparent how these maps are made and
how they should be interpreted. Furthermore, while these maps undoubtedly serve
a decorative purpose in academic publications, it is not always apparent what
extra information can be garnered from reading or making them.This Account attempts
to answer such questions. We start with a concise summary of the theory of representing
chemical environments, followed by the introduction of a simple yet practical
conceptual approach for generating structure maps in a generic and automated manner.
Such analysis and mapping is made nearly effortless by employing the newly developed
software tool ASAP. To showcase the applicability to a wide variety of systems
in chemistry and materials science, we provide several illustrative examples,
including crystalline and amorphous materials, interfaces, and organic molecules.
In these examples, the maps not only help to sift through large data sets but
also reveal hidden patterns that could be easily missed using conventional analyses.The
explosion in the amount of computed information in chemistry and materials science
has made visualization into a science in itself. Not only have we benefited from
exploiting these visualization methods in previous works, we also believe that
the automated mapping of data sets will in turn stimulate further creativity and
exploration, as well as ultimately feed back into future advances in the respective
fields.
article_processing_charge: No
article_type: original
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Ryan-Rhys
full_name: Griffiths, Ryan-Rhys
last_name: Griffiths
- first_name: Simon
full_name: Wengert, Simon
last_name: Wengert
- first_name: Christian
full_name: Kunkel, Christian
last_name: Kunkel
- first_name: Tamas
full_name: Stenczel, Tamas
last_name: Stenczel
- first_name: Bonan
full_name: Zhu, Bonan
last_name: Zhu
- first_name: Volker L.
full_name: Deringer, Volker L.
last_name: Deringer
- first_name: Noam
full_name: Bernstein, Noam
last_name: Bernstein
- first_name: Johannes T.
full_name: Margraf, Johannes T.
last_name: Margraf
- first_name: Karsten
full_name: Reuter, Karsten
last_name: Reuter
- first_name: Gabor
full_name: Csanyi, Gabor
last_name: Csanyi
citation:
ama: Cheng B, Griffiths R-R, Wengert S, et al. Mapping materials and molecules.
Accounts of Chemical Research. 2020;53(9):1981-1991. doi:10.1021/acs.accounts.0c00403
apa: Cheng, B., Griffiths, R.-R., Wengert, S., Kunkel, C., Stenczel, T., Zhu, B.,
… Csanyi, G. (2020). Mapping materials and molecules. Accounts of Chemical
Research. American Chemical Society. https://doi.org/10.1021/acs.accounts.0c00403
chicago: Cheng, Bingqing, Ryan-Rhys Griffiths, Simon Wengert, Christian Kunkel,
Tamas Stenczel, Bonan Zhu, Volker L. Deringer, et al. “Mapping Materials and Molecules.”
Accounts of Chemical Research. American Chemical Society, 2020. https://doi.org/10.1021/acs.accounts.0c00403.
ieee: B. Cheng et al., “Mapping materials and molecules,” Accounts of
Chemical Research, vol. 53, no. 9. American Chemical Society, pp. 1981–1991,
2020.
ista: Cheng B, Griffiths R-R, Wengert S, Kunkel C, Stenczel T, Zhu B, Deringer VL,
Bernstein N, Margraf JT, Reuter K, Csanyi G. 2020. Mapping materials and molecules.
Accounts of Chemical Research. 53(9), 1981–1991.
mla: Cheng, Bingqing, et al. “Mapping Materials and Molecules.” Accounts of Chemical
Research, vol. 53, no. 9, American Chemical Society, 2020, pp. 1981–91, doi:10.1021/acs.accounts.0c00403.
short: B. Cheng, R.-R. Griffiths, S. Wengert, C. Kunkel, T. Stenczel, B. Zhu, V.L.
Deringer, N. Bernstein, J.T. Margraf, K. Reuter, G. Csanyi, Accounts of Chemical
Research 53 (2020) 1981–1991.
date_created: 2021-07-16T06:25:53Z
date_published: 2020-08-14T00:00:00Z
date_updated: 2021-11-24T15:54:41Z
day: '14'
doi: 10.1021/acs.accounts.0c00403
extern: '1'
external_id:
pmid:
- '32794697'
intvolume: ' 53'
issue: '9'
language:
- iso: eng
month: '08'
oa_version: None
page: 1981-1991
pmid: 1
publication: Accounts of Chemical Research
publication_identifier:
eissn:
- 1520-4898
issn:
- 0001-4842
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Mapping materials and molecules
type: journal_article
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 53
year: '2020'
...
---
_id: '9685'
abstract:
- lang: eng
text: Hydrogen, the simplest and most abundant element in the Universe, develops
a remarkably complex behaviour upon compression^1. Since Wigner predicted the
dissociation and metallization of solid hydrogen at megabar pressures almost a
century ago^2, several efforts have been made to explain the many unusual properties
of dense hydrogen, including a rich and poorly understood solid polymorphism^1,3-5,
an anomalous melting line6 and the possible transition to a superconducting state^7.
Experiments at such extreme conditions are challenging and often lead to hard-to-interpret
and controversial observations, whereas theoretical investigations are constrained
by the huge computational cost of sufficiently accurate quantum mechanical calculations.
Here we present a theoretical study of the phase diagram of dense hydrogen that
uses machine learning to 'learn' potential-energy surfaces and interatomic forces
from reference calculations and then predict them at low computational cost, overcoming
length- and timescale limitations. We reproduce both the re-entrant melting behaviour
and the polymorphism of the solid phase. Simulations using our machine-learning-based
potentials provide evidence for a continuous molecular-to-atomic transition in
the liquid, with no first-order transition observed above the melting line. This
suggests a smooth transition between insulating and metallic layers in giant gas
planets, and reconciles existing discrepancies between experiments as a manifestation
of supercritical behaviour.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Guglielmo
full_name: Mazzola, Guglielmo
last_name: Mazzola
- first_name: Chris J.
full_name: Pickard, Chris J.
last_name: Pickard
- first_name: Michele
full_name: Ceriotti, Michele
last_name: Ceriotti
citation:
ama: Cheng B, Mazzola G, Pickard CJ, Ceriotti M. Evidence for supercritical behaviour
of high-pressure liquid hydrogen. Nature. 2020;585(7824):217-220. doi:10.1038/s41586-020-2677-y
apa: Cheng, B., Mazzola, G., Pickard, C. J., & Ceriotti, M. (2020). Evidence
for supercritical behaviour of high-pressure liquid hydrogen. Nature. Springer
Nature. https://doi.org/10.1038/s41586-020-2677-y
chicago: Cheng, Bingqing, Guglielmo Mazzola, Chris J. Pickard, and Michele Ceriotti.
“Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen.” Nature.
Springer Nature, 2020. https://doi.org/10.1038/s41586-020-2677-y.
ieee: B. Cheng, G. Mazzola, C. J. Pickard, and M. Ceriotti, “Evidence for supercritical
behaviour of high-pressure liquid hydrogen,” Nature, vol. 585, no. 7824.
Springer Nature, pp. 217–220, 2020.
ista: Cheng B, Mazzola G, Pickard CJ, Ceriotti M. 2020. Evidence for supercritical
behaviour of high-pressure liquid hydrogen. Nature. 585(7824), 217–220.
mla: Cheng, Bingqing, et al. “Evidence for Supercritical Behaviour of High-Pressure
Liquid Hydrogen.” Nature, vol. 585, no. 7824, Springer Nature, 2020, pp.
217–20, doi:10.1038/s41586-020-2677-y.
short: B. Cheng, G. Mazzola, C.J. Pickard, M. Ceriotti, Nature 585 (2020) 217–220.
date_created: 2021-07-19T09:17:49Z
date_published: 2020-09-10T00:00:00Z
date_updated: 2021-08-09T12:38:01Z
day: '10'
doi: 10.1038/s41586-020-2677-y
extern: '1'
external_id:
arxiv:
- '1906.03341'
pmid:
- '32908269'
intvolume: ' 585'
issue: '7824'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1906.03341
month: '09'
oa: 1
oa_version: Preprint
page: 217-220
pmid: 1
publication: Nature
publication_identifier:
eissn:
- 1476-4687
issn:
- 0028-0836
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Evidence for supercritical behaviour of high-pressure liquid hydrogen
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 585
year: '2020'
...
---
_id: '9699'
abstract:
- lang: eng
text: "We investigate the structural similarities between liquid water and 53 ices,
including 20 known crystalline phases. We base such similarity comparison on the
local environments that consist of atoms within a certain cutoff radius of a central
atom. We reveal that liquid water explores the local environments of the diverse
ice phases, by directly comparing the environments in these phases using general
atomic descriptors, and also by demonstrating that a machine-learning potential
trained on liquid water alone can predict the densities, the lattice energies,
and vibrational properties of the\r\nices. The finding that the local environments
characterising the different ice phases are found in water sheds light on water
phase behaviors, and rationalizes the transferability of water models between
different phases."
article_number: '2006.13316'
article_processing_charge: No
arxiv: 1
author:
- first_name: Bartomeu
full_name: Monserrat, Bartomeu
last_name: Monserrat
- first_name: Jan Gerit
full_name: Brandenburg, Jan Gerit
last_name: Brandenburg
- first_name: Edgar A.
full_name: Engel, Edgar A.
last_name: Engel
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 'Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from
liquid water: Why a machine-learning water model generalizes so well. arXiv.
doi:10.48550/arXiv.2006.13316'
apa: 'Monserrat, B., Brandenburg, J. G., Engel, E. A., & Cheng, B. (n.d.). Extracting
ice phases from liquid water: Why a machine-learning water model generalizes so
well. arXiv. https://doi.org/10.48550/arXiv.2006.13316'
chicago: 'Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing
Cheng. “Extracting Ice Phases from Liquid Water: Why a Machine-Learning Water
Model Generalizes so Well.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2006.13316.'
ieee: 'B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Extracting ice
phases from liquid water: Why a machine-learning water model generalizes so well,”
arXiv. .'
ista: 'Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from
liquid water: Why a machine-learning water model generalizes so well. arXiv, 2006.13316.'
mla: 'Monserrat, Bartomeu, et al. “Extracting Ice Phases from Liquid Water: Why
a Machine-Learning Water Model Generalizes so Well.” ArXiv, 2006.13316,
doi:10.48550/arXiv.2006.13316.'
short: B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, ArXiv (n.d.).
date_created: 2021-07-20T11:25:15Z
date_published: 2020-06-23T00:00:00Z
date_updated: 2024-10-14T12:05:56Z
day: '23'
doi: 10.48550/arXiv.2006.13316
extern: '1'
external_id:
arxiv:
- '2006.13316'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2006.13316
month: '06'
oa: 1
oa_version: Submitted Version
publication: arXiv
publication_status: submitted
status: public
title: 'Extracting ice phases from liquid water: Why a machine-learning water model
generalizes so well'
type: preprint
user_id: 2DF688A6-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: 2026-04-02T14:28:32Z
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: 2025-07-10T11:54:51Z
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: '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: 2025-06-12T06:58:06Z
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: 2025-06-12T06:58:06Z
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: 2025-06-12T06:58:06Z
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: used_in_publication
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: 2026-06-18T19:32:35Z
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'
...
---
OA_place: publisher
OA_type: hybrid
_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: 2026-06-18T19:05:02Z
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: 0043cee0-e5fc-11ee-9736-f83bc23afbf0
year: '2020'
...
---
OA_place: publisher
OA_type: hybrid
_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: 2026-06-18T19:05:02Z
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
landscapes
type: research_data_reference
user_id: 0043cee0-e5fc-11ee-9736-f83bc23afbf0
year: '2020'
...
---
_id: '6488'
abstract:
- lang: eng
text: We prove a central limit theorem for the difference of linear eigenvalue statistics
of a sample covariance matrix W˜ and its minor W. We find that the fluctuation
of this difference is much smaller than those of the individual linear statistics,
as a consequence of the strong correlation between the eigenvalues of W˜ and W.
Our result identifies the fluctuation of the spatial derivative of the approximate
Gaussian field in the recent paper by Dumitru and Paquette. Unlike in a similar
result for Wigner matrices, for sample covariance matrices, the fluctuation may
entirely vanish.
article_number: '2050006'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Giorgio
full_name: Cipolloni, Giorgio
id: 42198EFA-F248-11E8-B48F-1D18A9856A87
last_name: Cipolloni
orcid: 0000-0002-4901-7992
- first_name: László
full_name: Erdös, László
id: 4DBD5372-F248-11E8-B48F-1D18A9856A87
last_name: Erdös
orcid: 0000-0001-5366-9603
citation:
ama: 'Cipolloni G, Erdös L. Fluctuations for differences of linear eigenvalue statistics
for sample covariance matrices. Random Matrices: Theory and Application.
2020;9(3). doi:10.1142/S2010326320500069'
apa: 'Cipolloni, G., & Erdös, L. (2020). Fluctuations for differences of linear
eigenvalue statistics for sample covariance matrices. Random Matrices: Theory
and Application. World Scientific Publishing. https://doi.org/10.1142/S2010326320500069'
chicago: 'Cipolloni, Giorgio, and László Erdös. “Fluctuations for Differences of
Linear Eigenvalue Statistics for Sample Covariance Matrices.” Random Matrices:
Theory and Application. World Scientific Publishing, 2020. https://doi.org/10.1142/S2010326320500069.'
ieee: 'G. Cipolloni and L. Erdös, “Fluctuations for differences of linear eigenvalue
statistics for sample covariance matrices,” Random Matrices: Theory and Application,
vol. 9, no. 3. World Scientific Publishing, 2020.'
ista: 'Cipolloni G, Erdös L. 2020. Fluctuations for differences of linear eigenvalue
statistics for sample covariance matrices. Random Matrices: Theory and Application.
9(3), 2050006.'
mla: 'Cipolloni, Giorgio, and László Erdös. “Fluctuations for Differences of Linear
Eigenvalue Statistics for Sample Covariance Matrices.” Random Matrices: Theory
and Application, vol. 9, no. 3, 2050006, World Scientific Publishing, 2020,
doi:10.1142/S2010326320500069.'
short: 'G. Cipolloni, L. Erdös, Random Matrices: Theory and Application 9 (2020).'
date_created: 2019-05-26T21:59:14Z
date_published: 2020-07-01T00:00:00Z
date_updated: 2025-07-10T11:53:26Z
day: '01'
department:
- _id: LaEr
doi: 10.1142/S2010326320500069
ec_funded: 1
external_id:
arxiv:
- '1806.08751'
isi:
- '000547464400001'
intvolume: ' 9'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.08751
month: '07'
oa: 1
oa_version: Preprint
project:
- _id: 258DCDE6-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '338804'
name: Random matrices, universality and disordered quantum systems
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: 'Random Matrices: Theory and Application'
publication_identifier:
eissn:
- 2010-3271
issn:
- 2010-3263
publication_status: published
publisher: World Scientific Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Fluctuations for differences of linear eigenvalue statistics for sample covariance
matrices
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9
year: '2020'
...