---
_id: '9299'
abstract:
- lang: eng
text: We call a multigraph non-homotopic if it can be drawn in the plane in such
a way that no two edges connecting the same pair of vertices can be continuously
transformed into each other without passing through a vertex, and no loop can
be shrunk to its end-vertex in the same way. It is easy to see that a non-homotopic
multigraph on n>1 vertices can have arbitrarily many edges. We prove that the
number of crossings between the edges of a non-homotopic multigraph with n vertices
and m>4n edges is larger than cm2n for some constant c>0 , and that this
bound is tight up to a polylogarithmic factor. We also show that the lower bound
is not asymptotically sharp as n is fixed and m⟶∞ .
acknowledgement: Supported by the National Research, Development and Innovation Office,
NKFIH, KKP-133864, K-131529, K-116769, K-132696, by the Higher Educational Institutional
Excellence Program 2019 NKFIH-1158-6/2019, the Austrian Science Fund (FWF), grant
Z 342-N31, by the Ministry of Education and Science of the Russian Federation MegaGrant
No. 075-15-2019-1926, and by the ERC Synergy Grant “Dynasnet” No. 810115. A full
version can be found at https://arxiv.org/abs/2006.14908.
article_processing_charge: No
arxiv: 1
author:
- first_name: János
full_name: Pach, János
id: E62E3130-B088-11EA-B919-BF823C25FEA4
last_name: Pach
- first_name: Gábor
full_name: Tardos, Gábor
last_name: Tardos
- first_name: Géza
full_name: Tóth, Géza
last_name: Tóth
citation:
ama: 'Pach J, Tardos G, Tóth G. Crossings between non-homotopic edges. In: 28th
International Symposium on Graph Drawing and Network Visualization. Vol 12590.
LNCS. Springer Nature; 2020:359-371. doi:10.1007/978-3-030-68766-3_28'
apa: 'Pach, J., Tardos, G., & Tóth, G. (2020). Crossings between non-homotopic
edges. In 28th International Symposium on Graph Drawing and Network Visualization
(Vol. 12590, pp. 359–371). Virtual, Online: Springer Nature. https://doi.org/10.1007/978-3-030-68766-3_28'
chicago: Pach, János, Gábor Tardos, and Géza Tóth. “Crossings between Non-Homotopic
Edges.” In 28th International Symposium on Graph Drawing and Network Visualization,
12590:359–71. LNCS. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-68766-3_28.
ieee: J. Pach, G. Tardos, and G. Tóth, “Crossings between non-homotopic edges,”
in 28th International Symposium on Graph Drawing and Network Visualization,
Virtual, Online, 2020, vol. 12590, pp. 359–371.
ista: 'Pach J, Tardos G, Tóth G. 2020. Crossings between non-homotopic edges. 28th
International Symposium on Graph Drawing and Network Visualization. GD: Graph
Drawing and Network VisualizationLNCS vol. 12590, 359–371.'
mla: Pach, János, et al. “Crossings between Non-Homotopic Edges.” 28th International
Symposium on Graph Drawing and Network Visualization, vol. 12590, Springer
Nature, 2020, pp. 359–71, doi:10.1007/978-3-030-68766-3_28.
short: J. Pach, G. Tardos, G. Tóth, in:, 28th International Symposium on Graph Drawing
and Network Visualization, Springer Nature, 2020, pp. 359–371.
conference:
end_date: 2020-09-18
location: Virtual, Online
name: 'GD: Graph Drawing and Network Visualization'
start_date: 2020-09-16
date_created: 2021-03-28T22:01:44Z
date_published: 2020-09-20T00:00:00Z
date_updated: 2021-04-06T11:32:32Z
day: '20'
department:
- _id: HeEd
doi: 10.1007/978-3-030-68766-3_28
external_id:
arxiv:
- '2006.14908'
intvolume: ' 12590'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2006.14908
month: '09'
oa: 1
oa_version: Preprint
page: 359-371
project:
- _id: 268116B8-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00342
name: The Wittgenstein Prize
publication: 28th International Symposium on Graph Drawing and Network Visualization
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783030687656'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
series_title: LNCS
status: public
title: Crossings between non-homotopic edges
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 12590
year: '2020'
...
---
_id: '9308'
acknowledgement: This research was carried out with the support of the Russian Foundation
for Basic Research(grant no. 19-01-00169)
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Sergey
full_name: Avvakumov, Sergey
id: 3827DAC8-F248-11E8-B48F-1D18A9856A87
last_name: Avvakumov
- first_name: Uli
full_name: Wagner, Uli
id: 36690CA2-F248-11E8-B48F-1D18A9856A87
last_name: Wagner
orcid: 0000-0002-1494-0568
- first_name: Isaac
full_name: Mabillard, Isaac
id: 32BF9DAA-F248-11E8-B48F-1D18A9856A87
last_name: Mabillard
- first_name: A. B.
full_name: Skopenkov, A. B.
last_name: Skopenkov
citation:
ama: Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. Eliminating higher-multiplicity
intersections, III. Codimension 2. Russian Mathematical Surveys. 2020;75(6):1156-1158.
doi:10.1070/RM9943
apa: Avvakumov, S., Wagner, U., Mabillard, I., & Skopenkov, A. B. (2020). Eliminating
higher-multiplicity intersections, III. Codimension 2. Russian Mathematical
Surveys. IOP Publishing. https://doi.org/10.1070/RM9943
chicago: Avvakumov, Sergey, Uli Wagner, Isaac Mabillard, and A. B. Skopenkov. “Eliminating
Higher-Multiplicity Intersections, III. Codimension 2.” Russian Mathematical
Surveys. IOP Publishing, 2020. https://doi.org/10.1070/RM9943.
ieee: S. Avvakumov, U. Wagner, I. Mabillard, and A. B. Skopenkov, “Eliminating higher-multiplicity
intersections, III. Codimension 2,” Russian Mathematical Surveys, vol.
75, no. 6. IOP Publishing, pp. 1156–1158, 2020.
ista: Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. 2020. Eliminating higher-multiplicity
intersections, III. Codimension 2. Russian Mathematical Surveys. 75(6), 1156–1158.
mla: Avvakumov, Sergey, et al. “Eliminating Higher-Multiplicity Intersections, III.
Codimension 2.” Russian Mathematical Surveys, vol. 75, no. 6, IOP Publishing,
2020, pp. 1156–58, doi:10.1070/RM9943.
short: S. Avvakumov, U. Wagner, I. Mabillard, A.B. Skopenkov, Russian Mathematical
Surveys 75 (2020) 1156–1158.
date_created: 2021-04-04T22:01:22Z
date_published: 2020-12-01T00:00:00Z
date_updated: 2023-08-14T11:43:54Z
day: '01'
department:
- _id: UlWa
doi: 10.1070/RM9943
external_id:
arxiv:
- '1511.03501'
isi:
- '000625983100001'
intvolume: ' 75'
isi: 1
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1511.03501
month: '12'
oa: 1
oa_version: Preprint
page: 1156-1158
publication: Russian Mathematical Surveys
publication_identifier:
issn:
- 0036-0279
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
related_material:
record:
- id: '8183'
relation: earlier_version
status: public
- id: '10220'
relation: later_version
status: public
scopus_import: '1'
status: public
title: Eliminating higher-multiplicity intersections, III. Codimension 2
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 75
year: '2020'
...
---
_id: '9326'
abstract:
- lang: eng
text: The mitochondrial respiratory chain, formed by five protein complexes, utilizes
energy from catabolic processes to synthesize ATP. Complex I, the first and the
largest protein complex of the chain, harvests electrons from NADH to reduce quinone,
while pumping protons across the mitochondrial membrane. Detailed knowledge of
the working principle of such coupled charge-transfer processes remains, however,
fragmentary due to bottlenecks in understanding redox-driven conformational transitions
and their interplay with the hydrated proton pathways. Complex I from Thermus
thermophilus encases 16 subunits with nine iron–sulfur clusters, reduced by electrons
from NADH. Here, employing the latest crystal structure of T. thermophilus complex
I, we have used microsecond-scale molecular dynamics simulations to study the
chemo-mechanical coupling between redox changes of the iron–sulfur clusters and
conformational transitions across complex I. First, we identify the redox switches
within complex I, which allosterically couple the dynamics of the quinone binding
pocket to the site of NADH reduction. Second, our free-energy calculations reveal
that the affinity of the quinone, specifically menaquinone, for the binding-site
is higher than that of its reduced, menaquinol forma design essential for menaquinol
release. Remarkably, the barriers to diffusive menaquinone dynamics are lesser
than that of the more ubiquitous ubiquinone, and the naphthoquinone headgroup
of the former furnishes stronger binding interactions with the pocket, favoring
menaquinone for charge transport in T. thermophilus. Our computations are consistent
with experimentally validated mutations and hierarchize the key residues into
three functional classes, identifying new mutation targets. Third, long-range
hydrogen-bond networks connecting the quinone-binding site to the transmembrane
subunits are found to be responsible for proton pumping. Put together, the simulations
reveal the molecular design principles linking redox reactions to quinone turnover
to proton translocation in complex I.
article_processing_charge: No
author:
- first_name: Chitrak
full_name: Gupta, Chitrak
last_name: Gupta
- first_name: Umesh
full_name: Khaniya, Umesh
last_name: Khaniya
- first_name: Chun
full_name: Chan, Chun
last_name: Chan
- first_name: Francois
full_name: Dehez, Francois
last_name: Dehez
- first_name: Mrinal
full_name: Shekhar, Mrinal
last_name: Shekhar
- first_name: M. R.
full_name: Gunner, M. R.
last_name: Gunner
- first_name: Leonid A
full_name: Sazanov, Leonid A
id: 338D39FE-F248-11E8-B48F-1D18A9856A87
last_name: Sazanov
orcid: 0000-0002-0977-7989
- first_name: Christophe
full_name: Chipot, Christophe
last_name: Chipot
- first_name: Abhishek
full_name: Singharoy, Abhishek
last_name: Singharoy
citation:
ama: Gupta C, Khaniya U, Chan C, et al. Charge transfer and chemo-mechanical coupling
in respiratory complex I. 2020. doi:10.1021/jacs.9b13450.s002
apa: Gupta, C., Khaniya, U., Chan, C., Dehez, F., Shekhar, M., Gunner, M. R., …
Singharoy, A. (2020). Charge transfer and chemo-mechanical coupling in respiratory
complex I. American Chemical Society. https://doi.org/10.1021/jacs.9b13450.s002
chicago: Gupta, Chitrak, Umesh Khaniya, Chun Chan, Francois Dehez, Mrinal Shekhar,
M. R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Charge
Transfer and Chemo-Mechanical Coupling in Respiratory Complex I.” American Chemical
Society, 2020. https://doi.org/10.1021/jacs.9b13450.s002.
ieee: C. Gupta et al., “Charge transfer and chemo-mechanical coupling in
respiratory complex I.” American Chemical Society, 2020.
ista: Gupta C, Khaniya U, Chan C, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot
C, Singharoy A. 2020. Charge transfer and chemo-mechanical coupling in respiratory
complex I, American Chemical Society, 10.1021/jacs.9b13450.s002.
mla: Gupta, Chitrak, et al. Charge Transfer and Chemo-Mechanical Coupling in
Respiratory Complex I. American Chemical Society, 2020, doi:10.1021/jacs.9b13450.s002.
short: C. Gupta, U. Khaniya, C. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov,
C. Chipot, A. Singharoy, (2020).
date_created: 2021-04-14T12:05:20Z
date_published: 2020-05-20T00:00:00Z
date_updated: 2023-08-22T07:49:37Z
day: '20'
department:
- _id: LeSa
doi: 10.1021/jacs.9b13450.s002
license: https://creativecommons.org/licenses/by-nc/4.0/
main_file_link:
- open_access: '1'
month: '05'
oa: 1
oa_version: Published Version
publisher: American Chemical Society
related_material:
record:
- id: '8040'
relation: used_in_publication
status: public
status: public
title: Charge transfer and chemo-mechanical coupling in respiratory complex I
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '9415'
abstract:
- lang: eng
text: 'Optimizing convolutional neural networks for fast inference has recently
become an extremely active area of research. One of the go-to solutions in this
context is weight pruning, which aims to reduce computational and memory footprint
by removing large subsets of the connections in a neural network. Surprisingly,
much less attention has been given to exploiting sparsity in the activation maps,
which tend to be naturally sparse in many settings thanks to the structure of
rectified linear (ReLU) activation functions. In this paper, we present an in-depth
analysis of methods for maximizing the sparsity of the activations in a trained
neural network, and show that, when coupled with an efficient sparse-input convolution
algorithm, we can leverage this sparsity for significant performance gains. To
induce highly sparse activation maps without accuracy loss, we introduce a new
regularization technique, coupled with a new threshold-based sparsification method
based on a parameterized activation function called Forced-Activation-Threshold
Rectified Linear Unit (FATReLU). We examine the impact of our methods on popular
image classification models, showing that most architectures can adapt to significantly
sparser activation maps without any accuracy loss. Our second contribution is
showing that these these compression gains can be translated into inference speedups:
we provide a new algorithm to enable fast convolution operations over networks
with sparse activations, and show that it can enable significant speedups for
end-to-end inference on a range of popular models on the large-scale ImageNet
image classification task on modern Intel CPUs, with little or no retraining cost. '
article_processing_charge: No
author:
- first_name: Mark
full_name: Kurtz, Mark
last_name: Kurtz
- first_name: Justin
full_name: Kopinsky, Justin
last_name: Kopinsky
- first_name: Rati
full_name: Gelashvili, Rati
last_name: Gelashvili
- first_name: Alexander
full_name: Matveev, Alexander
last_name: Matveev
- first_name: John
full_name: Carr, John
last_name: Carr
- first_name: Michael
full_name: Goin, Michael
last_name: Goin
- first_name: William
full_name: Leiserson, William
last_name: Leiserson
- first_name: Sage
full_name: Moore, Sage
last_name: Moore
- first_name: Bill
full_name: Nell, Bill
last_name: Nell
- first_name: Nir
full_name: Shavit, Nir
last_name: Shavit
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation
sparsity for fast neural network inference. In: 37th International Conference
on Machine Learning, ICML 2020. Vol 119. ; 2020:5533-5543.'
apa: Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M., …
Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural
network inference. In 37th International Conference on Machine Learning, ICML
2020 (Vol. 119, pp. 5533–5543). Online.
chicago: Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John
Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation
Sparsity for Fast Neural Network Inference.” In 37th International Conference
on Machine Learning, ICML 2020, 119:5533–43, 2020.
ieee: M. Kurtz et al., “Inducing and exploiting activation sparsity for fast
neural network inference,” in 37th International Conference on Machine Learning,
ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
ista: 'Kurtz M, Kopinsky J, Gelashvili R, Matveev A, Carr J, Goin M, Leiserson W,
Moore S, Nell B, Shavit N, Alistarh D-A. 2020. Inducing and exploiting activation
sparsity for fast neural network inference. 37th International Conference on Machine
Learning, ICML 2020. ICML: International Conference on Machine Learning vol. 119,
5533–5543.'
mla: Kurtz, Mark, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural
Network Inference.” 37th International Conference on Machine Learning, ICML
2020, vol. 119, 2020, pp. 5533–43.
short: M. Kurtz, J. Kopinsky, R. Gelashvili, A. Matveev, J. Carr, M. Goin, W. Leiserson,
S. Moore, B. Nell, N. Shavit, D.-A. Alistarh, in:, 37th International Conference
on Machine Learning, ICML 2020, 2020, pp. 5533–5543.
conference:
end_date: 2020-07-18
location: Online
name: 'ICML: International Conference on Machine Learning'
start_date: 2020-07-12
date_created: 2021-05-23T22:01:45Z
date_published: 2020-07-12T00:00:00Z
date_updated: 2023-02-23T13:57:24Z
day: '12'
ddc:
- '000'
department:
- _id: DaAl
file:
- access_level: open_access
checksum: 2aaaa7d7226e49161311d91627cf783b
content_type: application/pdf
creator: kschuh
date_created: 2021-05-25T09:51:36Z
date_updated: 2021-05-25T09:51:36Z
file_id: '9421'
file_name: 2020_PMLR_Kurtz.pdf
file_size: 741899
relation: main_file
success: 1
file_date_updated: 2021-05-25T09:51:36Z
has_accepted_license: '1'
intvolume: ' 119'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 5533-5543
publication: 37th International Conference on Machine Learning, ICML 2020
publication_identifier:
issn:
- 2640-3498
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inducing and exploiting activation sparsity for fast neural network inference
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 119
year: '2020'
...
---
_id: '9526'
abstract:
- lang: eng
text: DNA methylation and histone H1 mediate transcriptional silencing of genes
and transposable elements, but how they interact is unclear. In plants and animals
with mosaic genomic methylation, functionally mysterious methylation is also common
within constitutively active housekeeping genes. Here, we show that H1 is enriched
in methylated sequences, including genes, of Arabidopsis thaliana, yet this enrichment
is independent of DNA methylation. Loss of H1 disperses heterochromatin, globally
alters nucleosome organization, and activates H1-bound genes, but only weakly
de-represses transposable elements. However, H1 loss strongly activates transposable
elements hypomethylated through mutation of DNA methyltransferase MET1. Hypomethylation
of genes also activates antisense transcription, which is modestly enhanced by
H1 loss. Our results demonstrate that H1 and DNA methylation jointly maintain
transcriptional homeostasis by silencing transposable elements and aberrant intragenic
transcripts. Such functionality plausibly explains why DNA methylation, a well-known
mutagen, has been maintained within coding sequences of crucial plant and animal
genes.
article_processing_charge: No
article_type: original
author:
- first_name: Jaemyung
full_name: Choi, Jaemyung
last_name: Choi
- first_name: David B.
full_name: Lyons, David B.
last_name: Lyons
- first_name: M. Yvonne
full_name: Kim, M. Yvonne
last_name: Kim
- first_name: Jonathan D.
full_name: Moore, Jonathan D.
last_name: Moore
- first_name: Daniel
full_name: Zilberman, Daniel
id: 6973db13-dd5f-11ea-814e-b3e5455e9ed1
last_name: Zilberman
orcid: 0000-0002-0123-8649
citation:
ama: Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. DNA methylation and histone
H1 jointly repress transposable elements and aberrant intragenic transcripts.
Molecular Cell. 2020;77(2):310-323.e7. doi:10.1016/j.molcel.2019.10.011
apa: Choi, J., Lyons, D. B., Kim, M. Y., Moore, J. D., & Zilberman, D. (2020).
DNA methylation and histone H1 jointly repress transposable elements and aberrant
intragenic transcripts. Molecular Cell. Elsevier. https://doi.org/10.1016/j.molcel.2019.10.011
chicago: Choi, Jaemyung, David B. Lyons, M. Yvonne Kim, Jonathan D. Moore, and Daniel
Zilberman. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements
and Aberrant Intragenic Transcripts.” Molecular Cell. Elsevier, 2020. https://doi.org/10.1016/j.molcel.2019.10.011.
ieee: J. Choi, D. B. Lyons, M. Y. Kim, J. D. Moore, and D. Zilberman, “DNA methylation
and histone H1 jointly repress transposable elements and aberrant intragenic transcripts,”
Molecular Cell, vol. 77, no. 2. Elsevier, p. 310–323.e7, 2020.
ista: Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. 2020. DNA methylation and
histone H1 jointly repress transposable elements and aberrant intragenic transcripts.
Molecular Cell. 77(2), 310–323.e7.
mla: Choi, Jaemyung, et al. “DNA Methylation and Histone H1 Jointly Repress Transposable
Elements and Aberrant Intragenic Transcripts.” Molecular Cell, vol. 77,
no. 2, Elsevier, 2020, p. 310–323.e7, doi:10.1016/j.molcel.2019.10.011.
short: J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77
(2020) 310–323.e7.
date_created: 2021-06-08T06:37:09Z
date_published: 2020-01-16T00:00:00Z
date_updated: 2021-12-14T07:51:15Z
day: '16'
department:
- _id: DaZi
doi: 10.1016/j.molcel.2019.10.011
extern: '1'
external_id:
pmid:
- '31732458'
intvolume: ' 77'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.molcel.2019.10.011
month: '01'
oa: 1
oa_version: Published Version
page: 310-323.e7
pmid: 1
publication: Molecular Cell
publication_identifier:
eissn:
- 1097-4164
issn:
- 1097-2765
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: DNA methylation and histone H1 jointly repress transposable elements and aberrant
intragenic transcripts
type: journal_article
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 77
year: '2020'
...
---
_id: '9573'
abstract:
- lang: eng
text: It is a classical fact that for any ε>0, a random permutation of length n=(1+ε)k2/4
typically contains a monotone subsequence of length k. As a far-reaching generalization,
Alon conjectured that a random permutation of this same length n is typically
k-universal, meaning that it simultaneously contains every pattern of length k.
He also made the simple observation that for n=O(k2logk), a random length-n permutation
is typically k-universal. We make the first significant progress towards Alon's
conjecture by showing that n=2000k2loglogk suffices.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Xiaoyu
full_name: He, Xiaoyu
last_name: He
- 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: He X, Kwan MA. Universality of random permutations. Bulletin of the London
Mathematical Society. 2020;52(3):515-529. doi:10.1112/blms.12345
apa: He, X., & Kwan, M. A. (2020). Universality of random permutations. Bulletin
of the London Mathematical Society. Wiley. https://doi.org/10.1112/blms.12345
chicago: He, Xiaoyu, and Matthew Alan Kwan. “Universality of Random Permutations.”
Bulletin of the London Mathematical Society. Wiley, 2020. https://doi.org/10.1112/blms.12345.
ieee: X. He and M. A. Kwan, “Universality of random permutations,” Bulletin of
the London Mathematical Society, vol. 52, no. 3. Wiley, pp. 515–529, 2020.
ista: He X, Kwan MA. 2020. Universality of random permutations. Bulletin of the
London Mathematical Society. 52(3), 515–529.
mla: He, Xiaoyu, and Matthew Alan Kwan. “Universality of Random Permutations.” Bulletin
of the London Mathematical Society, vol. 52, no. 3, Wiley, 2020, pp. 515–29,
doi:10.1112/blms.12345.
short: X. He, M.A. Kwan, Bulletin of the London Mathematical Society 52 (2020) 515–529.
date_created: 2021-06-21T06:23:42Z
date_published: 2020-06-01T00:00:00Z
date_updated: 2023-02-23T14:01:23Z
day: '01'
doi: 10.1112/blms.12345
extern: '1'
external_id:
arxiv:
- '1911.12878'
intvolume: ' 52'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1911.12878
month: '06'
oa: 1
oa_version: Preprint
page: 515-529
publication: Bulletin of the London Mathematical Society
publication_identifier:
eissn:
- 1469-2120
issn:
- 0024-6093
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Universality of random permutations
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 52
year: '2020'
...
---
_id: '9576'
abstract:
- lang: eng
text: In 1989, Rota made the following conjecture. Given n bases B1,…,Bn in an n-dimensional
vector space V, one can always find n disjoint bases of V, each containing exactly
one element from each Bi (we call such bases transversal bases). Rota’s basis
conjecture remains wide open despite its apparent simplicity and the efforts of
many researchers (e.g., the conjecture was recently the subject of the collaborative
“Polymath” project). In this paper we prove that one can always find (1/2−o(1))n
disjoint transversal bases, improving on the previous best bound of Ω(n/logn).
Our results also apply to the more general setting of matroids.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Matija
full_name: Bucić, Matija
last_name: Bucić
- first_name: Matthew Alan
full_name: Kwan, Matthew Alan
id: 5fca0887-a1db-11eb-95d1-ca9d5e0453b3
last_name: Kwan
orcid: 0000-0002-4003-7567
- first_name: Alexey
full_name: Pokrovskiy, Alexey
last_name: Pokrovskiy
- first_name: Benny
full_name: Sudakov, Benny
last_name: Sudakov
citation:
ama: Bucić M, Kwan MA, Pokrovskiy A, Sudakov B. Halfway to Rota’s basis conjecture.
International Mathematics Research Notices. 2020;2020(21):8007-8026. doi:10.1093/imrn/rnaa004
apa: Bucić, M., Kwan, M. A., Pokrovskiy, A., & Sudakov, B. (2020). Halfway to
Rota’s basis conjecture. International Mathematics Research Notices. Oxford
University Press. https://doi.org/10.1093/imrn/rnaa004
chicago: Bucić, Matija, Matthew Alan Kwan, Alexey Pokrovskiy, and Benny Sudakov.
“Halfway to Rota’s Basis Conjecture.” International Mathematics Research Notices.
Oxford University Press, 2020. https://doi.org/10.1093/imrn/rnaa004.
ieee: M. Bucić, M. A. Kwan, A. Pokrovskiy, and B. Sudakov, “Halfway to Rota’s basis
conjecture,” International Mathematics Research Notices, vol. 2020, no.
21. Oxford University Press, pp. 8007–8026, 2020.
ista: Bucić M, Kwan MA, Pokrovskiy A, Sudakov B. 2020. Halfway to Rota’s basis conjecture.
International Mathematics Research Notices. 2020(21), 8007–8026.
mla: Bucić, Matija, et al. “Halfway to Rota’s Basis Conjecture.” International
Mathematics Research Notices, vol. 2020, no. 21, Oxford University Press,
2020, pp. 8007–26, doi:10.1093/imrn/rnaa004.
short: M. Bucić, M.A. Kwan, A. Pokrovskiy, B. Sudakov, International Mathematics
Research Notices 2020 (2020) 8007–8026.
date_created: 2021-06-21T08:12:30Z
date_published: 2020-11-01T00:00:00Z
date_updated: 2023-02-23T14:01:30Z
day: '01'
doi: 10.1093/imrn/rnaa004
extern: '1'
external_id:
arxiv:
- '1810.07462'
intvolume: ' 2020'
issue: '21'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv-export-lb.library.cornell.edu/abs/1810.07462
month: '11'
oa: 1
oa_version: Preprint
page: 8007-8026
publication: International Mathematics Research Notices
publication_identifier:
eissn:
- 1687-0247
issn:
- 1073-7928
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Halfway to Rota’s basis conjecture
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 2020
year: '2020'
...
---
_id: '9577'
abstract:
- lang: eng
text: An n-vertex graph is called C-Ramsey if it has no clique or independent set
of size Clogn. All known constructions of Ramsey graphs involve randomness in
an essential way, and there is an ongoing line of research towards showing that
in fact all Ramsey graphs must obey certain “richness” properties characteristic
of random graphs. Motivated by an old problem of Erd̋s and McKay, recently Narayanan,
Sahasrabudhe, and Tomon conjectured that for any fixed C, every n-vertex C-Ramsey
graph induces subgraphs of Θ(n2) different sizes. In this paper we prove this
conjecture.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Matthew Alan
full_name: Kwan, Matthew Alan
id: 5fca0887-a1db-11eb-95d1-ca9d5e0453b3
last_name: Kwan
orcid: 0000-0002-4003-7567
- first_name: Benny
full_name: Sudakov, Benny
last_name: Sudakov
citation:
ama: Kwan MA, Sudakov B. Ramsey graphs induce subgraphs of quadratically many sizes.
International Mathematics Research Notices. 2020;2020(6):1621–1638. doi:10.1093/imrn/rny064
apa: Kwan, M. A., & Sudakov, B. (2020). Ramsey graphs induce subgraphs of quadratically
many sizes. International Mathematics Research Notices. Oxford University
Press. https://doi.org/10.1093/imrn/rny064
chicago: Kwan, Matthew Alan, and Benny Sudakov. “Ramsey Graphs Induce Subgraphs
of Quadratically Many Sizes.” International Mathematics Research Notices.
Oxford University Press, 2020. https://doi.org/10.1093/imrn/rny064.
ieee: M. A. Kwan and B. Sudakov, “Ramsey graphs induce subgraphs of quadratically
many sizes,” International Mathematics Research Notices, vol. 2020, no.
6. Oxford University Press, pp. 1621–1638, 2020.
ista: Kwan MA, Sudakov B. 2020. Ramsey graphs induce subgraphs of quadratically
many sizes. International Mathematics Research Notices. 2020(6), 1621–1638.
mla: Kwan, Matthew Alan, and Benny Sudakov. “Ramsey Graphs Induce Subgraphs of Quadratically
Many Sizes.” International Mathematics Research Notices, vol. 2020, no.
6, Oxford University Press, 2020, pp. 1621–1638, doi:10.1093/imrn/rny064.
short: M.A. Kwan, B. Sudakov, International Mathematics Research Notices 2020 (2020)
1621–1638.
date_created: 2021-06-21T08:30:12Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-02-23T14:01:33Z
day: '01'
doi: 10.1093/imrn/rny064
extern: '1'
external_id:
arxiv:
- '1711.02937'
intvolume: ' 2020'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1093/imrn/rny064
month: '03'
oa: 1
oa_version: Published Version
page: 1621–1638
publication: International Mathematics Research Notices
publication_identifier:
eissn:
- 1687-0247
issn:
- 1073-7928
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Ramsey graphs induce subgraphs of quadratically many sizes
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 2020
year: '2020'
...
---
_id: '9578'
abstract:
- lang: eng
text: How long a monotone path can one always find in any edge-ordering of the complete
graph Kn? This appealing question was first asked by Chvátal and Komlós in 1971,
and has since attracted the attention of many researchers, inspiring a variety
of related problems. The prevailing conjecture is that one can always find a monotone
path of linear length, but until now the best known lower bound was n2/3-o(1).
In this paper we almost close this gap, proving that any edge-ordering of the
complete graph contains a monotone path of length n1-o(1).
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Matija
full_name: Bucić, Matija
last_name: Bucić
- first_name: Matthew Alan
full_name: Kwan, Matthew Alan
id: 5fca0887-a1db-11eb-95d1-ca9d5e0453b3
last_name: Kwan
orcid: 0000-0002-4003-7567
- first_name: Alexey
full_name: Pokrovskiy, Alexey
last_name: Pokrovskiy
- first_name: Benny
full_name: Sudakov, Benny
last_name: Sudakov
- first_name: Tuan
full_name: Tran, Tuan
last_name: Tran
- first_name: Adam Zsolt
full_name: Wagner, Adam Zsolt
last_name: Wagner
citation:
ama: Bucić M, Kwan MA, Pokrovskiy A, Sudakov B, Tran T, Wagner AZ. Nearly-linear
monotone paths in edge-ordered graphs. Israel Journal of Mathematics. 2020;238(2):663-685.
doi:10.1007/s11856-020-2035-7
apa: Bucić, M., Kwan, M. A., Pokrovskiy, A., Sudakov, B., Tran, T., & Wagner,
A. Z. (2020). Nearly-linear monotone paths in edge-ordered graphs. Israel Journal
of Mathematics. Springer. https://doi.org/10.1007/s11856-020-2035-7
chicago: Bucić, Matija, Matthew Alan Kwan, Alexey Pokrovskiy, Benny Sudakov, Tuan
Tran, and Adam Zsolt Wagner. “Nearly-Linear Monotone Paths in Edge-Ordered Graphs.”
Israel Journal of Mathematics. Springer, 2020. https://doi.org/10.1007/s11856-020-2035-7.
ieee: M. Bucić, M. A. Kwan, A. Pokrovskiy, B. Sudakov, T. Tran, and A. Z. Wagner,
“Nearly-linear monotone paths in edge-ordered graphs,” Israel Journal of Mathematics,
vol. 238, no. 2. Springer, pp. 663–685, 2020.
ista: Bucić M, Kwan MA, Pokrovskiy A, Sudakov B, Tran T, Wagner AZ. 2020. Nearly-linear
monotone paths in edge-ordered graphs. Israel Journal of Mathematics. 238(2),
663–685.
mla: Bucić, Matija, et al. “Nearly-Linear Monotone Paths in Edge-Ordered Graphs.”
Israel Journal of Mathematics, vol. 238, no. 2, Springer, 2020, pp. 663–85,
doi:10.1007/s11856-020-2035-7.
short: M. Bucić, M.A. Kwan, A. Pokrovskiy, B. Sudakov, T. Tran, A.Z. Wagner, Israel
Journal of Mathematics 238 (2020) 663–685.
date_created: 2021-06-21T13:24:35Z
date_published: 2020-07-01T00:00:00Z
date_updated: 2023-02-23T14:01:35Z
day: '01'
doi: 10.1007/s11856-020-2035-7
extern: '1'
external_id:
arxiv:
- '1809.01468'
intvolume: ' 238'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1809.01468
month: '07'
oa: 1
oa_version: Preprint
page: 663-685
publication: Israel Journal of Mathematics
publication_identifier:
eissn:
- 1565-8511
issn:
- 0021-2172
publication_status: published
publisher: Springer
quality_controlled: '1'
scopus_import: '1'
status: public
title: Nearly-linear monotone paths in edge-ordered graphs
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 238
year: '2020'
...
---
_id: '9581'
abstract:
- lang: eng
text: "We show that for any \U0001D45B divisible by 3, almost all order- \U0001D45B
\ Steiner triple systems have a perfect matching (also known as a parallel class
or resolution class). In fact, we prove a general upper bound on the number of
perfect matchings in a Steiner triple system and show that almost all Steiner
triple systems essentially attain this maximum. We accomplish this via a general
theorem comparing a uniformly random Steiner triple system to the outcome of the
triangle removal process, which we hope will be useful for other problems. Our
methods can also be adapted to other types of designs; for example, we sketch
a proof of the theorem that almost all Latin squares have transversals."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- 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: Kwan MA. Almost all Steiner triple systems have perfect matchings. Proceedings
of the London Mathematical Society. 2020;121(6):1468-1495. doi:10.1112/plms.12373
apa: Kwan, M. A. (2020). Almost all Steiner triple systems have perfect matchings.
Proceedings of the London Mathematical Society. Wiley. https://doi.org/10.1112/plms.12373
chicago: Kwan, Matthew Alan. “Almost All Steiner Triple Systems Have Perfect Matchings.”
Proceedings of the London Mathematical Society. Wiley, 2020. https://doi.org/10.1112/plms.12373.
ieee: M. A. Kwan, “Almost all Steiner triple systems have perfect matchings,” Proceedings
of the London Mathematical Society, vol. 121, no. 6. Wiley, pp. 1468–1495,
2020.
ista: Kwan MA. 2020. Almost all Steiner triple systems have perfect matchings. Proceedings
of the London Mathematical Society. 121(6), 1468–1495.
mla: Kwan, Matthew Alan. “Almost All Steiner Triple Systems Have Perfect Matchings.”
Proceedings of the London Mathematical Society, vol. 121, no. 6, Wiley,
2020, pp. 1468–95, doi:10.1112/plms.12373.
short: M.A. Kwan, Proceedings of the London Mathematical Society 121 (2020) 1468–1495.
date_created: 2021-06-22T06:35:16Z
date_published: 2020-12-01T00:00:00Z
date_updated: 2023-02-23T14:01:43Z
day: '01'
doi: 10.1112/plms.12373
extern: '1'
external_id:
arxiv:
- '1611.02246'
intvolume: ' 121'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1611.02246
month: '12'
oa: 1
oa_version: Preprint
page: 1468-1495
publication: Proceedings of the London Mathematical Society
publication_identifier:
eissn:
- 1460-244X
issn:
- 0024-6115
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Almost all Steiner triple systems have perfect matchings
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 121
year: '2020'
...
---
_id: '9582'
abstract:
- lang: eng
text: The problem of finding dense induced bipartite subgraphs in H-free graphs
has a long history, and was posed 30 years ago by Erdős, Faudree, Pach and Spencer.
In this paper, we obtain several results in this direction. First we prove that
any H-free graph with minimum degree at least d contains an induced bipartite
subgraph of minimum degree at least cH log d/log log d, thus nearly confirming
one and proving another conjecture of Esperet, Kang and Thomassé. Complementing
this result, we further obtain optimal bounds for this problem in the case of
dense triangle-free graphs, and we also answer a question of Erdœs, Janson, Łuczak
and Spencer.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Matthew Alan
full_name: Kwan, Matthew Alan
id: 5fca0887-a1db-11eb-95d1-ca9d5e0453b3
last_name: Kwan
orcid: 0000-0002-4003-7567
- first_name: Shoham
full_name: Letzter, Shoham
last_name: Letzter
- first_name: Benny
full_name: Sudakov, Benny
last_name: Sudakov
- first_name: Tuan
full_name: Tran, Tuan
last_name: Tran
citation:
ama: Kwan MA, Letzter S, Sudakov B, Tran T. Dense induced bipartite subgraphs in
triangle-free graphs. Combinatorica. 2020;40(2):283-305. doi:10.1007/s00493-019-4086-0
apa: Kwan, M. A., Letzter, S., Sudakov, B., & Tran, T. (2020). Dense induced
bipartite subgraphs in triangle-free graphs. Combinatorica. Springer. https://doi.org/10.1007/s00493-019-4086-0
chicago: Kwan, Matthew Alan, Shoham Letzter, Benny Sudakov, and Tuan Tran. “Dense
Induced Bipartite Subgraphs in Triangle-Free Graphs.” Combinatorica. Springer,
2020. https://doi.org/10.1007/s00493-019-4086-0.
ieee: M. A. Kwan, S. Letzter, B. Sudakov, and T. Tran, “Dense induced bipartite
subgraphs in triangle-free graphs,” Combinatorica, vol. 40, no. 2. Springer,
pp. 283–305, 2020.
ista: Kwan MA, Letzter S, Sudakov B, Tran T. 2020. Dense induced bipartite subgraphs
in triangle-free graphs. Combinatorica. 40(2), 283–305.
mla: Kwan, Matthew Alan, et al. “Dense Induced Bipartite Subgraphs in Triangle-Free
Graphs.” Combinatorica, vol. 40, no. 2, Springer, 2020, pp. 283–305, doi:10.1007/s00493-019-4086-0.
short: M.A. Kwan, S. Letzter, B. Sudakov, T. Tran, Combinatorica 40 (2020) 283–305.
date_created: 2021-06-22T06:42:26Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2023-02-23T14:01:45Z
day: '01'
doi: 10.1007/s00493-019-4086-0
extern: '1'
external_id:
arxiv:
- '1810.12144'
intvolume: ' 40'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1810.12144
month: '04'
oa: 1
oa_version: Preprint
page: 283-305
publication: Combinatorica
publication_identifier:
eissn:
- 1439-6912
issn:
- 0209-9683
publication_status: published
publisher: Springer
quality_controlled: '1'
scopus_import: '1'
status: public
title: Dense induced bipartite subgraphs in triangle-free graphs
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 40
year: '2020'
...
---
_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: 2023-02-23T14:01:48Z
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: 6785fbc1-c503-11eb-8a32-93094b40e1cf
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
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
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creator: asandaue
date_created: 2021-08-11T11:55:11Z
date_updated: 2021-08-11T11:55:11Z
file_id: '9882'
file_name: 2020_JournalOfComputationalGeometry_Edelsbrunner.pdf
file_size: 1449234
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success: 1
file_date_updated: 2021-08-11T11:55:11Z
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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: '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'
...
---
_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: 2023-02-23T14:04:16Z
day: '14'
ddc:
- '530'
doi: 10.1039/d0cp02513e
extern: '1'
external_id:
arxiv:
- '1909.08934'
pmid:
- '32459228'
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file_size: 3151206
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file_date_updated: 2021-07-15T12:43:51Z
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intvolume: ' 22'
issue: '22'
language:
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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
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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
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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'
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checksum: 1edd9b6d8fa791f8094d87bd6453955b
content_type: application/pdf
creator: asandaue
date_created: 2021-07-15T14:05:45Z
date_updated: 2021-07-15T14:05:45Z
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file_name: 2020_NatureCommunications_Monserrat.pdf
file_size: 1385954
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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'
...