---
_id: '8940'
abstract:
- lang: eng
text: We quantise Whitney’s construction to prove the existence of a triangulation
for any C^2 manifold, so that we get an algorithm with explicit bounds. We also
give a new elementary proof, which is completely geometric.
acknowledgement: This work has been funded by the European Research Council under
the European Union’s ERC Grant Agreement Number 339025 GUDHI (Algorithmic Foundations
of Geometric Understanding in Higher Dimensions). The third author also received
funding from the European Union’s Horizon 2020 research and innovation programme
under the Marie Skłodowska-Curie Grant Agreement No. 754411. Open access funding
provided by the Institute of Science and Technology (IST Austria).
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Jean-Daniel
full_name: Boissonnat, Jean-Daniel
last_name: Boissonnat
- first_name: Siargey
full_name: Kachanovich, Siargey
last_name: Kachanovich
- first_name: Mathijs
full_name: Wintraecken, Mathijs
id: 307CFBC8-F248-11E8-B48F-1D18A9856A87
last_name: Wintraecken
orcid: 0000-0002-7472-2220
citation:
ama: 'Boissonnat J-D, Kachanovich S, Wintraecken M. Triangulating submanifolds:
An elementary and quantified version of Whitney’s method. Discrete & Computational
Geometry. 2021;66(1):386-434. doi:10.1007/s00454-020-00250-8'
apa: 'Boissonnat, J.-D., Kachanovich, S., & Wintraecken, M. (2021). Triangulating
submanifolds: An elementary and quantified version of Whitney’s method. Discrete
& Computational Geometry. Springer Nature. https://doi.org/10.1007/s00454-020-00250-8'
chicago: 'Boissonnat, Jean-Daniel, Siargey Kachanovich, and Mathijs Wintraecken.
“Triangulating Submanifolds: An Elementary and Quantified Version of Whitney’s
Method.” Discrete & Computational Geometry. Springer Nature, 2021.
https://doi.org/10.1007/s00454-020-00250-8.'
ieee: 'J.-D. Boissonnat, S. Kachanovich, and M. Wintraecken, “Triangulating submanifolds:
An elementary and quantified version of Whitney’s method,” Discrete & Computational
Geometry, vol. 66, no. 1. Springer Nature, pp. 386–434, 2021.'
ista: 'Boissonnat J-D, Kachanovich S, Wintraecken M. 2021. Triangulating submanifolds:
An elementary and quantified version of Whitney’s method. Discrete & Computational
Geometry. 66(1), 386–434.'
mla: 'Boissonnat, Jean-Daniel, et al. “Triangulating Submanifolds: An Elementary
and Quantified Version of Whitney’s Method.” Discrete & Computational Geometry,
vol. 66, no. 1, Springer Nature, 2021, pp. 386–434, doi:10.1007/s00454-020-00250-8.'
short: J.-D. Boissonnat, S. Kachanovich, M. Wintraecken, Discrete & Computational
Geometry 66 (2021) 386–434.
date_created: 2020-12-12T11:07:02Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2023-09-05T15:02:40Z
day: '01'
ddc:
- '516'
department:
- _id: HeEd
doi: 10.1007/s00454-020-00250-8
ec_funded: 1
external_id:
isi:
- '000597770300001'
file:
- access_level: open_access
checksum: c848986091e56699dc12de85adb1e39c
content_type: application/pdf
creator: kschuh
date_created: 2021-08-06T09:52:29Z
date_updated: 2021-08-06T09:52:29Z
file_id: '9795'
file_name: 2021_DescreteCompGeopmetry_Boissonnat.pdf
file_size: 983307
relation: main_file
success: 1
file_date_updated: 2021-08-06T09:52:29Z
has_accepted_license: '1'
intvolume: ' 66'
isi: 1
issue: '1'
keyword:
- Theoretical Computer Science
- Computational Theory and Mathematics
- Geometry and Topology
- Discrete Mathematics and Combinatorics
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 386-434
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Discrete & Computational Geometry
publication_identifier:
eissn:
- 1432-0444
issn:
- 0179-5376
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: 'Triangulating submanifolds: An elementary and quantified version of Whitney’s
method'
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: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 66
year: '2021'
...
---
_id: '10002'
abstract:
- lang: eng
text: 'We present a faster symbolic algorithm for the following central problem
in probabilistic verification: Compute the maximal end-component (MEC) decomposition
of Markov decision processes (MDPs). This problem generalizes the SCC decomposition
problem of graphs and closed recurrent sets of Markov chains. The model of symbolic
algorithms is widely used in formal verification and model-checking, where access
to the input model is restricted to only symbolic operations (e.g., basic set
operations and computation of one-step neighborhood). For an input MDP with n vertices
and m edges, the classical symbolic algorithm from the 1990s for the MEC decomposition
requires O(n2) symbolic operations and O(1) symbolic space. The only other
symbolic algorithm for the MEC decomposition requires O(nm−−√) symbolic operations
and O(m−−√) symbolic space. A main open question is whether the worst-case O(n2) bound
for symbolic operations can be beaten. We present a symbolic algorithm that requires O˜(n1.5) symbolic
operations and O˜(n−−√) symbolic space. Moreover, the parametrization of our
algorithm provides a trade-off between symbolic operations and symbolic space:
for all 0<ϵ≤1/2 the symbolic algorithm requires O˜(n2−ϵ) symbolic operations
and O˜(nϵ) symbolic space ( O˜ hides poly-logarithmic factors). Using our techniques
we present faster algorithms for computing the almost-sure winning regions of ω
-regular objectives for MDPs. We consider the canonical parity objectives for ω
-regular objectives, and for parity objectives with d -priorities we present
an algorithm that computes the almost-sure winning region with O˜(n2−ϵ) symbolic
operations and O˜(nϵ) symbolic space, for all 0<ϵ≤1/2 .'
acknowledgement: The authors are grateful to the anonymous referees for their valuable
comments. A. S. is fully supported by the Vienna Science and Technology Fund (WWTF)
through project ICT15–003. K. C. is supported by the Austrian Science Fund (FWF)
NFN Grant No S11407-N23 (RiSE/SHiNE) and by the ERC CoG 863818 (ForM-SMArt). For
M. H. the research leading to these results has received funding from the European
Research Council under the European Unions Seventh Framework Programme (FP/2007–2013)
/ ERC Grant Agreement no. 340506.
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Wolfgang
full_name: Dvorak, Wolfgang
last_name: Dvorak
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Alexander
full_name: Svozil, Alexander
last_name: Svozil
citation:
ama: 'Chatterjee K, Dvorak W, Henzinger MH, Svozil A. Symbolic time and space tradeoffs
for probabilistic verification. In: Proceedings of the 36th Annual ACM/IEEE
Symposium on Logic in Computer Science. Institute of Electrical and Electronics
Engineers; 2021:1-13. doi:10.1109/LICS52264.2021.9470739'
apa: 'Chatterjee, K., Dvorak, W., Henzinger, M. H., & Svozil, A. (2021). Symbolic
time and space tradeoffs for probabilistic verification. In Proceedings of
the 36th Annual ACM/IEEE Symposium on Logic in Computer Science (pp. 1–13).
Rome, Italy: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/LICS52264.2021.9470739'
chicago: Chatterjee, Krishnendu, Wolfgang Dvorak, Monika H Henzinger, and Alexander
Svozil. “Symbolic Time and Space Tradeoffs for Probabilistic Verification.” In
Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science,
1–13. Institute of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/LICS52264.2021.9470739.
ieee: K. Chatterjee, W. Dvorak, M. H. Henzinger, and A. Svozil, “Symbolic time and
space tradeoffs for probabilistic verification,” in Proceedings of the 36th
Annual ACM/IEEE Symposium on Logic in Computer Science, Rome, Italy, 2021,
pp. 1–13.
ista: 'Chatterjee K, Dvorak W, Henzinger MH, Svozil A. 2021. Symbolic time and space
tradeoffs for probabilistic verification. Proceedings of the 36th Annual ACM/IEEE
Symposium on Logic in Computer Science. LICS: Symposium on Logic in Computer Science,
1–13.'
mla: Chatterjee, Krishnendu, et al. “Symbolic Time and Space Tradeoffs for Probabilistic
Verification.” Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in
Computer Science, Institute of Electrical and Electronics Engineers, 2021,
pp. 1–13, doi:10.1109/LICS52264.2021.9470739.
short: K. Chatterjee, W. Dvorak, M.H. Henzinger, A. Svozil, in:, Proceedings of
the 36th Annual ACM/IEEE Symposium on Logic in Computer Science, Institute of
Electrical and Electronics Engineers, 2021, pp. 1–13.
conference:
end_date: 2021-07-02
location: Rome, Italy
name: 'LICS: Symposium on Logic in Computer Science'
start_date: 2021-06-29
date_created: 2021-09-12T22:01:24Z
date_published: 2021-07-07T00:00:00Z
date_updated: 2025-07-14T09:10:07Z
day: '07'
department:
- _id: KrCh
doi: 10.1109/LICS52264.2021.9470739
ec_funded: 1
external_id:
arxiv:
- '2104.07466'
isi:
- '000947350400089'
isi: 1
keyword:
- Computer science
- Computational modeling
- Markov processes
- Probabilistic logic
- Formal verification
- Game Theory
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2104.07466
month: '07'
oa: 1
oa_version: Preprint
page: 1-13
project:
- _id: 25863FF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11407
name: Game Theory
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer
Science
publication_identifier:
eisbn:
- 978-1-6654-4895-6
isbn:
- 978-1-6654-4896-3
issn:
- 1043-6871
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Symbolic time and space tradeoffs for probabilistic verification
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '10004'
abstract:
- lang: eng
text: 'Markov chains are the de facto finite-state model for stochastic dynamical
systems, and Markov decision processes (MDPs) extend Markov chains by incorporating
non-deterministic behaviors. Given an MDP and rewards on states, a classical optimization
criterion is the maximal expected total reward where the MDP stops after T steps,
which can be computed by a simple dynamic programming algorithm. We consider a
natural generalization of the problem where the stopping times can be chosen according
to a probability distribution, such that the expected stopping time is T, to optimize
the expected total reward. Quite surprisingly we establish inter-reducibility
of the expected stopping-time problem for Markov chains with the Positivity problem
(which is related to the well-known Skolem problem), for which establishing either
decidability or undecidability would be a major breakthrough. Given the hardness
of the exact problem, we consider the approximate version of the problem: we show
that it can be solved in exponential time for Markov chains and in exponential
space for MDPs.'
acknowledgement: We are grateful to the anonymous reviewers of LICS 2021 and of a
previous version of this paper for insightful comments that helped improving the
presentation. This research was partially supported by the grant ERC CoG 863818
(ForM-SMArt).
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Laurent
full_name: Doyen, Laurent
last_name: Doyen
citation:
ama: 'Chatterjee K, Doyen L. Stochastic processes with expected stopping time. In:
Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science.
Institute of Electrical and Electronics Engineers; 2021:1-13. doi:10.1109/LICS52264.2021.9470595'
apa: 'Chatterjee, K., & Doyen, L. (2021). Stochastic processes with expected
stopping time. In Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science (pp. 1–13). Rome, Italy: Institute of Electrical and Electronics
Engineers. https://doi.org/10.1109/LICS52264.2021.9470595'
chicago: Chatterjee, Krishnendu, and Laurent Doyen. “Stochastic Processes with Expected
Stopping Time.” In Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science, 1–13. Institute of Electrical and Electronics Engineers,
2021. https://doi.org/10.1109/LICS52264.2021.9470595.
ieee: K. Chatterjee and L. Doyen, “Stochastic processes with expected stopping time,”
in Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science,
Rome, Italy, 2021, pp. 1–13.
ista: 'Chatterjee K, Doyen L. 2021. Stochastic processes with expected stopping
time. Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science.
LICS: Symposium on Logic in Computer Science, 1–13.'
mla: Chatterjee, Krishnendu, and Laurent Doyen. “Stochastic Processes with Expected
Stopping Time.” Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science, Institute of Electrical and Electronics Engineers, 2021,
pp. 1–13, doi:10.1109/LICS52264.2021.9470595.
short: K. Chatterjee, L. Doyen, in:, Proceedings of the 36th Annual ACM/IEEE Symposium
on Logic in Computer Science, Institute of Electrical and Electronics Engineers,
2021, pp. 1–13.
conference:
end_date: 2021-07-02
location: Rome, Italy
name: 'LICS: Symposium on Logic in Computer Science'
start_date: 2021-06-29
date_created: 2021-09-12T22:01:25Z
date_published: 2021-07-07T00:00:00Z
date_updated: 2025-07-14T09:10:08Z
day: '07'
department:
- _id: KrCh
doi: 10.1109/LICS52264.2021.9470595
ec_funded: 1
external_id:
arxiv:
- '2104.07278'
isi:
- '000947350400036'
isi: 1
keyword:
- Computer science
- Heuristic algorithms
- Memory management
- Automata
- Markov processes
- Probability distribution
- Complexity theory
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2104.07278
month: '07'
oa: 1
oa_version: Preprint
page: 1-13
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer
Science
publication_identifier:
eisbn:
- 978-1-6654-4895-6
isbn:
- 978-1-6654-4896-3
issn:
- 1043-6871
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Stochastic processes with expected stopping time
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '14125'
abstract:
- lang: eng
text: "Motivation: Recent technological advances have led to an increase in the
production and availability of single-cell data. The ability to integrate a set
of multi-technology measurements would allow the identification of biologically
or clinically meaningful observations through the unification of the perspectives
afforded by each technology. In most cases, however, profiling technologies consume
the used cells and thus pairwise correspondences between datasets are lost. Due
to the sheer size single-cell datasets can acquire, scalable algorithms that are
able to universally match single-cell measurements carried out in one cell to
its corresponding sibling in another technology are needed.\r\nResults: We propose
Single-Cell data Integration via Matching (SCIM), a scalable approach to recover
such correspondences in two or more technologies. SCIM assumes that cells share
a common (low-dimensional) underlying structure and that the underlying cell distribution
is approximately constant across technologies. It constructs a technology-invariant
latent space using an autoencoder framework with an adversarial objective. Multi-modal
datasets are integrated by pairing cells across technologies using a bipartite
matching scheme that operates on the low-dimensional latent representations. We
evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell
matches derived by SCIM reflect the same pseudotime on the simulated dataset.
Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample
and a human bone marrow sample, where we pair cells from a scRNA dataset to their
sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy
for each one of the samples, respectively."
article_processing_charge: No
article_type: original
author:
- first_name: Stefan G
full_name: Stark, Stefan G
last_name: Stark
- first_name: Joanna
full_name: Ficek, Joanna
last_name: Ficek
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Ximena
full_name: Bonilla, Ximena
last_name: Bonilla
- first_name: Stéphane
full_name: Chevrier, Stéphane
last_name: Chevrier
- first_name: Franziska
full_name: Singer, Franziska
last_name: Singer
- first_name: Rudolf
full_name: Aebersold, Rudolf
last_name: Aebersold
- first_name: Faisal S
full_name: Al-Quaddoomi, Faisal S
last_name: Al-Quaddoomi
- first_name: Jonas
full_name: Albinus, Jonas
last_name: Albinus
- first_name: Ilaria
full_name: Alborelli, Ilaria
last_name: Alborelli
- first_name: Sonali
full_name: Andani, Sonali
last_name: Andani
- first_name: Per-Olof
full_name: Attinger, Per-Olof
last_name: Attinger
- first_name: Marina
full_name: Bacac, Marina
last_name: Bacac
- first_name: Daniel
full_name: Baumhoer, Daniel
last_name: Baumhoer
- first_name: Beatrice
full_name: Beck-Schimmer, Beatrice
last_name: Beck-Schimmer
- first_name: Niko
full_name: Beerenwinkel, Niko
last_name: Beerenwinkel
- first_name: Christian
full_name: Beisel, Christian
last_name: Beisel
- first_name: Lara
full_name: Bernasconi, Lara
last_name: Bernasconi
- first_name: Anne
full_name: Bertolini, Anne
last_name: Bertolini
- first_name: Bernd
full_name: Bodenmiller, Bernd
last_name: Bodenmiller
- first_name: Ximena
full_name: Bonilla, Ximena
last_name: Bonilla
- first_name: Ruben
full_name: Casanova, Ruben
last_name: Casanova
- first_name: Stéphane
full_name: Chevrier, Stéphane
last_name: Chevrier
- first_name: Natalia
full_name: Chicherova, Natalia
last_name: Chicherova
- first_name: Maya
full_name: D'Costa, Maya
last_name: D'Costa
- first_name: Esther
full_name: Danenberg, Esther
last_name: Danenberg
- first_name: Natalie
full_name: Davidson, Natalie
last_name: Davidson
- first_name: Monica-Andreea Dră
full_name: gan, Monica-Andreea Dră
last_name: gan
- first_name: Reinhard
full_name: Dummer, Reinhard
last_name: Dummer
- first_name: Stefanie
full_name: Engler, Stefanie
last_name: Engler
- first_name: Martin
full_name: Erkens, Martin
last_name: Erkens
- first_name: Katja
full_name: Eschbach, Katja
last_name: Eschbach
- first_name: Cinzia
full_name: Esposito, Cinzia
last_name: Esposito
- first_name: André
full_name: Fedier, André
last_name: Fedier
- first_name: Pedro
full_name: Ferreira, Pedro
last_name: Ferreira
- first_name: Joanna
full_name: Ficek, Joanna
last_name: Ficek
- first_name: Anja L
full_name: Frei, Anja L
last_name: Frei
- first_name: Bruno
full_name: Frey, Bruno
last_name: Frey
- first_name: Sandra
full_name: Goetze, Sandra
last_name: Goetze
- first_name: Linda
full_name: Grob, Linda
last_name: Grob
- first_name: Gabriele
full_name: Gut, Gabriele
last_name: Gut
- first_name: Detlef
full_name: Günther, Detlef
last_name: Günther
- first_name: Martina
full_name: Haberecker, Martina
last_name: Haberecker
- first_name: Pirmin
full_name: Haeuptle, Pirmin
last_name: Haeuptle
- first_name: Viola
full_name: Heinzelmann-Schwarz, Viola
last_name: Heinzelmann-Schwarz
- first_name: Sylvia
full_name: Herter, Sylvia
last_name: Herter
- first_name: Rene
full_name: Holtackers, Rene
last_name: Holtackers
- first_name: Tamara
full_name: Huesser, Tamara
last_name: Huesser
- first_name: Anja
full_name: Irmisch, Anja
last_name: Irmisch
- first_name: Francis
full_name: Jacob, Francis
last_name: Jacob
- first_name: Andrea
full_name: Jacobs, Andrea
last_name: Jacobs
- first_name: Tim M
full_name: Jaeger, Tim M
last_name: Jaeger
- first_name: Katharina
full_name: Jahn, Katharina
last_name: Jahn
- first_name: Alva R
full_name: James, Alva R
last_name: James
- first_name: Philip M
full_name: Jermann, Philip M
last_name: Jermann
- first_name: André
full_name: Kahles, André
last_name: Kahles
- first_name: Abdullah
full_name: Kahraman, Abdullah
last_name: Kahraman
- first_name: Viktor H
full_name: Koelzer, Viktor H
last_name: Koelzer
- first_name: Werner
full_name: Kuebler, Werner
last_name: Kuebler
- first_name: Jack
full_name: Kuipers, Jack
last_name: Kuipers
- first_name: Christian P
full_name: Kunze, Christian P
last_name: Kunze
- first_name: Christian
full_name: Kurzeder, Christian
last_name: Kurzeder
- first_name: Kjong-Van
full_name: Lehmann, Kjong-Van
last_name: Lehmann
- first_name: Mitchell
full_name: Levesque, Mitchell
last_name: Levesque
- first_name: Sebastian
full_name: Lugert, Sebastian
last_name: Lugert
- first_name: Gerd
full_name: Maass, Gerd
last_name: Maass
- first_name: Markus
full_name: Manz, Markus
last_name: Manz
- first_name: Philipp
full_name: Markolin, Philipp
last_name: Markolin
- first_name: Julien
full_name: Mena, Julien
last_name: Mena
- first_name: Ulrike
full_name: Menzel, Ulrike
last_name: Menzel
- first_name: Julian M
full_name: Metzler, Julian M
last_name: Metzler
- first_name: Nicola
full_name: Miglino, Nicola
last_name: Miglino
- first_name: Emanuela S
full_name: Milani, Emanuela S
last_name: Milani
- first_name: Holger
full_name: Moch, Holger
last_name: Moch
- first_name: Simone
full_name: Muenst, Simone
last_name: Muenst
- first_name: Riccardo
full_name: Murri, Riccardo
last_name: Murri
- first_name: Charlotte KY
full_name: Ng, Charlotte KY
last_name: Ng
- first_name: Stefan
full_name: Nicolet, Stefan
last_name: Nicolet
- first_name: Marta
full_name: Nowak, Marta
last_name: Nowak
- first_name: Patrick GA
full_name: Pedrioli, Patrick GA
last_name: Pedrioli
- first_name: Lucas
full_name: Pelkmans, Lucas
last_name: Pelkmans
- first_name: Salvatore
full_name: Piscuoglio, Salvatore
last_name: Piscuoglio
- first_name: Michael
full_name: Prummer, Michael
last_name: Prummer
- first_name: Mathilde
full_name: Ritter, Mathilde
last_name: Ritter
- first_name: Christian
full_name: Rommel, Christian
last_name: Rommel
- first_name: María L
full_name: Rosano-González, María L
last_name: Rosano-González
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
- first_name: Natascha
full_name: Santacroce, Natascha
last_name: Santacroce
- first_name: Jacobo Sarabia del
full_name: Castillo, Jacobo Sarabia del
last_name: Castillo
- first_name: Ramona
full_name: Schlenker, Ramona
last_name: Schlenker
- first_name: Petra C
full_name: Schwalie, Petra C
last_name: Schwalie
- first_name: Severin
full_name: Schwan, Severin
last_name: Schwan
- first_name: Tobias
full_name: Schär, Tobias
last_name: Schär
- first_name: Gabriela
full_name: Senti, Gabriela
last_name: Senti
- first_name: Franziska
full_name: Singer, Franziska
last_name: Singer
- first_name: Sujana
full_name: Sivapatham, Sujana
last_name: Sivapatham
- first_name: Berend
full_name: Snijder, Berend
last_name: Snijder
- first_name: Bettina
full_name: Sobottka, Bettina
last_name: Sobottka
- first_name: Vipin T
full_name: Sreedharan, Vipin T
last_name: Sreedharan
- first_name: Stefan
full_name: Stark, Stefan
last_name: Stark
- first_name: Daniel J
full_name: Stekhoven, Daniel J
last_name: Stekhoven
- first_name: Alexandre PA
full_name: Theocharides, Alexandre PA
last_name: Theocharides
- first_name: Tinu M
full_name: Thomas, Tinu M
last_name: Thomas
- first_name: Markus
full_name: Tolnay, Markus
last_name: Tolnay
- first_name: Vinko
full_name: Tosevski, Vinko
last_name: Tosevski
- first_name: Nora C
full_name: Toussaint, Nora C
last_name: Toussaint
- first_name: Mustafa A
full_name: Tuncel, Mustafa A
last_name: Tuncel
- first_name: Marina
full_name: Tusup, Marina
last_name: Tusup
- first_name: Audrey Van
full_name: Drogen, Audrey Van
last_name: Drogen
- first_name: Marcus
full_name: Vetter, Marcus
last_name: Vetter
- first_name: Tatjana
full_name: Vlajnic, Tatjana
last_name: Vlajnic
- first_name: Sandra
full_name: Weber, Sandra
last_name: Weber
- first_name: Walter P
full_name: Weber, Walter P
last_name: Weber
- first_name: Rebekka
full_name: Wegmann, Rebekka
last_name: Wegmann
- first_name: Michael
full_name: Weller, Michael
last_name: Weller
- first_name: Fabian
full_name: Wendt, Fabian
last_name: Wendt
- first_name: Norbert
full_name: Wey, Norbert
last_name: Wey
- first_name: Andreas
full_name: Wicki, Andreas
last_name: Wicki
- first_name: Bernd
full_name: Wollscheid, Bernd
last_name: Wollscheid
- first_name: Shuqing
full_name: Yu, Shuqing
last_name: Yu
- first_name: Johanna
full_name: Ziegler, Johanna
last_name: Ziegler
- first_name: Marc
full_name: Zimmermann, Marc
last_name: Zimmermann
- first_name: Martin
full_name: Zoche, Martin
last_name: Zoche
- first_name: Gregor
full_name: Zuend, Gregor
last_name: Zuend
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
- first_name: Kjong-Van
full_name: Lehmann, Kjong-Van
last_name: Lehmann
citation:
ama: 'Stark SG, Ficek J, Locatello F, et al. SCIM: Universal single-cell matching
with unpaired feature sets. Bioinformatics. 2020;36(Supplement_2):i919-i927.
doi:10.1093/bioinformatics/btaa843'
apa: 'Stark, S. G., Ficek, J., Locatello, F., Bonilla, X., Chevrier, S., Singer,
F., … Lehmann, K.-V. (2020). SCIM: Universal single-cell matching with unpaired
feature sets. Bioinformatics. Oxford University Press. https://doi.org/10.1093/bioinformatics/btaa843'
chicago: 'Stark, Stefan G, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane
Chevrier, Franziska Singer, Rudolf Aebersold, et al. “SCIM: Universal Single-Cell
Matching with Unpaired Feature Sets.” Bioinformatics. Oxford University
Press, 2020. https://doi.org/10.1093/bioinformatics/btaa843.'
ieee: 'S. G. Stark et al., “SCIM: Universal single-cell matching with unpaired
feature sets,” Bioinformatics, vol. 36, no. Supplement_2. Oxford University
Press, pp. i919–i927, 2020.'
ista: 'Stark SG et al. 2020. SCIM: Universal single-cell matching with unpaired
feature sets. Bioinformatics. 36(Supplement_2), i919–i927.'
mla: 'Stark, Stefan G., et al. “SCIM: Universal Single-Cell Matching with Unpaired
Feature Sets.” Bioinformatics, vol. 36, no. Supplement_2, Oxford University
Press, 2020, pp. i919–27, doi:10.1093/bioinformatics/btaa843.'
short: S.G. Stark, J. Ficek, F. Locatello, X. Bonilla, S. Chevrier, F. Singer, R.
Aebersold, F.S. Al-Quaddoomi, J. Albinus, I. Alborelli, S. Andani, P.-O. Attinger,
M. Bacac, D. Baumhoer, B. Beck-Schimmer, N. Beerenwinkel, C. Beisel, L. Bernasconi,
A. Bertolini, B. Bodenmiller, X. Bonilla, R. Casanova, S. Chevrier, N. Chicherova,
M. D’Costa, E. Danenberg, N. Davidson, M.-A.D. gan, R. Dummer, S. Engler, M. Erkens,
K. Eschbach, C. Esposito, A. Fedier, P. Ferreira, J. Ficek, A.L. Frei, B. Frey,
S. Goetze, L. Grob, G. Gut, D. Günther, M. Haberecker, P. Haeuptle, V. Heinzelmann-Schwarz,
S. Herter, R. Holtackers, T. Huesser, A. Irmisch, F. Jacob, A. Jacobs, T.M. Jaeger,
K. Jahn, A.R. James, P.M. Jermann, A. Kahles, A. Kahraman, V.H. Koelzer, W. Kuebler,
J. Kuipers, C.P. Kunze, C. Kurzeder, K.-V. Lehmann, M. Levesque, S. Lugert, G.
Maass, M. Manz, P. Markolin, J. Mena, U. Menzel, J.M. Metzler, N. Miglino, E.S.
Milani, H. Moch, S. Muenst, R. Murri, C.K. Ng, S. Nicolet, M. Nowak, P.G. Pedrioli,
L. Pelkmans, S. Piscuoglio, M. Prummer, M. Ritter, C. Rommel, M.L. Rosano-González,
G. Rätsch, N. Santacroce, J.S. del Castillo, R. Schlenker, P.C. Schwalie, S. Schwan,
T. Schär, G. Senti, F. Singer, S. Sivapatham, B. Snijder, B. Sobottka, V.T. Sreedharan,
S. Stark, D.J. Stekhoven, A.P. Theocharides, T.M. Thomas, M. Tolnay, V. Tosevski,
N.C. Toussaint, M.A. Tuncel, M. Tusup, A.V. Drogen, M. Vetter, T. Vlajnic, S.
Weber, W.P. Weber, R. Wegmann, M. Weller, F. Wendt, N. Wey, A. Wicki, B. Wollscheid,
S. Yu, J. Ziegler, M. Zimmermann, M. Zoche, G. Zuend, G. Rätsch, K.-V. Lehmann,
Bioinformatics 36 (2020) i919–i927.
date_created: 2023-08-21T12:28:20Z
date_published: 2020-12-01T00:00:00Z
date_updated: 2023-09-11T10:21:00Z
day: '01'
department:
- _id: FrLo
doi: 10.1093/bioinformatics/btaa843
extern: '1'
external_id:
pmid:
- '33381818'
intvolume: ' 36'
issue: Supplement_2
keyword:
- Computational Mathematics
- Computational Theory and Mathematics
- Computer Science Applications
- Molecular Biology
- Biochemistry
- Statistics and Probability
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1093/bioinformatics/btaa843
month: '12'
oa: 1
oa_version: Published Version
page: i919-i927
pmid: 1
publication: Bioinformatics
publication_identifier:
eissn:
- 1367-4811
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ratschlab/scim
scopus_import: '1'
status: public
title: 'SCIM: Universal single-cell matching with unpaired feature sets'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2020'
...
---
_id: '11657'
abstract:
- lang: eng
text: The minimum cut problem for an undirected edge-weighted graph asks us to divide
its set of nodes into two blocks while minimizing the weight sum of the cut edges.
Here, we introduce a linear-time algorithm to compute near-minimum cuts. Our algorithm
is based on cluster contraction using label propagation and Padberg and Rinaldi’s
contraction heuristics [SIAM Review, 1991]. We give both sequential and shared-memory
parallel implementations of our algorithm. Extensive experiments on both real-world
and generated instances show that our algorithm finds the optimal cut on nearly
all instances significantly faster than other state-of-the-art exact algorithms,
and our error rate is lower than that of other heuristic algorithms. In addition,
our parallel algorithm runs a factor 7.5× faster on average when using 32 threads.
To further speed up computations, we also give a version of our algorithm that
performs random edge contractions as preprocessing. This version achieves a lower
running time and better parallel scalability at the expense of a higher error
rate.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Alexander
full_name: Noe, Alexander
last_name: Noe
- first_name: Christian
full_name: Schulz, Christian
last_name: Schulz
- first_name: Darren
full_name: Strash, Darren
last_name: Strash
citation:
ama: Henzinger MH, Noe A, Schulz C, Strash D. Practical minimum cut algorithms.
ACM Journal of Experimental Algorithmics. 2018;23:1-22. doi:10.1145/3274662
apa: Henzinger, M. H., Noe, A., Schulz, C., & Strash, D. (2018). Practical minimum
cut algorithms. ACM Journal of Experimental Algorithmics. Association for
Computing Machinery. https://doi.org/10.1145/3274662
chicago: Henzinger, Monika H, Alexander Noe, Christian Schulz, and Darren Strash.
“Practical Minimum Cut Algorithms.” ACM Journal of Experimental Algorithmics.
Association for Computing Machinery, 2018. https://doi.org/10.1145/3274662.
ieee: M. H. Henzinger, A. Noe, C. Schulz, and D. Strash, “Practical minimum cut
algorithms,” ACM Journal of Experimental Algorithmics, vol. 23. Association
for Computing Machinery, pp. 1–22, 2018.
ista: Henzinger MH, Noe A, Schulz C, Strash D. 2018. Practical minimum cut algorithms.
ACM Journal of Experimental Algorithmics. 23, 1–22.
mla: Henzinger, Monika H., et al. “Practical Minimum Cut Algorithms.” ACM Journal
of Experimental Algorithmics, vol. 23, Association for Computing Machinery,
2018, pp. 1–22, doi:10.1145/3274662.
short: M.H. Henzinger, A. Noe, C. Schulz, D. Strash, ACM Journal of Experimental
Algorithmics 23 (2018) 1–22.
date_created: 2022-07-27T08:28:26Z
date_published: 2018-10-01T00:00:00Z
date_updated: 2022-09-09T11:32:52Z
day: '01'
doi: 10.1145/3274662
extern: '1'
external_id:
arxiv:
- '1708.06127'
intvolume: ' 23'
keyword:
- Theoretical Computer Science
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1708.06127
month: '10'
oa: 1
oa_version: Preprint
page: 1-22
publication: ACM Journal of Experimental Algorithmics
publication_identifier:
eissn:
- 1084-6654
issn:
- 1084-6654
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Practical minimum cut algorithms
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 23
year: '2018'
...
---
_id: '11670'
abstract:
- lang: eng
text: Auctions are widely used on the Web. Applications range from sponsored search
to platforms such as eBay. In these and in many other applications the auctions
in use are single-/multi-item auctions with unit demand. The main drawback of
standard mechanisms for this type of auctions, such as VCG and GSP, is the limited
expressiveness that they offer to the bidders. The General Auction Mechanism (GAM)
of Aggarwal et al. [2009] takes a first step toward addressing the problem of
limited expressiveness by computing a bidder optimal, envy-free outcome for linear
utility functions with identical slopes and a single discontinuity per bidder-item
pair. We show that in many practical situations this does not suffice to adequately
model the preferences of the bidders, and we overcome this problem by presenting
the first mechanism for piecewise linear utility functions with nonidentical slopes
and multiple discontinuities. Our mechanism runs in polynomial time. Like GAM
it is incentive compatible for inputs that fulfill a certain nondegeneracy assumption,
but our requirement is more general than the requirement of GAM. For discontinuous
utility functions that are nondegenerate as well as for continuous utility functions
the outcome of our mechanism is a competitive equilibrium. We also show how our
mechanism can be used to compute approximately bidder optimal, envy-free outcomes
for a general class of continuous utility functions via piecewise linear approximation.
Finally, we prove hardness results for even more expressive settings.
acknowledgement: We would like to thank Veronika Loitzenbauer and the anonymous referees
for their valuable feedback.
article_number: '1'
article_processing_charge: No
article_type: original
author:
- first_name: Paul
full_name: Dütting, Paul
last_name: Dütting
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Ingmar
full_name: Weber, Ingmar
last_name: Weber
citation:
ama: Dütting P, Henzinger MH, Weber I. An expressive mechanism for auctions on the
web. ACM Transactions on Economics and Computation. 2015;4(1). doi:10.1145/2716312
apa: Dütting, P., Henzinger, M. H., & Weber, I. (2015). An expressive mechanism
for auctions on the web. ACM Transactions on Economics and Computation.
Association for Computing Machinery. https://doi.org/10.1145/2716312
chicago: Dütting, Paul, Monika H Henzinger, and Ingmar Weber. “An Expressive Mechanism
for Auctions on the Web.” ACM Transactions on Economics and Computation.
Association for Computing Machinery, 2015. https://doi.org/10.1145/2716312.
ieee: P. Dütting, M. H. Henzinger, and I. Weber, “An expressive mechanism for auctions
on the web,” ACM Transactions on Economics and Computation, vol. 4, no.
1. Association for Computing Machinery, 2015.
ista: Dütting P, Henzinger MH, Weber I. 2015. An expressive mechanism for auctions
on the web. ACM Transactions on Economics and Computation. 4(1), 1.
mla: Dütting, Paul, et al. “An Expressive Mechanism for Auctions on the Web.” ACM
Transactions on Economics and Computation, vol. 4, no. 1, 1, Association for
Computing Machinery, 2015, doi:10.1145/2716312.
short: P. Dütting, M.H. Henzinger, I. Weber, ACM Transactions on Economics and Computation
4 (2015).
date_created: 2022-07-27T12:43:18Z
date_published: 2015-12-02T00:00:00Z
date_updated: 2023-02-09T10:08:41Z
day: '02'
doi: 10.1145/2716312
extern: '1'
intvolume: ' 4'
issue: '1'
keyword:
- Computational Mathematics
- Marketing
- Economics and Econometrics
- Statistics and Probability
- Computer Science (miscellaneous)
language:
- iso: eng
month: '12'
oa_version: None
publication: ACM Transactions on Economics and Computation
publication_identifier:
eissn:
- 2167-8383
issn:
- 2167-8375
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: An expressive mechanism for auctions on the web
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2015'
...
---
_id: '8459'
abstract:
- lang: eng
text: Nuclear magnetic resonance (NMR) is a powerful tool for observing the motion
of biomolecules at the atomic level. One technique, the analysis of relaxation
dispersion phenomenon, is highly suited for studying the kinetics and thermodynamics
of biological processes. Built on top of the relax computational environment for
NMR dynamics is a new dispersion analysis designed to be comprehensive, accurate
and easy-to-use. The software supports more models, both numeric and analytic,
than current solutions. An automated protocol, available for scripting and driving
the graphical user interface (GUI), is designed to simplify the analysis of dispersion
data for NMR spectroscopists. Decreases in optimization time are granted by parallelization
for running on computer clusters and by skipping an initial grid search by using
parameters from one solution as the starting point for another —using analytic
model results for the numeric models, taking advantage of model nesting, and using
averaged non-clustered results for the clustered analysis.
article_processing_charge: No
article_type: original
author:
- first_name: Sébastien
full_name: Morin, Sébastien
last_name: Morin
- first_name: Troels E
full_name: Linnet, Troels E
last_name: Linnet
- first_name: Mathilde
full_name: Lescanne, Mathilde
last_name: Lescanne
- first_name: Paul
full_name: Schanda, Paul
id: 7B541462-FAF6-11E9-A490-E8DFE5697425
last_name: Schanda
orcid: 0000-0002-9350-7606
- first_name: Gary S
full_name: Thompson, Gary S
last_name: Thompson
- first_name: Martin
full_name: Tollinger, Martin
last_name: Tollinger
- first_name: Kaare
full_name: Teilum, Kaare
last_name: Teilum
- first_name: Stéphane
full_name: Gagné, Stéphane
last_name: Gagné
- first_name: Dominique
full_name: Marion, Dominique
last_name: Marion
- first_name: Christian
full_name: Griesinger, Christian
last_name: Griesinger
- first_name: Martin
full_name: Blackledge, Martin
last_name: Blackledge
- first_name: Edward J
full_name: d’Auvergne, Edward J
last_name: d’Auvergne
citation:
ama: 'Morin S, Linnet TE, Lescanne M, et al. Relax: The analysis of biomolecular
kinetics and thermodynamics using NMR relaxation dispersion data. Bioinformatics.
2014;30(15):2219-2220. doi:10.1093/bioinformatics/btu166'
apa: 'Morin, S., Linnet, T. E., Lescanne, M., Schanda, P., Thompson, G. S., Tollinger,
M., … d’Auvergne, E. J. (2014). Relax: The analysis of biomolecular kinetics and
thermodynamics using NMR relaxation dispersion data. Bioinformatics. Oxford
University Press. https://doi.org/10.1093/bioinformatics/btu166'
chicago: 'Morin, Sébastien, Troels E Linnet, Mathilde Lescanne, Paul Schanda, Gary
S Thompson, Martin Tollinger, Kaare Teilum, et al. “Relax: The Analysis of Biomolecular
Kinetics and Thermodynamics Using NMR Relaxation Dispersion Data.” Bioinformatics.
Oxford University Press, 2014. https://doi.org/10.1093/bioinformatics/btu166.'
ieee: 'S. Morin et al., “Relax: The analysis of biomolecular kinetics and
thermodynamics using NMR relaxation dispersion data,” Bioinformatics, vol.
30, no. 15. Oxford University Press, pp. 2219–2220, 2014.'
ista: 'Morin S, Linnet TE, Lescanne M, Schanda P, Thompson GS, Tollinger M, Teilum
K, Gagné S, Marion D, Griesinger C, Blackledge M, d’Auvergne EJ. 2014. Relax:
The analysis of biomolecular kinetics and thermodynamics using NMR relaxation
dispersion data. Bioinformatics. 30(15), 2219–2220.'
mla: 'Morin, Sébastien, et al. “Relax: The Analysis of Biomolecular Kinetics and
Thermodynamics Using NMR Relaxation Dispersion Data.” Bioinformatics, vol.
30, no. 15, Oxford University Press, 2014, pp. 2219–20, doi:10.1093/bioinformatics/btu166.'
short: S. Morin, T.E. Linnet, M. Lescanne, P. Schanda, G.S. Thompson, M. Tollinger,
K. Teilum, S. Gagné, D. Marion, C. Griesinger, M. Blackledge, E.J. d’Auvergne,
Bioinformatics 30 (2014) 2219–2220.
date_created: 2020-09-18T10:08:07Z
date_published: 2014-08-01T00:00:00Z
date_updated: 2021-01-12T08:19:25Z
day: '01'
doi: 10.1093/bioinformatics/btu166
extern: '1'
intvolume: ' 30'
issue: '15'
keyword:
- Statistics and Probability
- Computational Theory and Mathematics
- Biochemistry
- Molecular Biology
- Computational Mathematics
- Computer Science Applications
language:
- iso: eng
month: '08'
oa_version: None
page: 2219-2220
publication: Bioinformatics
publication_identifier:
issn:
- 1367-4803
- 1460-2059
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1093/bioinformatics/btz397
status: public
title: 'Relax: The analysis of biomolecular kinetics and thermodynamics using NMR
relaxation dispersion data'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 30
year: '2014'
...
---
_id: '9145'
abstract:
- lang: eng
text: "We have found a new way to express the solutions of the RSM (Reynolds Stress
Model) equations that allows us to present the turbulent diffusivities for heat,
salt and momentum in a way that is considerably simpler and thus easier to implement
than in previous work. The RSM provides the dimensionless mixing efficiencies
Γα (α stands for heat, salt and momentum). However, to compute the diffusivities,
one needs additional information, specifically, the dissipation ε. Since a dynamic
equation for the latter that includes the physical processes relevant to the ocean
is still not available, one must resort to different sources of information outside
the RSM to obtain a complete Mixing Scheme usable in OGCMs.\r\nAs for the RSM
results, we show that the Γα’s are functions of both Ri and Rρ (Richardson number
and density ratio representing double diffusion, DD); the Γα are different for
heat, salt and momentum; in the case of heat, the traditional value Γh = 0.2 is
valid only in the presence of strong shear (when DD is inoperative) while when
shear subsides, NATRE data show that Γh can be three times as large, a result
that we reproduce. The salt Γs is given in terms of Γh. The momentum Γm has thus
far been guessed with different prescriptions while the RSM provides a well defined
expression for Γm(Ri, Rρ). Having tested Γh, we then test the momentum Γm by showing
that the turbulent Prandtl number Γm/Γh vs. Ri reproduces the available data quite
well.\r\n\r\nAs for the dissipation ε, we use different representations, one for
the mixed layer (ML), one for the thermocline and one for the ocean’s bottom.
For the ML, we adopt a procedure analogous to the one successfully used in PB
(planetary boundary layer) studies; for the thermocline, we employ an expression
for the variable εN−2 from studies of the internal gravity waves spectra which
includes a latitude dependence; for the ocean bottom, we adopt the enhanced bottom
diffusivity expression used by previous authors but with a state of the art internal
tidal energy formulation and replace the fixed Γα = 0.2 with the RSM result that
brings into the problem the Ri, Rρ dependence of the Γα; the unresolved bottom
drag, which has thus far been either ignored or modeled with heuristic relations,
is modeled using a formalism we previously developed and tested in PBL studies.\r\nWe
carried out several tests without an OGCM. Prandtl and flux Richardson numbers
vs. Ri. The RSM model reproduces both types of data satisfactorily. DD and Mixing
efficiency Γh(Ri, Rρ). The RSM model reproduces well the NATRE data. Bimodal ε-distribution.
NATRE data show that ε(Ri < 1) ≈ 10ε(Ri > 1), which our model reproduces. Heat
to salt flux ratio. In the Ri ≫ 1 regime, the RSM predictions reproduce the data
satisfactorily. NATRE mass diffusivity. The z-profile of the mass diffusivity
reproduces well the measurements at NATRE. The local form of the mixing scheme
is algebraic with one cubic equation to solve."
article_processing_charge: No
article_type: original
author:
- first_name: V.M.
full_name: Canuto, V.M.
last_name: Canuto
- first_name: A.M.
full_name: Howard, A.M.
last_name: Howard
- first_name: Y.
full_name: Cheng, Y.
last_name: Cheng
- first_name: Caroline J
full_name: Muller, Caroline J
id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b
last_name: Muller
orcid: 0000-0001-5836-5350
- first_name: A.
full_name: Leboissetier, A.
last_name: Leboissetier
- first_name: S.R.
full_name: Jayne, S.R.
last_name: Jayne
citation:
ama: 'Canuto VM, Howard AM, Cheng Y, Muller CJ, Leboissetier A, Jayne SR. Ocean
turbulence, III: New GISS vertical mixing scheme. Ocean Modelling. 2010;34(3-4):70-91.
doi:10.1016/j.ocemod.2010.04.006'
apa: 'Canuto, V. M., Howard, A. M., Cheng, Y., Muller, C. J., Leboissetier, A.,
& Jayne, S. R. (2010). Ocean turbulence, III: New GISS vertical mixing scheme.
Ocean Modelling. Elsevier. https://doi.org/10.1016/j.ocemod.2010.04.006'
chicago: 'Canuto, V.M., A.M. Howard, Y. Cheng, Caroline J Muller, A. Leboissetier,
and S.R. Jayne. “Ocean Turbulence, III: New GISS Vertical Mixing Scheme.” Ocean
Modelling. Elsevier, 2010. https://doi.org/10.1016/j.ocemod.2010.04.006.'
ieee: 'V. M. Canuto, A. M. Howard, Y. Cheng, C. J. Muller, A. Leboissetier, and
S. R. Jayne, “Ocean turbulence, III: New GISS vertical mixing scheme,” Ocean
Modelling, vol. 34, no. 3–4. Elsevier, pp. 70–91, 2010.'
ista: 'Canuto VM, Howard AM, Cheng Y, Muller CJ, Leboissetier A, Jayne SR. 2010.
Ocean turbulence, III: New GISS vertical mixing scheme. Ocean Modelling. 34(3–4),
70–91.'
mla: 'Canuto, V. M., et al. “Ocean Turbulence, III: New GISS Vertical Mixing Scheme.”
Ocean Modelling, vol. 34, no. 3–4, Elsevier, 2010, pp. 70–91, doi:10.1016/j.ocemod.2010.04.006.'
short: V.M. Canuto, A.M. Howard, Y. Cheng, C.J. Muller, A. Leboissetier, S.R. Jayne,
Ocean Modelling 34 (2010) 70–91.
date_created: 2021-02-15T14:40:19Z
date_published: 2010-05-12T00:00:00Z
date_updated: 2022-01-24T13:51:35Z
day: '12'
doi: 10.1016/j.ocemod.2010.04.006
extern: '1'
intvolume: ' 34'
issue: 3-4
keyword:
- Computer Science (miscellaneous)
- Geotechnical Engineering and Engineering Geology
- Atmospheric Science
- Oceanography
language:
- iso: eng
month: '05'
oa_version: None
page: 70-91
publication: Ocean Modelling
publication_identifier:
issn:
- 1463-5003
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: 'Ocean turbulence, III: New GISS vertical mixing scheme'
type: journal_article
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 34
year: '2010'
...
---
_id: '8509'
abstract:
- lang: eng
text: The goal of this paper is to present to nonspecialists what is perhaps the
simplest possible geometrical picture explaining the mechanism of Arnold diffusion.
We choose to speak of a specific model—that of geometric rays in a periodic optical
medium. This model is equivalent to that of a particle in a periodic potential
in ${\mathbb R}^{n}$ with energy prescribed and to the geodesic flow in a Riemannian
metric on ${\mathbb R}^{n} $.
article_processing_charge: No
article_type: original
author:
- first_name: Vadim
full_name: Kaloshin, Vadim
id: FE553552-CDE8-11E9-B324-C0EBE5697425
last_name: Kaloshin
orcid: 0000-0002-6051-2628
- first_name: Mark
full_name: Levi, Mark
last_name: Levi
citation:
ama: Kaloshin V, Levi M. Geometry of Arnold diffusion. SIAM Review. 2008;50(4):702-720.
doi:10.1137/070703235
apa: Kaloshin, V., & Levi, M. (2008). Geometry of Arnold diffusion. SIAM
Review. Society for Industrial & Applied Mathematics. https://doi.org/10.1137/070703235
chicago: Kaloshin, Vadim, and Mark Levi. “Geometry of Arnold Diffusion.” SIAM
Review. Society for Industrial & Applied Mathematics, 2008. https://doi.org/10.1137/070703235.
ieee: V. Kaloshin and M. Levi, “Geometry of Arnold diffusion,” SIAM Review,
vol. 50, no. 4. Society for Industrial & Applied Mathematics, pp. 702–720,
2008.
ista: Kaloshin V, Levi M. 2008. Geometry of Arnold diffusion. SIAM Review. 50(4),
702–720.
mla: Kaloshin, Vadim, and Mark Levi. “Geometry of Arnold Diffusion.” SIAM Review,
vol. 50, no. 4, Society for Industrial & Applied Mathematics, 2008, pp. 702–20,
doi:10.1137/070703235.
short: V. Kaloshin, M. Levi, SIAM Review 50 (2008) 702–720.
date_created: 2020-09-18T10:48:12Z
date_published: 2008-11-05T00:00:00Z
date_updated: 2021-01-12T08:19:46Z
day: '05'
doi: 10.1137/070703235
extern: '1'
intvolume: ' 50'
issue: '4'
keyword:
- Theoretical Computer Science
- Applied Mathematics
- Computational Mathematics
language:
- iso: eng
month: '11'
oa_version: None
page: 702-720
publication: SIAM Review
publication_identifier:
issn:
- 0036-1445
- 1095-7200
publication_status: published
publisher: Society for Industrial & Applied Mathematics
quality_controlled: '1'
status: public
title: Geometry of Arnold diffusion
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 50
year: '2008'
...