---
_id: '6796'
abstract:
- lang: eng
  text: Nearby grid cells have been observed to express a remarkable degree of long-rangeorder,
    which is often idealized as extending potentially to infinity. Yet their strict
    peri-odic firing and ensemble coherence are theoretically possible only in flat
    environments, much unlike the burrows which rodents usually live in. Are the symmetrical,
    coherent grid maps inferred in the lab relevant to chart their way in their natural
    habitat? We consider spheres as simple models of curved environments and waiting
    for the appropriate experiments to be performed, we use our adaptation model to
    predict what grid maps would emerge in a network with the same type of recurrent
    connections, which on the plane produce coherence among the units. We find that
    on the sphere such connections distort the maps that single grid units would express
    on their own, and aggregate them into clusters. When remapping to a different
    spherical environment, units in each cluster maintain only partial coherence,
    similar to what is observed in disordered materials, such as spin glasses.
article_processing_charge: No
article_type: original
author:
- first_name: Federico
  full_name: Stella, Federico
  id: 39AF1E74-F248-11E8-B48F-1D18A9856A87
  last_name: Stella
  orcid: 0000-0001-9439-3148
- first_name: Eugenio
  full_name: Urdapilleta, Eugenio
  last_name: Urdapilleta
- first_name: Yifan
  full_name: Luo, Yifan
  last_name: Luo
- first_name: Alessandro
  full_name: Treves, Alessandro
  last_name: Treves
citation:
  ama: Stella F, Urdapilleta E, Luo Y, Treves A. Partial coherence and frustration
    in self-organizing spherical grids. <i>Hippocampus</i>. 2020;30(4):302-313. doi:<a
    href="https://doi.org/10.1002/hipo.23144">10.1002/hipo.23144</a>
  apa: Stella, F., Urdapilleta, E., Luo, Y., &#38; Treves, A. (2020). Partial coherence
    and frustration in self-organizing spherical grids. <i>Hippocampus</i>. Wiley.
    <a href="https://doi.org/10.1002/hipo.23144">https://doi.org/10.1002/hipo.23144</a>
  chicago: Stella, Federico, Eugenio Urdapilleta, Yifan Luo, and Alessandro Treves.
    “Partial Coherence and Frustration in Self-Organizing Spherical Grids.” <i>Hippocampus</i>.
    Wiley, 2020. <a href="https://doi.org/10.1002/hipo.23144">https://doi.org/10.1002/hipo.23144</a>.
  ieee: F. Stella, E. Urdapilleta, Y. Luo, and A. Treves, “Partial coherence and frustration
    in self-organizing spherical grids,” <i>Hippocampus</i>, vol. 30, no. 4. Wiley,
    pp. 302–313, 2020.
  ista: Stella F, Urdapilleta E, Luo Y, Treves A. 2020. Partial coherence and frustration
    in self-organizing spherical grids. Hippocampus. 30(4), 302–313.
  mla: Stella, Federico, et al. “Partial Coherence and Frustration in Self-Organizing
    Spherical Grids.” <i>Hippocampus</i>, vol. 30, no. 4, Wiley, 2020, pp. 302–13,
    doi:<a href="https://doi.org/10.1002/hipo.23144">10.1002/hipo.23144</a>.
  short: F. Stella, E. Urdapilleta, Y. Luo, A. Treves, Hippocampus 30 (2020) 302–313.
date_created: 2019-08-11T21:59:24Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2023-08-17T13:53:14Z
day: '01'
ddc:
- '570'
department:
- _id: JoCs
doi: 10.1002/hipo.23144
external_id:
  isi:
  - '000477299600001'
  pmid:
  - '31339190'
file:
- access_level: open_access
  checksum: 7b54d22bfbfc0d1188a9ea24d985bfb2
  content_type: application/pdf
  creator: dernst
  date_created: 2019-08-12T07:53:33Z
  date_updated: 2020-07-14T12:47:40Z
  file_id: '6800'
  file_name: 2019_Hippocampus_Stella.pdf
  file_size: 2370658
  relation: main_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
intvolume: '        30'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 302-313
pmid: 1
publication: Hippocampus
publication_identifier:
  eissn:
  - '10981063'
  issn:
  - '10509631'
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Partial coherence and frustration in self-organizing spherical grids
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 30
year: '2020'
...
---
_id: '6808'
abstract:
- lang: eng
  text: Super-resolution fluorescence microscopy has become an important catalyst
    for discovery in the life sciences. In STimulated Emission Depletion (STED) microscopy,
    a pattern of light drives fluorophores from a signal-emitting on-state to a non-signalling
    off-state. Only emitters residing in a sub-diffraction volume around an intensity
    minimum are allowed to fluoresce, rendering them distinguishable from the nearby,
    but dark fluorophores. STED routinely achieves resolution in the few tens of nanometers
    range in biological samples and is suitable for live imaging. Here, we review
    the working principle of STED and provide general guidelines for successful STED
    imaging. The strive for ever higher resolution comes at the cost of increased
    light burden. We discuss techniques to reduce light exposure and mitigate its
    detrimental effects on the specimen. These include specialized illumination strategies
    as well as protecting fluorophores from photobleaching mediated by high-intensity
    STED light. This opens up the prospect of volumetric imaging in living cells and
    tissues with diffraction-unlimited resolution in all three spatial dimensions.
article_processing_charge: No
article_type: original
author:
- first_name: Wiebke
  full_name: Jahr, Wiebke
  id: 425C1CE8-F248-11E8-B48F-1D18A9856A87
  last_name: Jahr
- first_name: Philipp
  full_name: Velicky, Philipp
  id: 39BDC62C-F248-11E8-B48F-1D18A9856A87
  last_name: Velicky
  orcid: 0000-0002-2340-7431
- first_name: Johann G
  full_name: Danzl, Johann G
  id: 42EFD3B6-F248-11E8-B48F-1D18A9856A87
  last_name: Danzl
  orcid: 0000-0001-8559-3973
citation:
  ama: Jahr W, Velicky P, Danzl JG. Strategies to maximize performance in STimulated
    Emission Depletion (STED) nanoscopy of biological specimens. <i>Methods</i>. 2020;174(3):27-41.
    doi:<a href="https://doi.org/10.1016/j.ymeth.2019.07.019">10.1016/j.ymeth.2019.07.019</a>
  apa: Jahr, W., Velicky, P., &#38; Danzl, J. G. (2020). Strategies to maximize performance
    in STimulated Emission Depletion (STED) nanoscopy of biological specimens. <i>Methods</i>.
    Elsevier. <a href="https://doi.org/10.1016/j.ymeth.2019.07.019">https://doi.org/10.1016/j.ymeth.2019.07.019</a>
  chicago: Jahr, Wiebke, Philipp Velicky, and Johann G Danzl. “Strategies to Maximize
    Performance in STimulated Emission Depletion (STED) Nanoscopy of Biological Specimens.”
    <i>Methods</i>. Elsevier, 2020. <a href="https://doi.org/10.1016/j.ymeth.2019.07.019">https://doi.org/10.1016/j.ymeth.2019.07.019</a>.
  ieee: W. Jahr, P. Velicky, and J. G. Danzl, “Strategies to maximize performance
    in STimulated Emission Depletion (STED) nanoscopy of biological specimens,” <i>Methods</i>,
    vol. 174, no. 3. Elsevier, pp. 27–41, 2020.
  ista: Jahr W, Velicky P, Danzl JG. 2020. Strategies to maximize performance in STimulated
    Emission Depletion (STED) nanoscopy of biological specimens. Methods. 174(3),
    27–41.
  mla: Jahr, Wiebke, et al. “Strategies to Maximize Performance in STimulated Emission
    Depletion (STED) Nanoscopy of Biological Specimens.” <i>Methods</i>, vol. 174,
    no. 3, Elsevier, 2020, pp. 27–41, doi:<a href="https://doi.org/10.1016/j.ymeth.2019.07.019">10.1016/j.ymeth.2019.07.019</a>.
  short: W. Jahr, P. Velicky, J.G. Danzl, Methods 174 (2020) 27–41.
date_created: 2019-08-12T16:36:32Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-08-17T13:59:57Z
day: '01'
department:
- _id: JoDa
doi: 10.1016/j.ymeth.2019.07.019
external_id:
  isi:
  - '000525860400005'
  pmid:
  - '31344404'
intvolume: '       174'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100895/
month: '03'
oa: 1
oa_version: Submitted Version
page: 27-41
pmid: 1
project:
- _id: 265CB4D0-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: I03600
  name: Optical control of synaptic function via adhesion molecules
- _id: 2668BFA0-B435-11E9-9278-68D0E5697425
  grant_number: LT00057
  name: High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration
publication: Methods
publication_identifier:
  issn:
  - 1046-2023
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Strategies to maximize performance in STimulated Emission Depletion (STED)
  nanoscopy of biological specimens
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 174
year: '2020'
...
---
_id: '6906'
abstract:
- lang: eng
  text: We consider systems of bosons trapped in a box, in the Gross–Pitaevskii regime.
    We show that low-energy states exhibit complete Bose–Einstein condensation with
    an optimal bound on the number of orthogonal excitations. This extends recent
    results obtained in Boccato et al. (Commun Math Phys 359(3):975–1026, 2018), removing
    the assumption of small interaction potential.
acknowledgement: "We would like to thank P. T. Nam and R. Seiringer for several useful
  discussions and\r\nfor suggesting us to use the localization techniques from [9].
  C. Boccato has received funding from the\r\nEuropean Research Council (ERC) under
  the programme Horizon 2020 (Grant Agreement 694227). B. Schlein gratefully acknowledges
  support from the NCCR SwissMAP and from the Swiss National Foundation of Science
  (Grant No. 200020_1726230) through the SNF Grant “Dynamical and energetic properties
  of Bose–Einstein condensates”."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Chiara
  full_name: Boccato, Chiara
  id: 342E7E22-F248-11E8-B48F-1D18A9856A87
  last_name: Boccato
- first_name: Christian
  full_name: Brennecke, Christian
  last_name: Brennecke
- first_name: Serena
  full_name: Cenatiempo, Serena
  last_name: Cenatiempo
- first_name: Benjamin
  full_name: Schlein, Benjamin
  last_name: Schlein
citation:
  ama: Boccato C, Brennecke C, Cenatiempo S, Schlein B. Optimal rate for Bose-Einstein
    condensation in the Gross-Pitaevskii regime. <i>Communications in Mathematical
    Physics</i>. 2020;376:1311-1395. doi:<a href="https://doi.org/10.1007/s00220-019-03555-9">10.1007/s00220-019-03555-9</a>
  apa: Boccato, C., Brennecke, C., Cenatiempo, S., &#38; Schlein, B. (2020). Optimal
    rate for Bose-Einstein condensation in the Gross-Pitaevskii regime. <i>Communications
    in Mathematical Physics</i>. Springer. <a href="https://doi.org/10.1007/s00220-019-03555-9">https://doi.org/10.1007/s00220-019-03555-9</a>
  chicago: Boccato, Chiara, Christian Brennecke, Serena Cenatiempo, and Benjamin Schlein.
    “Optimal Rate for Bose-Einstein Condensation in the Gross-Pitaevskii Regime.”
    <i>Communications in Mathematical Physics</i>. Springer, 2020. <a href="https://doi.org/10.1007/s00220-019-03555-9">https://doi.org/10.1007/s00220-019-03555-9</a>.
  ieee: C. Boccato, C. Brennecke, S. Cenatiempo, and B. Schlein, “Optimal rate for
    Bose-Einstein condensation in the Gross-Pitaevskii regime,” <i>Communications
    in Mathematical Physics</i>, vol. 376. Springer, pp. 1311–1395, 2020.
  ista: Boccato C, Brennecke C, Cenatiempo S, Schlein B. 2020. Optimal rate for Bose-Einstein
    condensation in the Gross-Pitaevskii regime. Communications in Mathematical Physics.
    376, 1311–1395.
  mla: Boccato, Chiara, et al. “Optimal Rate for Bose-Einstein Condensation in the
    Gross-Pitaevskii Regime.” <i>Communications in Mathematical Physics</i>, vol.
    376, Springer, 2020, pp. 1311–95, doi:<a href="https://doi.org/10.1007/s00220-019-03555-9">10.1007/s00220-019-03555-9</a>.
  short: C. Boccato, C. Brennecke, S. Cenatiempo, B. Schlein, Communications in Mathematical
    Physics 376 (2020) 1311–1395.
date_created: 2019-09-24T17:30:59Z
date_published: 2020-06-01T00:00:00Z
date_updated: 2024-02-22T13:33:02Z
day: '01'
department:
- _id: RoSe
doi: 10.1007/s00220-019-03555-9
ec_funded: 1
external_id:
  arxiv:
  - '1812.03086'
  isi:
  - '000536053300012'
intvolume: '       376'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1812.03086
month: '06'
oa: 1
oa_version: Preprint
page: 1311-1395
project:
- _id: 25C6DC12-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '694227'
  name: Analysis of quantum many-body systems
publication: Communications in Mathematical Physics
publication_identifier:
  eissn:
  - 1432-0916
  issn:
  - 0010-3616
publication_status: published
publisher: Springer
quality_controlled: '1'
scopus_import: '1'
status: public
title: Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 376
year: '2020'
...
---
_id: '10012'
abstract:
- lang: eng
  text: We prove that in the absence of topological changes, the notion of BV solutions
    to planar multiphase mean curvature flow does not allow for a mechanism for (unphysical)
    non-uniqueness. Our approach is based on the local structure of the energy landscape
    near a classical evolution by mean curvature. Mean curvature flow being the gradient
    flow of the surface energy functional, we develop a gradient-flow analogue of
    the notion of calibrations. Just like the existence of a calibration guarantees
    that one has reached a global minimum in the energy landscape, the existence of
    a "gradient flow calibration" ensures that the route of steepest descent in the
    energy landscape is unique and stable.
acknowledgement: Parts of the paper were written during the visit of the authors to
  the Hausdorff Research Institute for Mathematics (HIM), University of Bonn, in the
  framework of the trimester program “Evolution of Interfaces”. The support and the
  hospitality of HIM are gratefully acknowledged. This project has received funding
  from the European Union’s Horizon 2020 research and innovation programme under the
  Marie Sklodowska-Curie Grant Agreement No. 665385.
article_number: '2003.05478'
article_processing_charge: No
arxiv: 1
author:
- first_name: Julian L
  full_name: Fischer, Julian L
  id: 2C12A0B0-F248-11E8-B48F-1D18A9856A87
  last_name: Fischer
  orcid: 0000-0002-0479-558X
- first_name: Sebastian
  full_name: Hensel, Sebastian
  id: 4D23B7DA-F248-11E8-B48F-1D18A9856A87
  last_name: Hensel
  orcid: 0000-0001-7252-8072
- first_name: Tim
  full_name: Laux, Tim
  last_name: Laux
- first_name: Thilo
  full_name: Simon, Thilo
  last_name: Simon
citation:
  ama: 'Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy landscape
    in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions.
    <i>arXiv</i>.'
  apa: 'Fischer, J. L., Hensel, S., Laux, T., &#38; Simon, T. (n.d.). The local structure
    of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness
    and stability of evolutions. <i>arXiv</i>.'
  chicago: 'Fischer, Julian L, Sebastian Hensel, Tim Laux, and Thilo Simon. “The Local
    Structure of the Energy Landscape in Multiphase Mean Curvature Flow: Weak-Strong
    Uniqueness and Stability of Evolutions.” <i>ArXiv</i>, n.d.'
  ieee: 'J. L. Fischer, S. Hensel, T. Laux, and T. Simon, “The local structure of
    the energy landscape in multiphase mean curvature flow: weak-strong uniqueness
    and stability of evolutions,” <i>arXiv</i>. .'
  ista: 'Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy
    landscape in multiphase mean curvature flow: weak-strong uniqueness and stability
    of evolutions. arXiv, 2003.05478.'
  mla: 'Fischer, Julian L., et al. “The Local Structure of the Energy Landscape in
    Multiphase Mean Curvature Flow: Weak-Strong Uniqueness and Stability of Evolutions.”
    <i>ArXiv</i>, 2003.05478.'
  short: J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.).
date_created: 2021-09-13T12:17:11Z
date_published: 2020-03-11T00:00:00Z
date_updated: 2023-09-07T13:30:45Z
day: '11'
department:
- _id: JuFi
ec_funded: 1
external_id:
  arxiv:
  - '2003.05478'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2003.05478
month: '03'
oa: 1
oa_version: Preprint
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '665385'
  name: International IST Doctoral Program
publication: arXiv
publication_status: submitted
related_material:
  record:
  - id: '10007'
    relation: dissertation_contains
    status: public
status: public
title: 'The local structure of the energy landscape in multiphase mean curvature flow:
  weak-strong uniqueness and stability of evolutions'
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '10022'
abstract:
- lang: eng
  text: We consider finite-volume approximations of Fokker-Planck equations on bounded
    convex domains in R^d and study the corresponding gradient flow structures. We
    reprove the convergence of the discrete to continuous Fokker-Planck equation via
    the method of Evolutionary Γ-convergence, i.e., we pass to the limit at the level
    of the gradient flow structures, generalising the one-dimensional result obtained
    by Disser and Liero. The proof is of variational nature and relies on a Mosco
    convergence result for functionals in the discrete-to-continuum limit that is
    of independent interest. Our results apply to arbitrary regular meshes, even though
    the associated discrete transport distances may fail to converge to the Wasserstein
    distance in this generality.
acknowledgement: This work is supported by the European Research Council (ERC) under
  the European Union’s Horizon 2020 research and innovation programme (grant agreement
  No 716117) and by the Austrian Science Fund (FWF), grants No F65 and W1245.
article_number: '2008.10962'
article_processing_charge: No
arxiv: 1
author:
- first_name: Dominik L
  full_name: Forkert, Dominik L
  id: 35C79D68-F248-11E8-B48F-1D18A9856A87
  last_name: Forkert
- first_name: Jan
  full_name: Maas, Jan
  id: 4C5696CE-F248-11E8-B48F-1D18A9856A87
  last_name: Maas
  orcid: 0000-0002-0845-1338
- first_name: Lorenzo
  full_name: Portinale, Lorenzo
  id: 30AD2CBC-F248-11E8-B48F-1D18A9856A87
  last_name: Portinale
citation:
  ama: Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient
    flow structures for Fokker-Planck equations in multiple dimensions. <i>arXiv</i>.
  apa: Forkert, D. L., Maas, J., &#38; Portinale, L. (n.d.). Evolutionary Γ-convergence
    of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions.
    <i>arXiv</i>.
  chicago: Forkert, Dominik L, Jan Maas, and Lorenzo Portinale. “Evolutionary Γ-Convergence
    of Entropic Gradient Flow Structures for Fokker-Planck Equations in Multiple Dimensions.”
    <i>ArXiv</i>, n.d.
  ieee: D. L. Forkert, J. Maas, and L. Portinale, “Evolutionary Γ-convergence of entropic
    gradient flow structures for Fokker-Planck equations in multiple dimensions,”
    <i>arXiv</i>. .
  ista: Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient
    flow structures for Fokker-Planck equations in multiple dimensions. arXiv, 2008.10962.
  mla: Forkert, Dominik L., et al. “Evolutionary Γ-Convergence of Entropic Gradient
    Flow Structures for Fokker-Planck Equations in Multiple Dimensions.” <i>ArXiv</i>,
    2008.10962.
  short: D.L. Forkert, J. Maas, L. Portinale, ArXiv (n.d.).
date_created: 2021-09-17T10:57:27Z
date_published: 2020-08-25T00:00:00Z
date_updated: 2023-09-07T13:31:05Z
day: '25'
department:
- _id: JaMa
ec_funded: 1
external_id:
  arxiv:
  - '2008.10962'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2008.10962
month: '08'
oa: 1
oa_version: Preprint
page: '33'
project:
- _id: 256E75B8-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '716117'
  name: Optimal Transport and Stochastic Dynamics
- _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2
  grant_number: F6504
  name: Taming Complexity in Partial Differential Systems
publication: arXiv
publication_status: submitted
related_material:
  record:
  - id: '11739'
    relation: later_version
    status: public
  - id: '10030'
    relation: dissertation_contains
    status: public
status: public
title: Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck
  equations in multiple dimensions
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '10328'
abstract:
- lang: eng
  text: We discus noise channels in coherent electro-optic up-conversion between microwave
    and optical fields, in particular due to optical heating. We also report on a
    novel configuration, which promises to be flexible and highly efficient.
alternative_title:
- OSA Technical Digest
article_number: QTu8A.1
article_processing_charge: No
author:
- first_name: Nicholas J.
  full_name: Lambert, Nicholas J.
  last_name: Lambert
- first_name: Sonia
  full_name: Mobassem, Sonia
  last_name: Mobassem
- first_name: Alfredo R
  full_name: Rueda Sanchez, Alfredo R
  id: 3B82B0F8-F248-11E8-B48F-1D18A9856A87
  last_name: Rueda Sanchez
  orcid: 0000-0001-6249-5860
- first_name: Harald G.L.
  full_name: Schwefel, Harald G.L.
  last_name: Schwefel
citation:
  ama: 'Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. New designs and noise
    channels in electro-optic microwave to optical up-conversion. In: <i>OSA Quantum
    2.0 Conference</i>. Optica Publishing Group; 2020. doi:<a href="https://doi.org/10.1364/QUANTUM.2020.QTu8A.1">10.1364/QUANTUM.2020.QTu8A.1</a>'
  apa: 'Lambert, N. J., Mobassem, S., Rueda Sanchez, A. R., &#38; Schwefel, H. G.
    L. (2020). New designs and noise channels in electro-optic microwave to optical
    up-conversion. In <i>OSA Quantum 2.0 Conference</i>. Washington, DC, United States:
    Optica Publishing Group. <a href="https://doi.org/10.1364/QUANTUM.2020.QTu8A.1">https://doi.org/10.1364/QUANTUM.2020.QTu8A.1</a>'
  chicago: Lambert, Nicholas J., Sonia Mobassem, Alfredo R Rueda Sanchez, and Harald
    G.L. Schwefel. “New Designs and Noise Channels in Electro-Optic Microwave to Optical
    up-Conversion.” In <i>OSA Quantum 2.0 Conference</i>. Optica Publishing Group,
    2020. <a href="https://doi.org/10.1364/QUANTUM.2020.QTu8A.1">https://doi.org/10.1364/QUANTUM.2020.QTu8A.1</a>.
  ieee: N. J. Lambert, S. Mobassem, A. R. Rueda Sanchez, and H. G. L. Schwefel, “New
    designs and noise channels in electro-optic microwave to optical up-conversion,”
    in <i>OSA Quantum 2.0 Conference</i>, Washington, DC, United States, 2020.
  ista: 'Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. 2020. New designs
    and noise channels in electro-optic microwave to optical up-conversion. OSA Quantum
    2.0 Conference. OSA: Optical Society of America, OSA Technical Digest, , QTu8A.1.'
  mla: Lambert, Nicholas J., et al. “New Designs and Noise Channels in Electro-Optic
    Microwave to Optical up-Conversion.” <i>OSA Quantum 2.0 Conference</i>, QTu8A.1,
    Optica Publishing Group, 2020, doi:<a href="https://doi.org/10.1364/QUANTUM.2020.QTu8A.1">10.1364/QUANTUM.2020.QTu8A.1</a>.
  short: N.J. Lambert, S. Mobassem, A.R. Rueda Sanchez, H.G.L. Schwefel, in:, OSA
    Quantum 2.0 Conference, Optica Publishing Group, 2020.
conference:
  end_date: 2020-09-17
  location: Washington, DC, United States
  name: 'OSA: Optical Society of America'
  start_date: 2020-09-14
date_created: 2021-11-21T23:01:31Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2023-10-18T08:32:34Z
day: '01'
department:
- _id: JoFi
doi: 10.1364/QUANTUM.2020.QTu8A.1
language:
- iso: eng
month: '01'
oa_version: None
publication: OSA Quantum 2.0 Conference
publication_identifier:
  isbn:
  - 9-781-5575-2820-9
publication_status: published
publisher: Optica Publishing Group
quality_controlled: '1'
scopus_import: '1'
status: public
title: New designs and noise channels in electro-optic microwave to optical up-conversion
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '10556'
abstract:
- lang: eng
  text: In this paper, we present the first Asynchronous Distributed Key Generation
    (ADKG) algorithm which is also the first distributed key generation algorithm
    that can generate cryptographic keys with a dual (f,2f+1)-threshold (where f is
    the number of faulty parties). As a result, using our ADKG we remove the trusted
    setup assumption that the most scalable consensus algorithms make. In order to
    create a DKG with a dual (f,2f+1)- threshold we first answer in the affirmative
    the open question posed by Cachin et al. [7] on how to create an Asynchronous
    Verifiable Secret Sharing (AVSS) protocol with a reconstruction threshold of f+1<k
    łe 2f+1, which is of independent interest. Our High-threshold-AVSS (HAVSS) uses
    an asymmetric bivariate polynomial to encode the secret. This enables the reconstruction
    of the secret only if a set of k nodes contribute while allowing an honest node
    that did not participate in the sharing phase to recover his share with the help
    of f+1 honest parties. Once we have HAVSS we can use it to bootstrap scalable
    partially synchronous consensus protocols, but the question on how to get a DKG
    in asynchrony remains as we need a way to produce common randomness. The solution
    comes from a novel Eventually Perfect Common Coin (EPCC) abstraction that enables
    the generation of a common coin from n concurrent HAVSS invocations. EPCC's key
    property is that it is eventually reliable, as it might fail to agree at most
    f times (even if invoked a polynomial number of times). Using EPCC we implement
    an Eventually Efficient Asynchronous Binary Agreement (EEABA) which is optimal
    when the EPCC agrees and protects safety when EPCC fails. Finally, using EEABA
    we construct the first ADKG which has the same overhead and expected runtime as
    the best partially-synchronous DKG (O(n4) words, O(f) rounds). As a corollary
    of our ADKG, we can also create the first Validated Asynchronous Byzantine Agreement
    (VABA) that does not need a trusted dealer to setup threshold signatures of degree
    n-f. Our VABA has an overhead of expected O(n2) words and O(1) time per instance,
    after an initial O(n4) words and O(f) time bootstrap via ADKG.
acknowledgement: We would like to thank Ittai Abraham for the discussions and guidance
  during the initial conception of the project, especially for HAVSS. Furthermore,
  we would like to thank the anonymous reviewers for pointing out the relevance of
  this work to MPC protocols.
article_processing_charge: No
author:
- first_name: Eleftherios
  full_name: Kokoris Kogias, Eleftherios
  id: f5983044-d7ef-11ea-ac6d-fd1430a26d30
  last_name: Kokoris Kogias
- first_name: Dahlia
  full_name: Malkhi, Dahlia
  last_name: Malkhi
- first_name: Alexander
  full_name: Spiegelman, Alexander
  last_name: Spiegelman
citation:
  ama: 'Kokoris Kogias E, Malkhi D, Spiegelman A. Asynchronous distributed key generation
    for computationally-secure randomness, consensus, and threshold signatures. In:
    <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications
    Security</i>. Association for Computing Machinery; 2020:1751–1767. doi:<a href="https://doi.org/10.1145/3372297.3423364">10.1145/3372297.3423364</a>'
  apa: 'Kokoris Kogias, E., Malkhi, D., &#38; Spiegelman, A. (2020). Asynchronous
    distributed key generation for computationally-secure randomness, consensus, and
    threshold signatures. In <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer
    and Communications Security</i> (pp. 1751–1767). Virtual, United States: Association
    for Computing Machinery. <a href="https://doi.org/10.1145/3372297.3423364">https://doi.org/10.1145/3372297.3423364</a>'
  chicago: Kokoris Kogias, Eleftherios, Dahlia Malkhi, and Alexander Spiegelman. “Asynchronous
    Distributed Key Generation for Computationally-Secure Randomness, Consensus, and
    Threshold Signatures.” In <i>Proceedings of the 2020 ACM SIGSAC Conference on
    Computer and Communications Security</i>, 1751–1767. Association for Computing
    Machinery, 2020. <a href="https://doi.org/10.1145/3372297.3423364">https://doi.org/10.1145/3372297.3423364</a>.
  ieee: E. Kokoris Kogias, D. Malkhi, and A. Spiegelman, “Asynchronous distributed
    key generation for computationally-secure randomness, consensus, and threshold
    signatures,” in <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and
    Communications Security</i>, Virtual, United States, 2020, pp. 1751–1767.
  ista: 'Kokoris Kogias E, Malkhi D, Spiegelman A. 2020. Asynchronous distributed
    key generation for computationally-secure randomness, consensus, and threshold
    signatures. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications
    Security. CCS: Computer and Communications Security, 1751–1767.'
  mla: Kokoris Kogias, Eleftherios, et al. “Asynchronous Distributed Key Generation
    for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” <i>Proceedings
    of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>,
    Association for Computing Machinery, 2020, pp. 1751–1767, doi:<a href="https://doi.org/10.1145/3372297.3423364">10.1145/3372297.3423364</a>.
  short: E. Kokoris Kogias, D. Malkhi, A. Spiegelman, in:, Proceedings of the 2020
    ACM SIGSAC Conference on Computer and Communications Security, Association for
    Computing Machinery, 2020, pp. 1751–1767.
conference:
  end_date: 2020-11-13
  location: Virtual, United States
  name: 'CCS: Computer and Communications Security'
  start_date: 2020-11-09
date_created: 2021-12-16T13:23:27Z
date_published: 2020-10-30T00:00:00Z
date_updated: 2024-02-22T13:10:45Z
day: '30'
department:
- _id: ElKo
doi: 10.1145/3372297.3423364
external_id:
  isi:
  - '000768470400104'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://eprint.iacr.org/2019/1015
month: '10'
oa: 1
oa_version: Preprint
page: 1751–1767
publication: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications
  Security
publication_identifier:
  isbn:
  - 978-1-4503-7089-9
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Asynchronous distributed key generation for computationally-secure randomness,
  consensus, and threshold signatures
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '10557'
abstract:
- lang: eng
  text: Data storage and retrieval systems, methods, and computer-readable media utilize
    a cryptographically verifiable data structure that facilitates verification of
    a transaction in a decentralized peer-to-peer environment using multi-hop backwards
    and forwards links. Backward links are cryptographic hashes of past records. Forward
    links are cryptographic signatures of future records that are added retroactively
    to records once the target block has been appended to the data structure.
applicant:
- Ecole Polytechnique Federale de Lausanne
application_date: 2017-06-09
article_processing_charge: No
author:
- first_name: Bryan
  full_name: Ford, Bryan
  last_name: Ford
- first_name: Linus
  full_name: Gasse, Linus
  last_name: Gasse
- first_name: Eleftherios
  full_name: Kokoris Kogias, Eleftherios
  id: f5983044-d7ef-11ea-ac6d-fd1430a26d30
  last_name: Kokoris Kogias
- first_name: Philipp
  full_name: Jovanovic, Philipp
  last_name: Jovanovic
citation:
  ama: Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. Cryptographically verifiable
    data structure having multi-hop forward and backwards links and associated systems
    and methods. 2020.
  apa: Ford, B., Gasse, L., Kokoris Kogias, E., &#38; Jovanovic, P. (2020). Cryptographically
    verifiable data structure having multi-hop forward and backwards links and associated
    systems and methods.
  chicago: Ford, Bryan, Linus Gasse, Eleftherios Kokoris Kogias, and Philipp Jovanovic.
    “Cryptographically Verifiable Data Structure Having Multi-Hop Forward and Backwards
    Links and Associated Systems and Methods,” 2020.
  ieee: B. Ford, L. Gasse, E. Kokoris Kogias, and P. Jovanovic, “Cryptographically
    verifiable data structure having multi-hop forward and backwards links and associated
    systems and methods.” 2020.
  ista: Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. 2020. Cryptographically verifiable
    data structure having multi-hop forward and backwards links and associated systems
    and methods.
  mla: Ford, Bryan, et al. <i>Cryptographically Verifiable Data Structure Having Multi-Hop
    Forward and Backwards Links and Associated Systems and Methods</i>. 2020.
  short: B. Ford, L. Gasse, E. Kokoris Kogias, P. Jovanovic, (2020).
date_created: 2021-12-16T13:28:59Z
date_published: 2020-03-03T00:00:00Z
date_updated: 2021-12-21T10:04:50Z
day: '03'
department:
- _id: ElKo
extern: '1'
ipc: ' H04L9/3247 ; G06Q20/29 ; G06Q20/382 ; H04L9/3236'
ipn: '10581613'
main_file_link:
- open_access: '1'
  url: https://patents.google.com/patent/US10581613B2/en
month: '03'
oa: 1
oa_version: Published Version
publication_date: 2020-03-03
related_material:
  link:
  - relation: earlier_version
    url: https://patents.google.com/patent/US20180359096A1/en
status: public
title: Cryptographically verifiable data structure having multi-hop forward and backwards
  links and associated systems and methods
type: patent
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '10672'
abstract:
- lang: eng
  text: The family of feedback alignment (FA) algorithms aims to provide a more biologically
    motivated alternative to backpropagation (BP), by substituting the computations
    that are unrealistic to be implemented in physical brains. While FA algorithms
    have been shown to work well in practice, there is a lack of rigorous theory proofing
    their learning capabilities. Here we introduce the first feedback alignment algorithm
    with provable learning guarantees. In contrast to existing work, we do not require
    any assumption about the size or depth of the network except that it has a single
    output neuron, i.e., such as for binary classification tasks. We show that our
    FA algorithm can deliver its theoretical promises in practice, surpassing the
    learning performance of existing FA methods and matching backpropagation in binary
    classification tasks. Finally, we demonstrate the limits of our FA variant when
    the number of output neurons grows beyond a certain quantity.
acknowledgement: "This research was supported in part by the Austrian Science Fund
  (FWF) under grant Z211-N23\r\n(Wittgenstein Award).\r\n"
article_processing_charge: No
author:
- first_name: Mathias
  full_name: Lechner, Mathias
  id: 3DC22916-F248-11E8-B48F-1D18A9856A87
  last_name: Lechner
citation:
  ama: 'Lechner M. Learning representations for binary-classification without backpropagation.
    In: <i>8th International Conference on Learning Representations</i>. ICLR; 2020.'
  apa: 'Lechner, M. (2020). Learning representations for binary-classification without
    backpropagation. In <i>8th International Conference on Learning Representations</i>.
    Virtual ; Addis Ababa, Ethiopia: ICLR.'
  chicago: Lechner, Mathias. “Learning Representations for Binary-Classification without
    Backpropagation.” In <i>8th International Conference on Learning Representations</i>.
    ICLR, 2020.
  ieee: M. Lechner, “Learning representations for binary-classification without backpropagation,”
    in <i>8th International Conference on Learning Representations</i>, Virtual ;
    Addis Ababa, Ethiopia, 2020.
  ista: 'Lechner M. 2020. Learning representations for binary-classification without
    backpropagation. 8th International Conference on Learning Representations. ICLR:
    International Conference on Learning Representations.'
  mla: Lechner, Mathias. “Learning Representations for Binary-Classification without
    Backpropagation.” <i>8th International Conference on Learning Representations</i>,
    ICLR, 2020.
  short: M. Lechner, in:, 8th International Conference on Learning Representations,
    ICLR, 2020.
conference:
  end_date: 2020-05-01
  location: Virtual ; Addis Ababa, Ethiopia
  name: 'ICLR: International Conference on Learning Representations'
  start_date: 2020-04-26
date_created: 2022-01-25T15:50:00Z
date_published: 2020-03-11T00:00:00Z
date_updated: 2023-04-03T07:33:40Z
day: '11'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
  checksum: ea13d42dd4541ddb239b6a75821fd6c9
  content_type: application/pdf
  creator: mlechner
  date_created: 2022-01-26T07:35:17Z
  date_updated: 2022-01-26T07:35:17Z
  file_id: '10677'
  file_name: iclr_2020.pdf
  file_size: 249431
  relation: main_file
  success: 1
file_date_updated: 2022-01-26T07:35:17Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/3.0/
main_file_link:
- open_access: '1'
  url: https://openreview.net/forum?id=Bke61krFvS
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: Z211
  name: The Wittgenstein Prize
publication: 8th International Conference on Learning Representations
publication_status: published
publisher: ICLR
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning representations for binary-classification without backpropagation
tmp:
  image: /images/cc_by_nc_nd.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
  name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
    3.0)
  short: CC BY-NC-ND (3.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '10673'
abstract:
- lang: eng
  text: We propose a neural information processing system obtained by re-purposing
    the function of a biological neural circuit model to govern simulated and real-world
    control tasks. Inspired by the structure of the nervous system of the soil-worm,
    C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model
    of biological neural circuits reparameterized for the control of alternative tasks.
    We first demonstrate that ONCs realize networks with higher maximum flow compared
    to arbitrary wired networks. We then learn instances of ONCs to control a series
    of robotic tasks, including the autonomous parking of a real-world rover robot.
    For reconfiguration of the purpose of the neural circuit, we adopt a search-based
    optimization algorithm. Ordinary neural circuits perform on par and, in some cases,
    significantly surpass the performance of contemporary deep learning models. ONC
    networks are compact, 77% sparser than their counterpart neural controllers, and
    their neural dynamics are fully interpretable at the cell-level.
acknowledgement: "RH and RG are partially supported by Horizon-2020 ECSEL Project
  grant No. 783163 (iDev40), Productive 4.0, and ATBMBFW CPS-IoT Ecosystem. ML was
  supported in part by the Austrian Science Fund (FWF) under grant Z211-N23\r\n(Wittgenstein
  Award). AA is supported by the National Science Foundation (NSF) Graduate Research
  Fellowship\r\nProgram. RH and DR are partially supported by The Boeing Company and
  JP Morgan Chase. This research work is\r\npartially drawn from the PhD dissertation
  of RH.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Ramin
  full_name: Hasani, Ramin
  last_name: Hasani
- first_name: Mathias
  full_name: Lechner, Mathias
  id: 3DC22916-F248-11E8-B48F-1D18A9856A87
  last_name: Lechner
- first_name: Alexander
  full_name: Amini, Alexander
  last_name: Amini
- first_name: Daniela
  full_name: Rus, Daniela
  last_name: Rus
- first_name: Radu
  full_name: Grosu, Radu
  last_name: Grosu
citation:
  ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. A natural lottery ticket winner:
    Reinforcement learning with ordinary neural circuits. In: <i>Proceedings of the
    37th International Conference on Machine Learning</i>. PMLR. ; 2020:4082-4093.'
  apa: 'Hasani, R., Lechner, M., Amini, A., Rus, D., &#38; Grosu, R. (2020). A natural
    lottery ticket winner: Reinforcement learning with ordinary neural circuits. In
    <i>Proceedings of the 37th International Conference on Machine Learning</i> (pp.
    4082–4093). Virtual.'
  chicago: 'Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu
    Grosu. “A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary
    Neural Circuits.” In <i>Proceedings of the 37th International Conference on Machine
    Learning</i>, 4082–93. PMLR, 2020.'
  ieee: 'R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “A natural lottery
    ticket winner: Reinforcement learning with ordinary neural circuits,” in <i>Proceedings
    of the 37th International Conference on Machine Learning</i>, Virtual, 2020, pp.
    4082–4093.'
  ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2020. A natural lottery ticket
    winner: Reinforcement learning with ordinary neural circuits. Proceedings of the
    37th International Conference on Machine Learning. ML: Machine LearningPMLR, PMLR,
    , 4082–4093.'
  mla: 'Hasani, Ramin, et al. “A Natural Lottery Ticket Winner: Reinforcement Learning
    with Ordinary Neural Circuits.” <i>Proceedings of the 37th International Conference
    on Machine Learning</i>, 2020, pp. 4082–93.'
  short: R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the
    37th International Conference on Machine Learning, 2020, pp. 4082–4093.
conference:
  end_date: 2020-07-18
  location: Virtual
  name: 'ML: Machine Learning'
  start_date: 2020-07-12
date_created: 2022-01-25T15:50:34Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2022-01-26T11:14:27Z
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
  checksum: c9a4a29161777fc1a89ef451c040e3b1
  content_type: application/pdf
  creator: cchlebak
  date_created: 2022-01-26T11:08:51Z
  date_updated: 2022-01-26T11:08:51Z
  file_id: '10691'
  file_name: 2020_PMLR_Hasani.pdf
  file_size: 2329798
  relation: main_file
  success: 1
file_date_updated: 2022-01-26T11:08:51Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://proceedings.mlr.press/v119/hasani20a.html
oa: 1
oa_version: Published Version
page: 4082-4093
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: Z211
  name: The Wittgenstein Prize
publication: Proceedings of the 37th International Conference on Machine Learning
publication_identifier:
  issn:
  - 2640-3498
publication_status: published
quality_controlled: '1'
scopus_import: '1'
series_title: PMLR
status: public
title: 'A natural lottery ticket winner: Reinforcement learning with ordinary neural
  circuits'
tmp:
  image: /images/cc_by_nc_nd.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
  name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
    3.0)
  short: CC BY-NC-ND (3.0)
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
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: <i>37th International Conference
    on Machine Learning, ICML 2020</i>. 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 <i>37th International Conference on Machine Learning, ICML
    2020</i> (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 <i>37th International Conference
    on Machine Learning, ICML 2020</i>, 119:5533–43, 2020.
  ieee: M. Kurtz <i>et al.</i>, “Inducing and exploiting activation sparsity for fast
    neural network inference,” in <i>37th International Conference on Machine Learning,
    ICML 2020</i>, 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.” <i>37th International Conference on Machine Learning, ICML
    2020</i>, 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.
    <i>Molecular Cell</i>. 2020;77(2):310-323.e7. doi:<a href="https://doi.org/10.1016/j.molcel.2019.10.011">10.1016/j.molcel.2019.10.011</a>
  apa: Choi, J., Lyons, D. B., Kim, M. Y., Moore, J. D., &#38; Zilberman, D. (2020).
    DNA methylation and histone H1 jointly repress transposable elements and aberrant
    intragenic transcripts. <i>Molecular Cell</i>. Elsevier. <a href="https://doi.org/10.1016/j.molcel.2019.10.011">https://doi.org/10.1016/j.molcel.2019.10.011</a>
  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.” <i>Molecular Cell</i>. Elsevier, 2020. <a
    href="https://doi.org/10.1016/j.molcel.2019.10.011">https://doi.org/10.1016/j.molcel.2019.10.011</a>.
  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,”
    <i>Molecular Cell</i>, 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.” <i>Molecular Cell</i>, vol. 77,
    no. 2, Elsevier, 2020, p. 310–323.e7, doi:<a href="https://doi.org/10.1016/j.molcel.2019.10.011">10.1016/j.molcel.2019.10.011</a>.
  short: J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77
    (2020) 310–323.e7.
date_created: 2021-06-08T06:37:09Z
date_published: 2020-01-16T00:00:00Z
date_updated: 2021-12-14T07:51:15Z
day: '16'
department:
- _id: DaZi
doi: 10.1016/j.molcel.2019.10.011
extern: '1'
external_id:
  pmid:
  - '31732458'
intvolume: '        77'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1016/j.molcel.2019.10.011
month: '01'
oa: 1
oa_version: Published Version
page: 310-323.e7
pmid: 1
publication: Molecular Cell
publication_identifier:
  eissn:
  - 1097-4164
  issn:
  - 1097-2765
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: DNA methylation and histone H1 jointly repress transposable elements and aberrant
  intragenic transcripts
type: journal_article
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 77
year: '2020'
...
---
_id: '9630'
abstract:
- lang: eng
  text: Various kinds of data are routinely represented as discrete probability distributions.
    Examples include text documents summarized by histograms of word occurrences and
    images represented as histograms of oriented gradients. Viewing a discrete probability
    distribution as a point in the standard simplex of the appropriate dimension,
    we can understand collections of such objects in geometric and topological terms.  Importantly,
    instead of using the standard Euclidean distance, we look into dissimilarity measures
    with information-theoretic justification, and we develop the theory needed for
    applying topological data analysis in this setting. In doing so, we emphasize
    constructions that enable the usage of existing computational topology software
    in this context.
acknowledgement: This research is partially supported by the Office of Naval Research,
  through grant no. N62909-18-1-2038, and the DFG Collaborative Research Center TRR
  109, ‘Discretization in Geometry and Dynamics’, through grant no. I02979-N35 of
  the Austrian Science Fund (FWF).
article_processing_charge: Yes
article_type: original
author:
- first_name: Herbert
  full_name: Edelsbrunner, Herbert
  id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
  last_name: Edelsbrunner
  orcid: 0000-0002-9823-6833
- first_name: Ziga
  full_name: Virk, Ziga
  id: 2E36B656-F248-11E8-B48F-1D18A9856A87
  last_name: Virk
- first_name: Hubert
  full_name: Wagner, Hubert
  id: 379CA8B8-F248-11E8-B48F-1D18A9856A87
  last_name: Wagner
citation:
  ama: Edelsbrunner H, Virk Z, Wagner H. Topological data analysis in information
    space. <i>Journal of Computational Geometry</i>. 2020;11(2):162-182. doi:<a href="https://doi.org/10.20382/jocg.v11i2a7">10.20382/jocg.v11i2a7</a>
  apa: Edelsbrunner, H., Virk, Z., &#38; Wagner, H. (2020). Topological data analysis
    in information space. <i>Journal of Computational Geometry</i>. Carleton University.
    <a href="https://doi.org/10.20382/jocg.v11i2a7">https://doi.org/10.20382/jocg.v11i2a7</a>
  chicago: Edelsbrunner, Herbert, Ziga Virk, and Hubert Wagner. “Topological Data
    Analysis in Information Space.” <i>Journal of Computational Geometry</i>. Carleton
    University, 2020. <a href="https://doi.org/10.20382/jocg.v11i2a7">https://doi.org/10.20382/jocg.v11i2a7</a>.
  ieee: H. Edelsbrunner, Z. Virk, and H. Wagner, “Topological data analysis in information
    space,” <i>Journal of Computational Geometry</i>, 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.”
    <i>Journal of Computational Geometry</i>, vol. 11, no. 2, Carleton University,
    2020, pp. 162–82, doi:<a href="https://doi.org/10.20382/jocg.v11i2a7">10.20382/jocg.v11i2a7</a>.
  short: H. Edelsbrunner, Z. Virk, H. Wagner, Journal of Computational Geometry 11
    (2020) 162–182.
date_created: 2021-07-04T22:01:26Z
date_published: 2020-12-14T00:00:00Z
date_updated: 2021-08-11T12:26:34Z
day: '14'
ddc:
- '510'
- '000'
department:
- _id: HeEd
doi: 10.20382/jocg.v11i2a7
file:
- access_level: open_access
  checksum: f02d0b2b3838e7891a6c417fc34ffdcd
  content_type: application/pdf
  creator: asandaue
  date_created: 2021-08-11T11:55:11Z
  date_updated: 2021-08-11T11:55:11Z
  file_id: '9882'
  file_name: 2020_JournalOfComputationalGeometry_Edelsbrunner.pdf
  file_size: 1449234
  relation: main_file
  success: 1
file_date_updated: 2021-08-11T11:55:11Z
has_accepted_license: '1'
intvolume: '        11'
issue: '2'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/3.0/
month: '12'
oa: 1
oa_version: Published Version
page: 162-182
project:
- _id: 0aa4bc98-070f-11eb-9043-e6fff9c6a316
  grant_number: I4887
  name: Discretization in Geometry and Dynamics
publication: Journal of Computational Geometry
publication_identifier:
  eissn:
  - 1920180X
publication_status: published
publisher: Carleton University
quality_controlled: '1'
scopus_import: '1'
status: public
title: Topological data analysis in information space
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
  name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
  short: CC BY (3.0)
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 11
year: '2020'
...
---
_id: '9631'
abstract:
- lang: eng
  text: The ability to leverage large-scale hardware parallelism has been one of the
    key enablers of the accelerated recent progress in machine learning. Consequently,
    there has been considerable effort invested into developing efficient parallel
    variants of classic machine learning algorithms. However, despite the wealth of
    knowledge on parallelization, some classic machine learning algorithms often prove
    hard to parallelize efficiently while maintaining convergence. In this paper,
    we focus on efficient parallel algorithms for the key machine learning task of
    inference on graphical models, in particular on the fundamental belief propagation
    algorithm. We address the challenge of efficiently parallelizing this classic
    paradigm by showing how to leverage scalable relaxed schedulers in this context.
    We present an extensive empirical study, showing that our approach outperforms
    previous parallel belief propagation implementations both in terms of scalability
    and in terms of wall-clock convergence time, on a range of practical applications.
acknowledgement: "We thank Marco Mondelli for discussions related to LDPC decoding,
  and Giorgi Nadiradze for discussions on analysis of relaxed schedulers. This project
  has received funding from the European Research Council (ERC) under the European\r\nUnion’s
  Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML)."
article_processing_charge: No
arxiv: 1
author:
- first_name: Vitaly
  full_name: Aksenov, Vitaly
  last_name: Aksenov
- first_name: Dan-Adrian
  full_name: Alistarh, Dan-Adrian
  id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
  last_name: Alistarh
  orcid: 0000-0003-3650-940X
- first_name: Janne
  full_name: Korhonen, Janne
  id: C5402D42-15BC-11E9-A202-CA2BE6697425
  last_name: Korhonen
citation:
  ama: 'Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed
    scheduling. In: <i>Advances in Neural Information Processing Systems</i>. Vol
    33. Curran Associates; 2020:22361-22372.'
  apa: 'Aksenov, V., Alistarh, D.-A., &#38; Korhonen, J. (2020). Scalable belief propagation
    via relaxed scheduling. In <i>Advances in Neural Information Processing Systems</i>
    (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 <i>Advances in Neural Information Processing
    Systems</i>, 33:22361–72. Curran Associates, 2020.
  ieee: V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation
    via relaxed scheduling,” in <i>Advances in Neural Information Processing Systems</i>,
    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.”
    <i>Advances in Neural Information Processing Systems</i>, 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: <i>Advances in Neural Information Processing Systems</i>.
    Vol 33. Curran Associates; 2020:18098-18109.'
  apa: 'Singh, S. P., &#38; Alistarh, D.-A. (2020). WoodFisher: Efficient second-order
    approximation for neural network compression. In <i>Advances in Neural Information
    Processing Systems</i> (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 <i>Advances in Neural Information
    Processing Systems</i>, 33:18098–109. Curran Associates, 2020.'
  ieee: 'S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation
    for neural network compression,” in <i>Advances in Neural Information Processing
    Systems</i>, 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.” <i>Advances in Neural Information
    Processing Systems</i>, 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: <i>Advances in Neural Information Processing Systems</i>.
    Vol 33. ; 2020:16398-16408.'
  apa: Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., &#38; Vogels, T.
    P. (2020). A meta-learning approach to (re)discover plasticity rules that carve
    a desired function into a neural network. In <i>Advances in Neural Information
    Processing Systems</i> (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 <i>Advances in Neural Information
    Processing Systems</i>, 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 <i>Advances in Neural Information Processing Systems</i>,
    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.” <i>Advances in Neural
    Information Processing Systems</i>, vol. 33, 2020, pp. 16398–408.
  short: B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances
    in Neural Information Processing Systems, 2020, pp. 16398–16408.
conference:
  end_date: 2020-12-12
  location: Vancouver, Canada
  name: 'NeurIPS: Conference on Neural Information Processing Systems'
  start_date: 2020-12-06
date_created: 2021-07-04T22:01:27Z
date_published: 2020-12-06T00:00:00Z
date_updated: 2023-10-18T09:20:55Z
day: '06'
department:
- _id: TiVo
ec_funded: 1
intvolume: '        33'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
page: 16398-16408
project:
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
  grant_number: 214316/Z/18/Z
  name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
    neuronal networks.
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
  call_identifier: H2020
  grant_number: '819603'
  name: Learning the shape of synaptic plasticity rules for neuronal architectures
    and function through machine learning.
publication: Advances in Neural Information Processing Systems
publication_identifier:
  issn:
  - 1049-5258
publication_status: published
quality_controlled: '1'
related_material:
  link:
  - relation: is_continued_by
    url: https://doi.org/10.1101/2020.10.24.353409
  record:
  - id: '14422'
    relation: dissertation_contains
    status: public
scopus_import: '1'
status: public
title: A meta-learning approach to (re)discover plasticity rules that carve a desired
  function into a neural network
type: conference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 33
year: '2020'
...
---
_id: '9706'
abstract:
- lang: eng
  text: 'Additional file 2: Supplementary Tables. The association of pre-adjusted
    protein levels with biological and technical covariates. Protein levels were adjusted
    for age, sex, array plate and four genetic principal components (population structure)
    prior to analyses. Significant associations are emboldened. (Table S1). pQTLs
    associated with inflammatory biomarker levels from Bayesian penalised regression
    model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated
    with inflammatory biomarker levels from ordinary least squares regression model
    (P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary
    least squares GWAS and EWAS performed on inflammatory protein levels (n = 70)
    in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs
    associated with inflammatory biomarker levels from ordinary least squares regression
    model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary
    least squares and Bayesian penalised regression models for concordantly identified
    SNPs. (Table S6). Estimate of heritability for blood protein levels as well as
    proportion of variance explained attributable to different prior mixtures. (Table
    S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood)
    and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant
    SNPs identified by linear model and Bayesian penalised regression and whether
    they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation
    for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression
    are putatively associated with circulating inflammatory proteins that harbour
    pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified
    by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table
    S12). CpGs associated with inflammatory protein biomarkers as identified by linear
    model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory
    protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10.
    (Table S14). Estimate of variance explained for blood protein levels by DNA methylation
    as well as proportion of explained attributable to different prior mixtures -
    BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide
    DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression
    model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker
    levels explained by common genetic and methylation data (joint and conditional
    estimates from BayesR+). Ordered by combined variance explained by genetic and
    epigenetic data - smallest to largest. Significant results from t-tests comparing
    distributions for variance explained by methylation or genetics alone versus combined
    estimate are emboldened. (Table S17). Genetic and epigenetic factors identified
    by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian
    Randomisation analyses to assess whether proteins with concordantly identified
    genetic signals are causally associated with Alzheimer’s disease risk. (Table
    S19).'
article_processing_charge: No
author:
- first_name: Robert F.
  full_name: Hillary, Robert F.
  last_name: Hillary
- first_name: Daniel
  full_name: Trejo-Banos, Daniel
  last_name: Trejo-Banos
- first_name: Athanasios
  full_name: Kousathanas, Athanasios
  last_name: Kousathanas
- first_name: Daniel L.
  full_name: McCartney, Daniel L.
  last_name: McCartney
- first_name: Sarah E.
  full_name: Harris, Sarah E.
  last_name: Harris
- first_name: Anna J.
  full_name: Stevenson, Anna J.
  last_name: Stevenson
- first_name: Marion
  full_name: Patxot, Marion
  last_name: Patxot
- first_name: Sven Erik
  full_name: Ojavee, Sven Erik
  last_name: Ojavee
- first_name: Qian
  full_name: Zhang, Qian
  last_name: Zhang
- first_name: David C.
  full_name: Liewald, David C.
  last_name: Liewald
- first_name: Craig W.
  full_name: Ritchie, Craig W.
  last_name: Ritchie
- first_name: Kathryn L.
  full_name: Evans, Kathryn L.
  last_name: Evans
- first_name: Elliot M.
  full_name: Tucker-Drob, Elliot M.
  last_name: Tucker-Drob
- first_name: Naomi R.
  full_name: Wray, Naomi R.
  last_name: Wray
- first_name: 'Allan F. '
  full_name: 'McRae, Allan F. '
  last_name: McRae
- first_name: Peter M.
  full_name: Visscher, Peter M.
  last_name: Visscher
- first_name: Ian J.
  full_name: Deary, Ian J.
  last_name: Deary
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
- first_name: 'Riccardo E. '
  full_name: 'Marioni, Riccardo E. '
  last_name: Marioni
citation:
  ama: Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method
    genome- and epigenome-wide studies of inflammatory protein levels in healthy older
    adults. 2020. doi:<a href="https://doi.org/10.6084/m9.figshare.12629697.v1">10.6084/m9.figshare.12629697.v1</a>
  apa: Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris,
    S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method
    genome- and epigenome-wide studies of inflammatory protein levels in healthy older
    adults. Springer Nature. <a href="https://doi.org/10.6084/m9.figshare.12629697.v1">https://doi.org/10.6084/m9.figshare.12629697.v1</a>
  chicago: Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel
    L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional
    File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein
    Levels in Healthy Older Adults.” Springer Nature, 2020. <a href="https://doi.org/10.6084/m9.figshare.12629697.v1">https://doi.org/10.6084/m9.figshare.12629697.v1</a>.
  ieee: R. F. Hillary <i>et al.</i>, “Additional file 2 of multi-method genome- and
    epigenome-wide studies of inflammatory protein levels in healthy older adults.”
    Springer Nature, 2020.
  ista: Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson
    AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob
    EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional
    file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein
    levels in healthy older adults, Springer Nature, <a href="https://doi.org/10.6084/m9.figshare.12629697.v1">10.6084/m9.figshare.12629697.v1</a>.
  mla: Hillary, Robert F., et al. <i>Additional File 2 of Multi-Method Genome- and
    Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults</i>.
    Springer Nature, 2020, doi:<a href="https://doi.org/10.6084/m9.figshare.12629697.v1">10.6084/m9.figshare.12629697.v1</a>.
  short: R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. McCartney, S.E. Harris,
    A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie,
    K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. McRae, P.M. Visscher, I.J. Deary,
    M.R. Robinson, R.E. Marioni, (2020).
date_created: 2021-07-23T08:59:15Z
date_published: 2020-07-09T00:00:00Z
date_updated: 2023-08-22T07:55:36Z
day: '09'
department:
- _id: MaRo
doi: 10.6084/m9.figshare.12629697.v1
has_accepted_license: '1'
main_file_link:
- open_access: '1'
  url: https://doi.org/10.6084/m9.figshare.12629697.v1
month: '07'
oa: 1
oa_version: Published Version
other_data_license: CC0 + CC BY (4.0)
publisher: Springer Nature
related_material:
  record:
  - id: '8133'
    relation: used_in_publication
    status: public
status: public
title: Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory
  protein levels in healthy older adults
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9708'
abstract:
- lang: eng
  text: This research data supports 'Hard antinodal gap revealed by quantum oscillations
    in the pseudogap regime of underdoped high-Tc superconductors'. A Readme file
    for plotting each figure is provided.
article_processing_charge: No
author:
- first_name: Mate
  full_name: Hartstein, Mate
  last_name: Hartstein
- first_name: Yu-Te
  full_name: Hsu, Yu-Te
  last_name: Hsu
- first_name: Kimberly A
  full_name: Modic, Kimberly A
  id: 13C26AC0-EB69-11E9-87C6-5F3BE6697425
  last_name: Modic
  orcid: 0000-0001-9760-3147
- first_name: Juan
  full_name: Porras, Juan
  last_name: Porras
- first_name: Toshinao
  full_name: Loew, Toshinao
  last_name: Loew
- first_name: Matthieu
  full_name: Le Tacon, Matthieu
  last_name: Le Tacon
- first_name: Huakun
  full_name: Zuo, Huakun
  last_name: Zuo
- first_name: Jinhua
  full_name: Wang, Jinhua
  last_name: Wang
- first_name: Zengwei
  full_name: Zhu, Zengwei
  last_name: Zhu
- first_name: Mun
  full_name: Chan, Mun
  last_name: Chan
- first_name: Ross
  full_name: McDonald, Ross
  last_name: McDonald
- first_name: Gilbert
  full_name: Lonzarich, Gilbert
  last_name: Lonzarich
- first_name: Bernhard
  full_name: Keimer, Bernhard
  last_name: Keimer
- first_name: Suchitra
  full_name: Sebastian, Suchitra
  last_name: Sebastian
- first_name: Neil
  full_name: Harrison, Neil
  last_name: Harrison
citation:
  ama: Hartstein M, Hsu Y-T, Modic KA, et al. Accompanying dataset for “Hard antinodal
    gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc
    superconductors.” 2020. doi:<a href="https://doi.org/10.17863/cam.50169">10.17863/cam.50169</a>
  apa: Hartstein, M., Hsu, Y.-T., Modic, K. A., Porras, J., Loew, T., Le Tacon, M.,
    … Harrison, N. (2020). Accompanying dataset for “Hard antinodal gap revealed by
    quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.”
    Apollo - University of Cambridge. <a href="https://doi.org/10.17863/cam.50169">https://doi.org/10.17863/cam.50169</a>
  chicago: Hartstein, Mate, Yu-Te Hsu, Kimberly A Modic, Juan Porras, Toshinao Loew,
    Matthieu Le Tacon, Huakun Zuo, et al. “Accompanying Dataset for ‘Hard Antinodal
    Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc
    Superconductors.’” Apollo - University of Cambridge, 2020. <a href="https://doi.org/10.17863/cam.50169">https://doi.org/10.17863/cam.50169</a>.
  ieee: M. Hartstein <i>et al.</i>, “Accompanying dataset for ‘Hard antinodal gap
    revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc
    superconductors.’” Apollo - University of Cambridge, 2020.
  ista: Hartstein M, Hsu Y-T, Modic KA, Porras J, Loew T, Le Tacon M, Zuo H, Wang
    J, Zhu Z, Chan M, McDonald R, Lonzarich G, Keimer B, Sebastian S, Harrison N.
    2020. Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations
    in the pseudogap regime of underdoped high-Tc superconductors’, Apollo - University
    of Cambridge, <a href="https://doi.org/10.17863/cam.50169">10.17863/cam.50169</a>.
  mla: Hartstein, Mate, et al. <i>Accompanying Dataset for “Hard Antinodal Gap Revealed
    by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.”</i>
    Apollo - University of Cambridge, 2020, doi:<a href="https://doi.org/10.17863/cam.50169">10.17863/cam.50169</a>.
  short: M. Hartstein, Y.-T. Hsu, K.A. Modic, J. Porras, T. Loew, M. Le Tacon, H.
    Zuo, J. Wang, Z. Zhu, M. Chan, R. McDonald, G. Lonzarich, B. Keimer, S. Sebastian,
    N. Harrison, (2020).
date_created: 2021-07-23T10:00:35Z
date_published: 2020-05-29T00:00:00Z
date_updated: 2023-08-21T07:06:48Z
day: '29'
department:
- _id: KiMo
doi: 10.17863/cam.50169
has_accepted_license: '1'
main_file_link:
- open_access: '1'
  url: https://doi.org/10.17863/CAM.50169
month: '05'
oa: 1
oa_version: Published Version
publisher: Apollo - University of Cambridge
related_material:
  record:
  - id: '7942'
    relation: used_in_publication
    status: public
status: public
title: Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations
  in the pseudogap regime of underdoped high-Tc superconductors'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9713'
abstract:
- lang: eng
  text: Additional analyses of the trajectories
article_processing_charge: No
author:
- first_name: Chitrak
  full_name: Gupta, Chitrak
  last_name: Gupta
- first_name: Umesh
  full_name: Khaniya, Umesh
  last_name: Khaniya
- first_name: Chun Kit
  full_name: Chan, Chun Kit
  last_name: Chan
- first_name: Francois
  full_name: Dehez, Francois
  last_name: Dehez
- first_name: Mrinal
  full_name: Shekhar, Mrinal
  last_name: Shekhar
- first_name: M.R.
  full_name: Gunner, M.R.
  last_name: Gunner
- first_name: Leonid A
  full_name: Sazanov, Leonid A
  id: 338D39FE-F248-11E8-B48F-1D18A9856A87
  last_name: Sazanov
  orcid: 0000-0002-0977-7989
- first_name: Christophe
  full_name: Chipot, Christophe
  last_name: Chipot
- first_name: Abhishek
  full_name: Singharoy, Abhishek
  last_name: Singharoy
citation:
  ama: Gupta C, Khaniya U, Chan CK, et al. Supporting information. 2020. doi:<a href="https://doi.org/10.1021/jacs.9b13450.s001">10.1021/jacs.9b13450.s001</a>
  apa: Gupta, C., Khaniya, U., Chan, C. K., Dehez, F., Shekhar, M., Gunner, M. R.,
    … Singharoy, A. (2020). Supporting information. American Chemical Society . <a
    href="https://doi.org/10.1021/jacs.9b13450.s001">https://doi.org/10.1021/jacs.9b13450.s001</a>
  chicago: Gupta, Chitrak, Umesh Khaniya, Chun Kit Chan, Francois Dehez, Mrinal Shekhar,
    M.R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Supporting
    Information.” American Chemical Society , 2020. <a href="https://doi.org/10.1021/jacs.9b13450.s001">https://doi.org/10.1021/jacs.9b13450.s001</a>.
  ieee: C. Gupta <i>et al.</i>, “Supporting information.” American Chemical Society
    , 2020.
  ista: Gupta C, Khaniya U, Chan CK, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot
    C, Singharoy A. 2020. Supporting information, American Chemical Society , <a href="https://doi.org/10.1021/jacs.9b13450.s001">10.1021/jacs.9b13450.s001</a>.
  mla: Gupta, Chitrak, et al. <i>Supporting Information</i>. American Chemical Society
    , 2020, doi:<a href="https://doi.org/10.1021/jacs.9b13450.s001">10.1021/jacs.9b13450.s001</a>.
  short: C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A.
    Sazanov, C. Chipot, A. Singharoy, (2020).
date_created: 2021-07-23T12:02:39Z
date_published: 2020-05-20T00:00:00Z
date_updated: 2023-08-22T07:49:38Z
day: '20'
department:
- _id: LeSa
doi: 10.1021/jacs.9b13450.s001
month: '05'
oa_version: Published Version
publisher: 'American Chemical Society '
related_material:
  record:
  - id: '8040'
    relation: used_in_publication
    status: public
status: public
title: Supporting information
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9750'
abstract:
- lang: eng
  text: Tension of the actomyosin cell cortex plays a key role in determining cell-cell
    contact growth and size. The level of cortical tension outside of the cell-cell
    contact, when pulling at the contact edge, scales with the total size to which
    a cell-cell contact can grow1,2. Here we show in zebrafish primary germ layer
    progenitor cells that this monotonic relationship only applies to a narrow range
    of cortical tension increase, and that above a critical threshold, contact size
    inversely scales with cortical tension. This switch from cortical tension increasing
    to decreasing progenitor cell-cell contact size is caused by cortical tension
    promoting E-cadherin anchoring to the actomyosin cytoskeleton, thereby increasing
    clustering and stability of E-cadherin at the contact. Once tension-mediated E-cadherin
    stabilization at the contact exceeds a critical threshold level, the rate by which
    the contact expands in response to pulling forces from the cortex sharply drops,
    leading to smaller contacts at physiologically relevant timescales of contact
    formation. Thus, the activity of cortical tension in expanding cell-cell contact
    size is limited by tension stabilizing E-cadherin-actin complexes at the contact.
acknowledged_ssus:
- _id: Bio
- _id: EM-Fac
- _id: SSU
acknowledgement: We would like to thank Edouard Hannezo for discussions, Shayan Shami
  Pour and Daniel Capek for help with data analysis, Vanessa Barone and other members
  of the Heisenberg laboratory for thoughtful discussions and comments on the manuscript.
  We also thank Jack Merrin for preparing the microwells, and the Scientific Service
  Units at IST Austria, specifically Bioimaging and Electron Microscopy, and the Zebrafish
  Facility for continuous support. We acknowledge Hitoshi Morita for the kind gift
  of VinculinB-GFP plasmid. This research was supported by an ERC Advanced Grant (MECSPEC)
  to C.-P.H, EMBO Long Term grant (ALTF 187-2013) to M.S and IST Fellow Marie-Curie
  COFUND No. P_IST_EU01 to J.S.
article_processing_charge: No
author:
- first_name: Jana
  full_name: Slovakova, Jana
  id: 30F3F2F0-F248-11E8-B48F-1D18A9856A87
  last_name: Slovakova
- first_name: Mateusz K
  full_name: Sikora, Mateusz K
  id: 2F74BCDE-F248-11E8-B48F-1D18A9856A87
  last_name: Sikora
- first_name: Silvia
  full_name: Caballero Mancebo, Silvia
  id: 2F1E1758-F248-11E8-B48F-1D18A9856A87
  last_name: Caballero Mancebo
  orcid: 0000-0002-5223-3346
- first_name: Gabriel
  full_name: Krens, Gabriel
  id: 2B819732-F248-11E8-B48F-1D18A9856A87
  last_name: Krens
  orcid: 0000-0003-4761-5996
- first_name: Walter
  full_name: Kaufmann, Walter
  id: 3F99E422-F248-11E8-B48F-1D18A9856A87
  last_name: Kaufmann
  orcid: 0000-0001-9735-5315
- first_name: Karla
  full_name: Huljev, Karla
  id: 44C6F6A6-F248-11E8-B48F-1D18A9856A87
  last_name: Huljev
- first_name: Carl-Philipp J
  full_name: Heisenberg, Carl-Philipp J
  id: 39427864-F248-11E8-B48F-1D18A9856A87
  last_name: Heisenberg
  orcid: 0000-0002-0912-4566
citation:
  ama: Slovakova J, Sikora MK, Caballero Mancebo S, et al. Tension-dependent stabilization
    of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. 2020. doi:<a
    href="https://doi.org/10.1101/2020.11.20.391284">10.1101/2020.11.20.391284</a>
  apa: Slovakova, J., Sikora, M. K., Caballero Mancebo, S., Krens, G., Kaufmann, W.,
    Huljev, K., &#38; Heisenberg, C.-P. J. (2020). Tension-dependent stabilization
    of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. Cold Spring
    Harbor Laboratory. <a href="https://doi.org/10.1101/2020.11.20.391284">https://doi.org/10.1101/2020.11.20.391284</a>
  chicago: Slovakova, Jana, Mateusz K Sikora, Silvia Caballero Mancebo, Gabriel Krens,
    Walter Kaufmann, Karla Huljev, and Carl-Philipp J Heisenberg. “Tension-Dependent
    Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” <i>BioRxiv</i>.
    Cold Spring Harbor Laboratory, 2020. <a href="https://doi.org/10.1101/2020.11.20.391284">https://doi.org/10.1101/2020.11.20.391284</a>.
  ieee: J. Slovakova <i>et al.</i>, “Tension-dependent stabilization of E-cadherin
    limits cell-cell contact expansion,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory,
    2020.
  ista: Slovakova J, Sikora MK, Caballero Mancebo S, Krens G, Kaufmann W, Huljev K,
    Heisenberg C-PJ. 2020. Tension-dependent stabilization of E-cadherin limits cell-cell
    contact expansion. bioRxiv, <a href="https://doi.org/10.1101/2020.11.20.391284">10.1101/2020.11.20.391284</a>.
  mla: Slovakova, Jana, et al. “Tension-Dependent Stabilization of E-Cadherin Limits
    Cell-Cell Contact Expansion.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, 2020,
    doi:<a href="https://doi.org/10.1101/2020.11.20.391284">10.1101/2020.11.20.391284</a>.
  short: J. Slovakova, M.K. Sikora, S. Caballero Mancebo, G. Krens, W. Kaufmann, K.
    Huljev, C.-P.J. Heisenberg, BioRxiv (2020).
date_created: 2021-07-29T11:29:50Z
date_published: 2020-11-20T00:00:00Z
date_updated: 2024-03-25T23:30:10Z
day: '20'
department:
- _id: CaHe
- _id: EM-Fac
- _id: Bio
doi: 10.1101/2020.11.20.391284
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1101/2020.11.20.391284
month: '11'
oa: 1
oa_version: Preprint
page: '41'
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
- _id: 260F1432-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '742573'
  name: Interaction and feedback between cell mechanics and fate specification in
    vertebrate gastrulation
- _id: 2521E28E-B435-11E9-9278-68D0E5697425
  grant_number: 187-2013
  name: Modulation of adhesion function in cell-cell contact formation by cortical
    tension
publication: bioRxiv
publication_status: published
publisher: Cold Spring Harbor Laboratory
related_material:
  record:
  - id: '10766'
    relation: later_version
    status: public
  - id: '9623'
    relation: dissertation_contains
    status: public
status: public
title: Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...