---
OA_type: closed access
_id: '17909'
abstract:
- lang: eng
text: Atomically precise clusters can be used to create single-electron devices
wherein a single redox-active cluster is connected to two macroscopic electrodes
via anchoring ligands. Unlike single-electron devices comprising nanocrystals,
these cluster-based devices can be fabricated with atomic precision. This affords
an unprecedented level of control over the device properties. Herein, we design
a series of cobalt chalcogenide clusters with varying ligand geometries and core
nuclearities to control their current–voltage (I–V) characteristics in a scanning
tunneling microscope-based break junction (STM-BJ) device. First, the device geometry
is modified by precisely positioning junction-anchoring ligands on the surface
of the cluster. We show that the I–V characteristics are independent of ligand
placement, confirming a sequential, single-electron tunneling mechanism. Next,
we chemically fuse two clusters to realize a larger cluster dimer that behaves
as a single electronic unit, possessing a smaller reorganization energy and more
accessible redox states than the monomeric analogues. As a result, dimer-based
devices exhibit significantly higher currents and can even be pushed to current
saturation at high bias. Owing to these controllable properties, single-cluster
junctions serve as an excellent platform for exploring incoherent charge transport
processes at the nanoscale. With this understanding, as well as properties such
as nonlinear I–V characteristics and rectification, these molecular clusters may
function as conductive inorganic nodes in new devices and materials.
article_processing_charge: No
article_type: original
author:
- first_name: Suman
full_name: Gunasekaran, Suman
last_name: Gunasekaran
- first_name: Douglas A.
full_name: Reed, Douglas A.
last_name: Reed
- first_name: Daniel W.
full_name: Paley, Daniel W.
last_name: Paley
- first_name: Amymarie K.
full_name: Bartholomew, Amymarie K.
last_name: Bartholomew
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
- first_name: Michael L.
full_name: Steigerwald, Michael L.
last_name: Steigerwald
- first_name: Xavier
full_name: Roy, Xavier
last_name: Roy
- first_name: Colin
full_name: Nuckolls, Colin
last_name: Nuckolls
citation:
ama: Gunasekaran S, Reed DA, Paley DW, et al. Single-electron currents in designer
single-cluster devices. Journal of the American Chemical Society. 2020;142(35):14924-14932.
doi:10.1021/jacs.0c04970
apa: Gunasekaran, S., Reed, D. A., Paley, D. W., Bartholomew, A. K., Venkataraman,
L., Steigerwald, M. L., … Nuckolls, C. (2020). Single-electron currents in designer
single-cluster devices. Journal of the American Chemical Society. American
Chemical Society. https://doi.org/10.1021/jacs.0c04970
chicago: Gunasekaran, Suman, Douglas A. Reed, Daniel W. Paley, Amymarie K. Bartholomew,
Latha Venkataraman, Michael L. Steigerwald, Xavier Roy, and Colin Nuckolls. “Single-Electron
Currents in Designer Single-Cluster Devices.” Journal of the American Chemical
Society. American Chemical Society, 2020. https://doi.org/10.1021/jacs.0c04970.
ieee: S. Gunasekaran et al., “Single-electron currents in designer single-cluster
devices,” Journal of the American Chemical Society, vol. 142, no. 35. American
Chemical Society, pp. 14924–14932, 2020.
ista: Gunasekaran S, Reed DA, Paley DW, Bartholomew AK, Venkataraman L, Steigerwald
ML, Roy X, Nuckolls C. 2020. Single-electron currents in designer single-cluster
devices. Journal of the American Chemical Society. 142(35), 14924–14932.
mla: Gunasekaran, Suman, et al. “Single-Electron Currents in Designer Single-Cluster
Devices.” Journal of the American Chemical Society, vol. 142, no. 35, American
Chemical Society, 2020, pp. 14924–32, doi:10.1021/jacs.0c04970.
short: S. Gunasekaran, D.A. Reed, D.W. Paley, A.K. Bartholomew, L. Venkataraman,
M.L. Steigerwald, X. Roy, C. Nuckolls, Journal of the American Chemical Society
142 (2020) 14924–14932.
date_created: 2024-09-09T07:19:56Z
date_published: 2020-08-18T00:00:00Z
date_updated: 2024-12-10T12:04:31Z
day: '18'
doi: 10.1021/jacs.0c04970
extern: '1'
external_id:
pmid:
- '32809814'
intvolume: ' 142'
issue: '35'
language:
- iso: eng
month: '08'
oa_version: None
page: 14924-14932
pmid: 1
publication: Journal of the American Chemical Society
publication_identifier:
eissn:
- 1520-5126
issn:
- 0002-7863
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Single-electron currents in designer single-cluster devices
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 142
year: '2020'
...
---
_id: '17910'
abstract:
- lang: eng
text: The scanning tunneling microscope-based break junction (STM-BJ) is used widely
to create and characterize single metal-molecule-metal junctions. In this technique,
conductance is continuously recorded as a metal point contact is broken in a solution
of molecules. Conductance plateaus are seen when stable molecular junctions are
formed. Typically, thousands of junctions are created and measured, yielding thousands
of distinct conductance versus extension traces. However, such traces are rarely
analyzed individually to recognize the types of junctions formed. Here, we present
a deep learning-based method to identify molecular junctions and show that it
performs better than several commonly used and recently reported techniques. We
demonstrate molecular junction identification from mixed solution measurements
with accuracies as high as 97%. We also apply this model to an in situ electric
field-driven isomerization reaction of a [3]cumulene to follow the reaction over
time. Furthermore, we demonstrate that our model can remain accurate even when
a key parameter, the average junction conductance, is eliminated from the analysis,
showing that our model goes beyond conventional analysis in existing methods.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Tianren
full_name: Fu, Tianren
last_name: Fu
- first_name: Yaping
full_name: Zang, Yaping
last_name: Zang
- first_name: Qi
full_name: Zou, Qi
last_name: Zou
- first_name: Colin
full_name: Nuckolls, Colin
last_name: Nuckolls
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
citation:
ama: Fu T, Zang Y, Zou Q, Nuckolls C, Venkataraman L. Using deep learning to identify
molecular junction characteristics. Nano Letters. 2020;20(5):3320-3325.
doi:10.1021/acs.nanolett.0c00198
apa: Fu, T., Zang, Y., Zou, Q., Nuckolls, C., & Venkataraman, L. (2020). Using
deep learning to identify molecular junction characteristics. Nano Letters.
American Chemical Society. https://doi.org/10.1021/acs.nanolett.0c00198
chicago: Fu, Tianren, Yaping Zang, Qi Zou, Colin Nuckolls, and Latha Venkataraman.
“Using Deep Learning to Identify Molecular Junction Characteristics.” Nano
Letters. American Chemical Society, 2020. https://doi.org/10.1021/acs.nanolett.0c00198.
ieee: T. Fu, Y. Zang, Q. Zou, C. Nuckolls, and L. Venkataraman, “Using deep learning
to identify molecular junction characteristics,” Nano Letters, vol. 20,
no. 5. American Chemical Society, pp. 3320–3325, 2020.
ista: Fu T, Zang Y, Zou Q, Nuckolls C, Venkataraman L. 2020. Using deep learning
to identify molecular junction characteristics. Nano Letters. 20(5), 3320–3325.
mla: Fu, Tianren, et al. “Using Deep Learning to Identify Molecular Junction Characteristics.”
Nano Letters, vol. 20, no. 5, American Chemical Society, 2020, pp. 3320–25,
doi:10.1021/acs.nanolett.0c00198.
short: T. Fu, Y. Zang, Q. Zou, C. Nuckolls, L. Venkataraman, Nano Letters 20 (2020)
3320–3325.
date_created: 2024-09-09T07:20:52Z
date_published: 2020-04-03T00:00:00Z
date_updated: 2024-12-10T12:08:53Z
day: '03'
doi: 10.1021/acs.nanolett.0c00198
extern: '1'
external_id:
pmid:
- '32242671'
intvolume: ' 20'
issue: '5'
language:
- iso: eng
month: '04'
oa_version: None
page: 3320-3325
pmid: 1
publication: Nano Letters
publication_identifier:
eissn:
- 1530-6992
issn:
- 1530-6984
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Using deep learning to identify molecular junction characteristics
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 20
year: '2020'
...
---
OA_place: publisher
OA_type: hybrid
_id: '17911'
abstract:
- lang: eng
text: Single-molecule break junction measurements deliver a huge number of conductance
vs. electrode separation traces. During such measurements, the target molecules
may bind to the electrodes in different geometries, and the evolution and rupture
of the single-molecule junction may also follow distinct trajectories. The unraveling
of the various typical trace classes is a prerequisite to the proper physical
interpretation of the data. Here we exploit the efficient feature recognition
properties of neural networks to automatically find the relevant trace classes.
To eliminate the need for manually labeled training data we apply a combined method,
which automatically selects training traces according to the extreme values of
principal component projections or some auxiliary measured quantities. Then the
network captures the features of these characteristic traces and generalizes its
inference to the entire dataset. The use of a simple neural network structure
also enables a direct insight into the decision-making mechanism. We demonstrate
that this combined machine learning method is efficient in the unsupervised recognition
of unobvious, but highly relevant trace classes within low and room temperature
gold–4,4′ bipyridine–gold single-molecule break junction data.
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: András
full_name: Magyarkuti, András
last_name: Magyarkuti
- first_name: Nóra
full_name: Balogh, Nóra
last_name: Balogh
- first_name: Zoltán
full_name: Balogh, Zoltán
last_name: Balogh
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
- first_name: András
full_name: Halbritter, András
last_name: Halbritter
citation:
ama: Magyarkuti A, Balogh N, Balogh Z, Venkataraman L, Halbritter A. Unsupervised
feature recognition in single-molecule break junction data. Nanoscale.
2020;12(15):8355-8363. doi:10.1039/d0nr00467g
apa: Magyarkuti, A., Balogh, N., Balogh, Z., Venkataraman, L., & Halbritter,
A. (2020). Unsupervised feature recognition in single-molecule break junction
data. Nanoscale. Royal Society of Chemistry. https://doi.org/10.1039/d0nr00467g
chicago: Magyarkuti, András, Nóra Balogh, Zoltán Balogh, Latha Venkataraman, and
András Halbritter. “Unsupervised Feature Recognition in Single-Molecule Break
Junction Data.” Nanoscale. Royal Society of Chemistry, 2020. https://doi.org/10.1039/d0nr00467g.
ieee: A. Magyarkuti, N. Balogh, Z. Balogh, L. Venkataraman, and A. Halbritter, “Unsupervised
feature recognition in single-molecule break junction data,” Nanoscale,
vol. 12, no. 15. Royal Society of Chemistry, pp. 8355–8363, 2020.
ista: Magyarkuti A, Balogh N, Balogh Z, Venkataraman L, Halbritter A. 2020. Unsupervised
feature recognition in single-molecule break junction data. Nanoscale. 12(15),
8355–8363.
mla: Magyarkuti, András, et al. “Unsupervised Feature Recognition in Single-Molecule
Break Junction Data.” Nanoscale, vol. 12, no. 15, Royal Society of Chemistry,
2020, pp. 8355–63, doi:10.1039/d0nr00467g.
short: A. Magyarkuti, N. Balogh, Z. Balogh, L. Venkataraman, A. Halbritter, Nanoscale
12 (2020) 8355–8363.
date_created: 2024-09-09T07:21:34Z
date_published: 2020-03-25T00:00:00Z
date_updated: 2024-12-10T12:13:16Z
day: '25'
doi: 10.1039/d0nr00467g
extern: '1'
external_id:
arxiv:
- '2001.03006'
intvolume: ' 12'
issue: '15'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/3.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1039/D0NR00467G
month: '03'
oa: 1
oa_version: Published Version
page: 8355-8363
publication: Nanoscale
publication_identifier:
eissn:
- 2040-3372
issn:
- 2040-3364
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Unsupervised feature recognition in single-molecule break junction data
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/3.0/legalcode
name: Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
short: CC BY-NC (3.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 12
year: '2020'
...
---
OA_type: closed access
_id: '17912'
abstract:
- lang: eng
text: Aryl halides are ubiquitous functional groups in organic chemistry, yet despite
their obvious appeal as surface-binding linkers and as precursors for controlled
graphene nanoribbon synthesis, they have seldom been used as such in molecular
electronics. The confusion regarding the bonding of aryl iodides to Au electrodes
is a case in point, with ambiguous reports of both dative Au–I and covalent Au–C
contacts. Here we form single-molecule junctions with a series of oligophenylene
molecular wires terminated asymmetrically with iodine and thiomethyl to show that
the dative Au–I contact has a lower conductance than the covalent Au–C interaction,
which we propose occurs via an in situ oxidative addition reaction at the Au surface.
Furthermore, we confirm the formation of the Au–C bond by measuring an analogous
series of molecules prepared ex situ with the complex AuI(PPh3) in place of the
iodide. Density functional theory-based transport calculations support our experimental
observations that Au–C linkages have higher conductance than Au–I linkages. Finally,
we demonstrate selective promotion of the Au–C bond formation by controlling the
bias applied across the junction. In addition to establishing the different binding
modes of aryl iodides, our results chart a path to actively controlling oxidative
addition on an Au surface using an applied bias.
article_processing_charge: No
article_type: original
author:
- first_name: Rachel L.
full_name: Starr, Rachel L.
last_name: Starr
- first_name: Tianren
full_name: Fu, Tianren
last_name: Fu
- first_name: Evan A.
full_name: Doud, Evan A.
last_name: Doud
- first_name: Ilana
full_name: Stone, Ilana
last_name: Stone
- first_name: Xavier
full_name: Roy, Xavier
last_name: Roy
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
citation:
ama: Starr RL, Fu T, Doud EA, Stone I, Roy X, Venkataraman L. Gold–carbon contacts
from oxidative addition of aryl iodides. Journal of the American Chemical Society.
2020;142(15):7128-7133. doi:10.1021/jacs.0c01466
apa: Starr, R. L., Fu, T., Doud, E. A., Stone, I., Roy, X., & Venkataraman,
L. (2020). Gold–carbon contacts from oxidative addition of aryl iodides. Journal
of the American Chemical Society. American Chemical Society. https://doi.org/10.1021/jacs.0c01466
chicago: Starr, Rachel L., Tianren Fu, Evan A. Doud, Ilana Stone, Xavier Roy, and
Latha Venkataraman. “Gold–Carbon Contacts from Oxidative Addition of Aryl Iodides.”
Journal of the American Chemical Society. American Chemical Society, 2020.
https://doi.org/10.1021/jacs.0c01466.
ieee: R. L. Starr, T. Fu, E. A. Doud, I. Stone, X. Roy, and L. Venkataraman, “Gold–carbon
contacts from oxidative addition of aryl iodides,” Journal of the American
Chemical Society, vol. 142, no. 15. American Chemical Society, pp. 7128–7133,
2020.
ista: Starr RL, Fu T, Doud EA, Stone I, Roy X, Venkataraman L. 2020. Gold–carbon
contacts from oxidative addition of aryl iodides. Journal of the American Chemical
Society. 142(15), 7128–7133.
mla: Starr, Rachel L., et al. “Gold–Carbon Contacts from Oxidative Addition of Aryl
Iodides.” Journal of the American Chemical Society, vol. 142, no. 15, American
Chemical Society, 2020, pp. 7128–33, doi:10.1021/jacs.0c01466.
short: R.L. Starr, T. Fu, E.A. Doud, I. Stone, X. Roy, L. Venkataraman, Journal
of the American Chemical Society 142 (2020) 7128–7133.
date_created: 2024-09-09T07:22:26Z
date_published: 2020-03-26T00:00:00Z
date_updated: 2024-12-10T12:20:47Z
day: '26'
doi: 10.1021/jacs.0c01466
extern: '1'
external_id:
pmid:
- '32212683'
intvolume: ' 142'
issue: '15'
language:
- iso: eng
month: '03'
oa_version: None
page: 7128-7133
pmid: 1
publication: Journal of the American Chemical Society
publication_identifier:
eissn:
- 1520-5126
issn:
- 0002-7863
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Gold–carbon contacts from oxidative addition of aryl iodides
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 142
year: '2020'
...
---
OA_type: closed access
_id: '17913'
abstract:
- lang: eng
text: Electron transport across a molecular junction is characterized by an energy-dependent
transmission function. The transmission function accounts for electrons tunneling
through multiple molecular orbitals (MOs) with different phases, which gives rise
to quantum interference (QI) effects. Because the transmission function comprises
both interfering and noninterfering effects, individual interferences between
MOs cannot be deduced from the transmission function directly. Herein, we demonstrate
how the transmission function can be deconstructed into its constituent interfering
and noninterfering contributions for any model molecular junction. These contributions
are arranged in a matrix and displayed pictorially as a QI map, which allows one
to easily identify individual QI effects. Importantly, we show that exponential
conductance decay with increasing oligomer length is primarily due to an increase
in destructive QI. With an ability to “see” QI effects using the QI map, we find
that QI is vital to all molecular-scale electron transport.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Suman
full_name: Gunasekaran, Suman
last_name: Gunasekaran
- first_name: Julia E.
full_name: Greenwald, Julia E.
last_name: Greenwald
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
citation:
ama: Gunasekaran S, Greenwald JE, Venkataraman L. Visualizing quantum interference
in molecular junctions. Nano Letters. 2020;20(4):2843-2848. doi:10.1021/acs.nanolett.0c00605
apa: Gunasekaran, S., Greenwald, J. E., & Venkataraman, L. (2020). Visualizing
quantum interference in molecular junctions. Nano Letters. American Chemical
Society. https://doi.org/10.1021/acs.nanolett.0c00605
chicago: Gunasekaran, Suman, Julia E. Greenwald, and Latha Venkataraman. “Visualizing
Quantum Interference in Molecular Junctions.” Nano Letters. American Chemical
Society, 2020. https://doi.org/10.1021/acs.nanolett.0c00605.
ieee: S. Gunasekaran, J. E. Greenwald, and L. Venkataraman, “Visualizing quantum
interference in molecular junctions,” Nano Letters, vol. 20, no. 4. American
Chemical Society, pp. 2843–2848, 2020.
ista: Gunasekaran S, Greenwald JE, Venkataraman L. 2020. Visualizing quantum interference
in molecular junctions. Nano Letters. 20(4), 2843–2848.
mla: Gunasekaran, Suman, et al. “Visualizing Quantum Interference in Molecular Junctions.”
Nano Letters, vol. 20, no. 4, American Chemical Society, 2020, pp. 2843–48,
doi:10.1021/acs.nanolett.0c00605.
short: S. Gunasekaran, J.E. Greenwald, L. Venkataraman, Nano Letters 20 (2020) 2843–2848.
date_created: 2024-09-09T07:36:41Z
date_published: 2020-03-06T00:00:00Z
date_updated: 2024-12-10T12:24:13Z
day: '06'
doi: 10.1021/acs.nanolett.0c00605
extern: '1'
external_id:
pmid:
- '32142291'
intvolume: ' 20'
issue: '4'
language:
- iso: eng
month: '03'
oa_version: None
page: 2843-2848
pmid: 1
publication: Nano Letters
publication_identifier:
eissn:
- 1530-6992
issn:
- 1530-6984
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Visualizing quantum interference in molecular junctions
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 20
year: '2020'
...
---
OA_type: closed access
_id: '17914'
abstract:
- lang: eng
text: Polyacetylene molecular wires have attracted a long-standing interest for
the past 40 years. From a fundamental perspective, there are two main reasons
for the interest. First, polyacetylenes are a prime realization of a one-dimensional
topological insulator. Second, long molecules support freely propagating topological
domain-wall states, so-called “solitons,” which provide an early paradigm for
spin-charge separation. Because of recent experimental developments, individual
polyacetylene chains can now be synthesized on substrates. Motivated by this breakthrough,
we here propose a novel way for chemically supported soliton design in these systems.
We demonstrate how to control the soliton position and how to read it out via
external means. Also, we show how extra soliton–antisoliton pairs arise when applying
a moderate static electric field. We thus make a step toward functionality of
electronic devices based on soliton manipulation, that is, “solitonics”.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Daniel
full_name: Hernangómez-Pérez, Daniel
last_name: Hernangómez-Pérez
- first_name: Suman
full_name: Gunasekaran, Suman
last_name: Gunasekaran
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
- first_name: Ferdinand
full_name: Evers, Ferdinand
last_name: Evers
citation:
ama: Hernangómez-Pérez D, Gunasekaran S, Venkataraman L, Evers F. Solitonics with
polyacetylenes. Nano Letters. 2020;20(4):2615-2619. doi:10.1021/acs.nanolett.0c00136
apa: Hernangómez-Pérez, D., Gunasekaran, S., Venkataraman, L., & Evers, F. (2020).
Solitonics with polyacetylenes. Nano Letters. American Chemical Society.
https://doi.org/10.1021/acs.nanolett.0c00136
chicago: Hernangómez-Pérez, Daniel, Suman Gunasekaran, Latha Venkataraman, and Ferdinand
Evers. “Solitonics with Polyacetylenes.” Nano Letters. American Chemical
Society, 2020. https://doi.org/10.1021/acs.nanolett.0c00136.
ieee: D. Hernangómez-Pérez, S. Gunasekaran, L. Venkataraman, and F. Evers, “Solitonics
with polyacetylenes,” Nano Letters, vol. 20, no. 4. American Chemical Society,
pp. 2615–2619, 2020.
ista: Hernangómez-Pérez D, Gunasekaran S, Venkataraman L, Evers F. 2020. Solitonics
with polyacetylenes. Nano Letters. 20(4), 2615–2619.
mla: Hernangómez-Pérez, Daniel, et al. “Solitonics with Polyacetylenes.” Nano
Letters, vol. 20, no. 4, American Chemical Society, 2020, pp. 2615–19, doi:10.1021/acs.nanolett.0c00136.
short: D. Hernangómez-Pérez, S. Gunasekaran, L. Venkataraman, F. Evers, Nano Letters
20 (2020) 2615–2619.
date_created: 2024-09-09T07:38:36Z
date_published: 2020-03-03T00:00:00Z
date_updated: 2024-12-10T12:26:43Z
day: '03'
doi: 10.1021/acs.nanolett.0c00136
extern: '1'
external_id:
pmid:
- '32125870'
intvolume: ' 20'
issue: '4'
language:
- iso: eng
month: '03'
oa_version: None
page: 2615-2619
pmid: 1
publication: Nano Letters
publication_identifier:
eissn:
- 1530-6992
issn:
- 1530-6984
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Solitonics with polyacetylenes
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 20
year: '2020'
...
---
_id: '18194'
abstract:
- lang: eng
text: 'Realizing strongly correlated topological phases of ultracold gases is a
central goal for ongoing experiments. While fractional quantum Hall states could
soon be implemented in small atomic ensembles, detecting their signatures in few-particle
settings remains a fundamental challenge. In this work, we numerically analyze
the center-of-mass Hall drift of a small ensemble of hardcore bosons, initially
prepared in the ground state of the Harper-Hofstadter-Hubbard model in a box potential.
By monitoring the Hall drift upon release, for a wide range of magnetic flux values,
we identify an emergent Hall plateau compatible with a fractional Chern insulator
state: The extracted Hall conductivity approaches a fractional value determined
by the many-body Chern number, while the width of the plateau agrees with the
spectral and topological properties of the prepared ground state. Besides, a direct
application of Streda''s formula indicates that such Hall plateaus can also be
directly obtained from static density-profile measurements. Our calculations suggest
that fractional Chern insulators can be detected in cold-atom experiments, using
available detection methods.'
article_number: '063316'
article_processing_charge: Yes (in subscription journal)
article_type: original
arxiv: 1
author:
- first_name: C.
full_name: Repellin, C.
last_name: Repellin
- first_name: Julian
full_name: Leonard, Julian
id: b75b3f45-7995-11ef-9bfd-9a9cd02c3577
last_name: Leonard
- first_name: N.
full_name: Goldman, N.
last_name: Goldman
citation:
ama: 'Repellin C, Leonard J, Goldman N. Fractional Chern insulators of few bosons
in a box: Hall plateaus from center-of-mass drifts and density profiles. Physical
Review A. 2020;102(6). doi:10.1103/physreva.102.063316'
apa: 'Repellin, C., Leonard, J., & Goldman, N. (2020). Fractional Chern insulators
of few bosons in a box: Hall plateaus from center-of-mass drifts and density profiles.
Physical Review A. American Physical Society. https://doi.org/10.1103/physreva.102.063316'
chicago: 'Repellin, C., Julian Leonard, and N. Goldman. “Fractional Chern Insulators
of Few Bosons in a Box: Hall Plateaus from Center-of-Mass Drifts and Density Profiles.”
Physical Review A. American Physical Society, 2020. https://doi.org/10.1103/physreva.102.063316.'
ieee: 'C. Repellin, J. Leonard, and N. Goldman, “Fractional Chern insulators of
few bosons in a box: Hall plateaus from center-of-mass drifts and density profiles,”
Physical Review A, vol. 102, no. 6. American Physical Society, 2020.'
ista: 'Repellin C, Leonard J, Goldman N. 2020. Fractional Chern insulators of few
bosons in a box: Hall plateaus from center-of-mass drifts and density profiles.
Physical Review A. 102(6), 063316.'
mla: 'Repellin, C., et al. “Fractional Chern Insulators of Few Bosons in a Box:
Hall Plateaus from Center-of-Mass Drifts and Density Profiles.” Physical Review
A, vol. 102, no. 6, 063316, American Physical Society, 2020, doi:10.1103/physreva.102.063316.'
short: C. Repellin, J. Leonard, N. Goldman, Physical Review A 102 (2020).
date_created: 2024-10-07T11:48:07Z
date_published: 2020-12-14T00:00:00Z
date_updated: 2024-10-08T09:51:57Z
day: '14'
ddc:
- '530'
doi: 10.1103/physreva.102.063316
extern: '1'
external_id:
arxiv:
- '2005.09689'
has_accepted_license: '1'
intvolume: ' 102'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1103/PhysRevA.102.063316
month: '12'
oa: 1
oa_version: Published Version
publication: Physical Review A
publication_identifier:
eissn:
- 2469-9934
issn:
- 2469-9926
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Fractional Chern insulators of few bosons in a box: Hall plateaus from center-of-mass
drifts and density profiles'
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 102
year: '2020'
...
---
OA_place: repository
OA_type: green
_id: '18228'
abstract:
- lang: eng
text: We introduce two constructions in geometric deep learning for 1) transporting
orientation-dependent convolutional filters over a manifold in a continuous way
and thereby defining a convolution operator that naturally incorporates the rotational
effect of holonomy; and 2) allowing efficient evaluation of manifold convolution
layers by sampling manifold valued random variables that center around a weighted
diffusion mean. Both methods are inspired by stochastics on manifolds and geometric
statistics, and provide examples of how stochastic methods – here horizontal frame
bundle flows and non-linear bridge sampling schemes, can be used in geometric
deep learning. We outline the theoretical foundation of the two methods, discuss
their relation to Euclidean deep networks and existing methodology in geometric
deep learning, and establish important properties of the proposed constructions.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Stefan
full_name: Sommer, Stefan
last_name: Sommer
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: Sommer S, Bronstein AM. Horizontal flows and manifold stochastics in geometric
deep learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
2020;44(2):811-822. doi:10.1109/tpami.2020.2994507
apa: Sommer, S., & Bronstein, A. M. (2020). Horizontal flows and manifold stochastics
in geometric deep learning. IEEE Transactions on Pattern Analysis and Machine
Intelligence. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tpami.2020.2994507
chicago: Sommer, Stefan, and Alex M. Bronstein. “Horizontal Flows and Manifold Stochastics
in Geometric Deep Learning.” IEEE Transactions on Pattern Analysis and Machine
Intelligence. Institute of Electrical and Electronics Engineers, 2020. https://doi.org/10.1109/tpami.2020.2994507.
ieee: S. Sommer and A. M. Bronstein, “Horizontal flows and manifold stochastics
in geometric deep learning,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 44, no. 2. Institute of Electrical and Electronics Engineers,
pp. 811–822, 2020.
ista: Sommer S, Bronstein AM. 2020. Horizontal flows and manifold stochastics in
geometric deep learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
44(2), 811–822.
mla: Sommer, Stefan, and Alex M. Bronstein. “Horizontal Flows and Manifold Stochastics
in Geometric Deep Learning.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 44, no. 2, Institute of Electrical and Electronics Engineers,
2020, pp. 811–22, doi:10.1109/tpami.2020.2994507.
short: S. Sommer, A.M. Bronstein, IEEE Transactions on Pattern Analysis and Machine
Intelligence 44 (2020) 811–822.
date_created: 2024-10-08T12:55:23Z
date_published: 2020-02-01T00:00:00Z
date_updated: 2024-10-15T06:56:47Z
day: '01'
doi: 10.1109/tpami.2020.2994507
extern: '1'
external_id:
arxiv:
- '1909.06397'
intvolume: ' 44'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1909.06397
month: '02'
oa: 1
oa_version: Preprint
page: 811-822
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
eissn:
- 1939-3539
issn:
- 0162-8828
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Horizontal flows and manifold stochastics in geometric deep learning
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 44
year: '2020'
...
---
OA_type: closed access
_id: '18245'
abstract:
- lang: eng
text: Intel® RealSense™ SR300 is a depth camera capable of providing a VGA-size
depth map at 60 fps and 0.125mm depth resolution. In addition, it outputs an infrared
VGA-resolution image and a 1080p color texture image at 30 fps. SR300 form-factor
enables it to be integrated into small consumer products and as a front facing
camera in laptops and Ultrabooks™. The SR300 depth camera is based on a coded-light
technology where triangulation between projected patterns and images captured
by a dedicated sensor is used to produce the depth map. Each projected line is
coded by a special temporal optical code, that enables a dense depth map reconstruction
from its reflection. The solid mechanical assembly of the camera allows it to
stay calibrated throughout temperature and pressure changes, drops, and hits.
In addition, active dynamic control maintains a calibrated depth output. An extended
API LibRS released with the camera allows developers to integrate the camera in
various applications. Algorithms for 3D scanning, facial analysis, hand gesture
recognition, and tracking are within reach for applications using the SR300. In
this paper, we describe the underlying technology, hardware, and algorithms of
the SR300, as well as its calibration procedure, and outline some use cases. We
believe that this paper will provide a full case study of a mass-produced depth
sensing product and technology.
article_processing_charge: No
article_type: original
author:
- first_name: Aviad
full_name: Zabatani, Aviad
last_name: Zabatani
- first_name: Vitaly
full_name: Surazhsky, Vitaly
last_name: Surazhsky
- first_name: Erez
full_name: Sperling, Erez
last_name: Sperling
- first_name: Sagi Ben
full_name: Moshe, Sagi Ben
last_name: Moshe
- first_name: Ohad
full_name: Menashe, Ohad
last_name: Menashe
- first_name: David H.
full_name: Silver, David H.
last_name: Silver
- first_name: Zachi
full_name: Karni, Zachi
last_name: Karni
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Michael K
full_name: Bronstein, Michael K
last_name: Bronstein
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Zabatani A, Surazhsky V, Sperling E, et al. Intel® RealSenseTM SR300
coded light depth camera. IEEE Transactions on Pattern Analysis and Machine
Intelligence. 2020;42(10):2333-2345. doi:10.1109/tpami.2019.2915841
apa: Zabatani, A., Surazhsky, V., Sperling, E., Moshe, S. B., Menashe, O., Silver,
D. H., … Kimmel, R. (2020). Intel® RealSenseTM SR300 coded light depth
camera. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tpami.2019.2915841
chicago: Zabatani, Aviad, Vitaly Surazhsky, Erez Sperling, Sagi Ben Moshe, Ohad
Menashe, David H. Silver, Zachi Karni, Alex M. Bronstein, Michael K Bronstein,
and Ron Kimmel. “Intel® RealSenseTM SR300 Coded Light Depth Camera.”
IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute
of Electrical and Electronics Engineers, 2020. https://doi.org/10.1109/tpami.2019.2915841.
ieee: A. Zabatani et al., “Intel® RealSenseTM SR300 coded light
depth camera,” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 42, no. 10. Institute of Electrical and Electronics Engineers, pp. 2333–2345,
2020.
ista: Zabatani A, Surazhsky V, Sperling E, Moshe SB, Menashe O, Silver DH, Karni
Z, Bronstein AM, Bronstein MK, Kimmel R. 2020. Intel® RealSenseTM SR300
coded light depth camera. IEEE Transactions on Pattern Analysis and Machine Intelligence.
42(10), 2333–2345.
mla: Zabatani, Aviad, et al. “Intel® RealSenseTM SR300 Coded Light Depth
Camera.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 42, no. 10, Institute of Electrical and Electronics Engineers, 2020, pp.
2333–45, doi:10.1109/tpami.2019.2915841.
short: A. Zabatani, V. Surazhsky, E. Sperling, S.B. Moshe, O. Menashe, D.H. Silver,
Z. Karni, A.M. Bronstein, M.K. Bronstein, R. Kimmel, IEEE Transactions on Pattern
Analysis and Machine Intelligence 42 (2020) 2333–2345.
date_created: 2024-10-08T13:04:18Z
date_published: 2020-10-01T00:00:00Z
date_updated: 2024-10-15T09:40:01Z
day: '01'
doi: 10.1109/tpami.2019.2915841
extern: '1'
external_id:
pmid:
- '31094683'
intvolume: ' 42'
issue: '10'
language:
- iso: eng
month: '10'
oa_version: None
page: 2333-2345
pmid: 1
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
eissn:
- 1939-3539
issn:
- 0162-8828
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Intel® RealSense™ SR300 coded light depth camera
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 42
year: '2020'
...
---
OA_type: closed access
_id: '18246'
abstract:
- lang: eng
text: Adversarial attacks have demonstrated remarkable efficacy in altering the
output of a learning model by applying a minimal perturbation to the input data.
While increasing attention has been placed on the image domain, however, the study
of adversarial perturbations for geometric data has been notably lagging behind.
In this paper, we show that effective adversarial attacks can be concocted for
surfaces embedded in 3D, under weak smoothness assumptions on the perceptibility
of the attack. We address the case of deformable 3D shapes in particular, and
introduce a general model that is not tailored to any specific surface representation,
nor does it assume access to a parametric description of the 3D object. In this
context, we consider targeted and untargeted variants of the attack, demonstrating
compelling results in either case. We further show how discovering adversarial
examples, and then using them for adversarial training, leads to an increase in
both robustness and accuracy. Our findings are confirmed empirically over multiple
datasets spanning different semantic classes and deformations.
article_processing_charge: No
article_type: original
author:
- first_name: G.
full_name: Mariani, G.
last_name: Mariani
- first_name: L.
full_name: Cosmo, L.
last_name: Cosmo
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: E.
full_name: Rodolà, E.
last_name: Rodolà
citation:
ama: Mariani G, Cosmo L, Bronstein AM, Rodolà E. Generating adversarial surfaces
via band‐limited perturbations. Computer Graphics Forum. 2020;39(5):253-264.
doi:10.1111/cgf.14083
apa: Mariani, G., Cosmo, L., Bronstein, A. M., & Rodolà, E. (2020). Generating
adversarial surfaces via band‐limited perturbations. Computer Graphics Forum.
Wiley. https://doi.org/10.1111/cgf.14083
chicago: Mariani, G., L. Cosmo, Alex M. Bronstein, and E. Rodolà. “Generating Adversarial
Surfaces via Band‐limited Perturbations.” Computer Graphics Forum. Wiley,
2020. https://doi.org/10.1111/cgf.14083.
ieee: G. Mariani, L. Cosmo, A. M. Bronstein, and E. Rodolà, “Generating adversarial
surfaces via band‐limited perturbations,” Computer Graphics Forum, vol.
39, no. 5. Wiley, pp. 253–264, 2020.
ista: Mariani G, Cosmo L, Bronstein AM, Rodolà E. 2020. Generating adversarial surfaces
via band‐limited perturbations. Computer Graphics Forum. 39(5), 253–264.
mla: Mariani, G., et al. “Generating Adversarial Surfaces via Band‐limited Perturbations.”
Computer Graphics Forum, vol. 39, no. 5, Wiley, 2020, pp. 253–64, doi:10.1111/cgf.14083.
short: G. Mariani, L. Cosmo, A.M. Bronstein, E. Rodolà, Computer Graphics Forum
39 (2020) 253–264.
date_created: 2024-10-08T13:04:35Z
date_published: 2020-08-01T00:00:00Z
date_updated: 2024-10-15T09:36:46Z
day: '01'
doi: 10.1111/cgf.14083
extern: '1'
intvolume: ' 39'
issue: '5'
language:
- iso: eng
month: '08'
oa_version: None
page: 253-264
publication: Computer Graphics Forum
publication_identifier:
eissn:
- 1467-8659
issn:
- 0167-7055
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Generating adversarial surfaces via band‐limited perturbations
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2020'
...
---
_id: '18247'
abstract:
- lang: eng
text: Convolutional neural networks (CNNs) achieve state-of-the-art accuracy in
a variety of tasks in computer vision and beyond. One of the major obstacles hindering
the ubiquitous use of CNNs for inference on low-power edge devices is their high
computational complexity and memory bandwidth requirements. The latter often dominates
the energy footprint on modern hardware. In this paper, we introduce a lossy transform
coding approach, inspired by image and video compression, designed to reduce the
memory bandwidth due to the storage of intermediate activation calculation results.
Our method does not require fine-tuning the network weights and halves the data
transfer volumes to the main memory by compressing feature maps, which are highly
correlated, with variable length coding. Our method outperform previous approach
in term of the number of bits per value with minor accuracy degradation on ResNet-34
and MobileNetV2. We analyze the performance of our approach on a variety of CNN
architectures and demonstrate that FPGA implementation of ResNet-18 with our approach
results in a reduction of around 40% in the memory energy footprint, compared
to quantized network, with negligible impact on accuracy. When allowing accuracy
degradation of up to 2%, the reduction of 60% is achieved. A reference implementation
accompanies the paper.
article_number: '9206968'
article_processing_charge: No
arxiv: 1
author:
- first_name: Brian
full_name: Chmiel, Brian
last_name: Chmiel
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Evgenii
full_name: Zheltonozhskii, Evgenii
last_name: Zheltonozhskii
- first_name: Ron
full_name: Banner, Ron
last_name: Banner
- first_name: Yevgeny
full_name: Yermolin, Yevgeny
last_name: Yermolin
- first_name: Alex
full_name: Karbachevsky, Alex
last_name: Karbachevsky
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Avi
full_name: Mendelson, Avi
last_name: Mendelson
citation:
ama: 'Chmiel B, Baskin C, Zheltonozhskii E, et al. Feature map transform coding
for energy-efficient CNN inference. In: 2020 International Joint Conference
on Neural Networks (IJCNN). IEEE; 2020. doi:10.1109/ijcnn48605.2020.9206968'
apa: 'Chmiel, B., Baskin, C., Zheltonozhskii, E., Banner, R., Yermolin, Y., Karbachevsky,
A., … Mendelson, A. (2020). Feature map transform coding for energy-efficient
CNN inference. In 2020 International Joint Conference on Neural Networks (IJCNN).
Glasgow, United Kingdom: IEEE. https://doi.org/10.1109/ijcnn48605.2020.9206968'
chicago: Chmiel, Brian, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Yevgeny
Yermolin, Alex Karbachevsky, Alex M. Bronstein, and Avi Mendelson. “Feature Map
Transform Coding for Energy-Efficient CNN Inference.” In 2020 International
Joint Conference on Neural Networks (IJCNN). IEEE, 2020. https://doi.org/10.1109/ijcnn48605.2020.9206968.
ieee: B. Chmiel et al., “Feature map transform coding for energy-efficient
CNN inference,” in 2020 International Joint Conference on Neural Networks (IJCNN),
Glasgow, United Kingdom, 2020.
ista: Chmiel B, Baskin C, Zheltonozhskii E, Banner R, Yermolin Y, Karbachevsky A,
Bronstein AM, Mendelson A. 2020. Feature map transform coding for energy-efficient
CNN inference. 2020 International Joint Conference on Neural Networks (IJCNN).
International Joint Conference on Neural Networks, 9206968.
mla: Chmiel, Brian, et al. “Feature Map Transform Coding for Energy-Efficient CNN
Inference.” 2020 International Joint Conference on Neural Networks (IJCNN),
9206968, IEEE, 2020, doi:10.1109/ijcnn48605.2020.9206968.
short: B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, Y. Yermolin, A. Karbachevsky,
A.M. Bronstein, A. Mendelson, in:, 2020 International Joint Conference on Neural
Networks (IJCNN), IEEE, 2020.
conference:
end_date: 2020-07-24
location: Glasgow, United Kingdom
name: International Joint Conference on Neural Networks
start_date: 2020-07-19
date_created: 2024-10-08T13:04:52Z
date_published: 2020-09-28T00:00:00Z
date_updated: 2024-12-12T10:04:54Z
day: '28'
doi: 10.1109/ijcnn48605.2020.9206968
extern: '1'
external_id:
arxiv:
- '1905.10830'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1905.10830
month: '09'
oa: 1
oa_version: Preprint
publication: 2020 International Joint Conference on Neural Networks (IJCNN)
publication_identifier:
eissn:
- 2161-4407
isbn:
- '9781728169279'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Feature map transform coding for energy-efficient CNN inference
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18248'
abstract:
- lang: eng
text: Learning an object detection or retrieval system requires a large data set
with manual annotations. Such data are expensive and time-consuming to create
and therefore difficult to obtain on a large scale. In this work, we propose using
the natural correlation in narrations and the visual presence of objects in video
to learn an object detector and retriever without any manual labeling involved.
We pose the problem as weakly supervised learning with noisy labels, and propose
a novel object detection and retrieval paradigm under these constraints. We handle
the background rejection by using contrastive samples and confront the high level
of label noise with a new clustering score. Our evaluation is based on a set of
ten objects with manual ground truth annotation in almost 5000 frames extracted
from instructional videos from the web. We demonstrate superior results compared
to state-of-the-art weakly- supervised approaches and report a strongly-labeled
upper bound as well. While the focus of the paper is object detection and retrieval,
the proposed methodology can be applied to a broader range of noisy weakly-supervised
problems.
article_number: '9150938'
article_processing_charge: No
author:
- first_name: Elad
full_name: Amrani, Elad
last_name: Amrani
- first_name: Rami
full_name: Ben-Ari, Rami
last_name: Ben-Ari
- first_name: Inbar
full_name: Shapira, Inbar
last_name: Shapira
- first_name: Tal
full_name: Hakim, Tal
last_name: Hakim
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Amrani E, Ben-Ari R, Shapira I, Hakim T, Bronstein AM. Self-supervised object
detection and retrieval using unlabeled videos. In: 2020 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE; 2020.
doi:10.1109/cvprw50498.2020.00485'
apa: 'Amrani, E., Ben-Ari, R., Shapira, I., Hakim, T., & Bronstein, A. M. (2020).
Self-supervised object detection and retrieval using unlabeled videos. In 2020
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
Seattle, WA, United States: IEEE. https://doi.org/10.1109/cvprw50498.2020.00485'
chicago: Amrani, Elad, Rami Ben-Ari, Inbar Shapira, Tal Hakim, and Alex M. Bronstein.
“Self-Supervised Object Detection and Retrieval Using Unlabeled Videos.” In 2020
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
IEEE, 2020. https://doi.org/10.1109/cvprw50498.2020.00485.
ieee: E. Amrani, R. Ben-Ari, I. Shapira, T. Hakim, and A. M. Bronstein, “Self-supervised
object detection and retrieval using unlabeled videos,” in 2020 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA,
United States, 2020.
ista: Amrani E, Ben-Ari R, Shapira I, Hakim T, Bronstein AM. 2020. Self-supervised
object detection and retrieval using unlabeled videos. 2020 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops, 9150938.
mla: Amrani, Elad, et al. “Self-Supervised Object Detection and Retrieval Using
Unlabeled Videos.” 2020 IEEE/CVF Conference on Computer Vision and Pattern
Recognition Workshops (CVPRW), 9150938, IEEE, 2020, doi:10.1109/cvprw50498.2020.00485.
short: E. Amrani, R. Ben-Ari, I. Shapira, T. Hakim, A.M. Bronstein, in:, 2020 IEEE/CVF
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE,
2020.
conference:
end_date: 2020-06-19
location: Seattle, WA, United States
name: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
start_date: 2020-06-14
date_created: 2024-10-08T13:05:08Z
date_published: 2020-07-28T00:00:00Z
date_updated: 2024-12-12T09:59:41Z
day: '28'
doi: 10.1109/cvprw50498.2020.00485
extern: '1'
language:
- iso: eng
month: '07'
oa_version: None
publication: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
(CVPRW)
publication_identifier:
eissn:
- 2160-7516
isbn:
- '9781728193618'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Self-supervised object detection and retrieval using unlabeled videos
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18249'
abstract:
- lang: eng
text: Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality
for soft tissues. The long acquisition times, however, make it more prone to motion
artifacts as well as contribute to the relative high costs of this examination.
Over the years, multiple studies concentrated on designing reduced measurement
schemes and image reconstruction schemes for MRI, however these problems have
been so far addressed separately. On the other hand, recent works in optical computational
imaging have demonstrated growing success of simultaneous learning-based design
of the acquisition and reconstruction schemes manifesting significant improvement
in the reconstruction quality with a constrained time budget. Inspired by these
successes, in this work, we propose to learn accelerated MR acquisition schemes
(in the form of Cartesian trajectories) jointly with the image reconstruction
operator. To this end, we propose an algorithm for training the combined acquisition-reconstruction
pipeline end-to-end in a differentiable way. We demonstrate the significance of
using the learned Cartesian trajectories at different speed up rates. Code available
at https://github.com/tomer196/fastMRI-Cartesian.
article_number: '9054542'
article_processing_charge: No
arxiv: 1
author:
- first_name: Tomer
full_name: Weiss, Tomer
last_name: Weiss
- first_name: Sanketh
full_name: Vedula, Sanketh
last_name: Vedula
- first_name: Ortal
full_name: Senouf, Ortal
last_name: Senouf
- first_name: Oleg
full_name: Michailovich, Oleg
last_name: Michailovich
- first_name: Michael
full_name: Zibulevsky, Michael
last_name: Zibulevsky
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Weiss T, Vedula S, Senouf O, Michailovich O, Zibulevsky M, Bronstein AM. Joint
learning of cartesian undersampling and reconstruction for accelerated MRI. In:
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP). IEEE; 2020. doi:10.1109/icassp40776.2020.9054542'
apa: 'Weiss, T., Vedula, S., Senouf, O., Michailovich, O., Zibulevsky, M., &
Bronstein, A. M. (2020). Joint learning of cartesian undersampling and reconstruction
for accelerated MRI. In ICASSP 2020 - 2020 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE.
https://doi.org/10.1109/icassp40776.2020.9054542'
chicago: Weiss, Tomer, Sanketh Vedula, Ortal Senouf, Oleg Michailovich, Michael
Zibulevsky, and Alex M. Bronstein. “Joint Learning of Cartesian Undersampling
and Reconstruction for Accelerated MRI.” In ICASSP 2020 - 2020 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.
https://doi.org/10.1109/icassp40776.2020.9054542.
ieee: T. Weiss, S. Vedula, O. Senouf, O. Michailovich, M. Zibulevsky, and A. M.
Bronstein, “Joint learning of cartesian undersampling and reconstruction for accelerated
MRI,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), Barcelona, Spain, 2020.
ista: Weiss T, Vedula S, Senouf O, Michailovich O, Zibulevsky M, Bronstein AM. 2020.
Joint learning of cartesian undersampling and reconstruction for accelerated MRI.
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP). IEEE International Conference on Acoustics, Speech, and Signal
Processing, 9054542.
mla: Weiss, Tomer, et al. “Joint Learning of Cartesian Undersampling and Reconstruction
for Accelerated MRI.” ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 9054542, IEEE, 2020, doi:10.1109/icassp40776.2020.9054542.
short: T. Weiss, S. Vedula, O. Senouf, O. Michailovich, M. Zibulevsky, A.M. Bronstein,
in:, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), IEEE, 2020.
conference:
end_date: 2020-05-08
location: Barcelona, Spain
name: IEEE International Conference on Acoustics, Speech, and Signal Processing
start_date: 2020-05-04
date_created: 2024-10-08T13:05:24Z
date_published: 2020-04-09T00:00:00Z
date_updated: 2024-12-11T16:06:20Z
day: '09'
doi: 10.1109/icassp40776.2020.9054542
extern: '1'
external_id:
arxiv:
- '1905.09324'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1905.09324
month: '04'
oa: 1
oa_version: Preprint
publication: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)
publication_identifier:
eissn:
- 2379-190X
isbn:
- '9781509066322'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Joint learning of cartesian undersampling and reconstruction for accelerated
MRI
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
OA_place: repository
OA_type: green
_id: '18250'
abstract:
- lang: eng
text: Many shape analysis methods treat the geometry of an object as a metric space
that can be captured by the Laplace-Beltrami operator. In this paper, we propose
to adapt the classical Hamiltonian operator from quantum mechanics to the field
of shape analysis. To this end, we study the addition of a potential function
to the Laplacian as a generator for dual spaces in which shape processing is performed.
We present general optimization approaches for solving variational problems involving
the basis defined by the Hamiltonian using perturbation theory for its eigenvectors.
The suggested operator is shown to produce better functional spaces to operate
with, as demonstrated on different shape analysis tasks.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yoni
full_name: Choukroun, Yoni
last_name: Choukroun
- first_name: Alon
full_name: Shtern, Alon
last_name: Shtern
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Choukroun Y, Shtern A, Bronstein AM, Kimmel R. Hamiltonian operator for spectral
shape analysis. IEEE Transactions on Visualization and Computer Graphics.
2020;26(2):1320-1331. doi:10.1109/tvcg.2018.2867513
apa: Choukroun, Y., Shtern, A., Bronstein, A. M., & Kimmel, R. (2020). Hamiltonian
operator for spectral shape analysis. IEEE Transactions on Visualization and
Computer Graphics. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tvcg.2018.2867513
chicago: Choukroun, Yoni, Alon Shtern, Alex M. Bronstein, and Ron Kimmel. “Hamiltonian
Operator for Spectral Shape Analysis.” IEEE Transactions on Visualization and
Computer Graphics. Institute of Electrical and Electronics Engineers, 2020.
https://doi.org/10.1109/tvcg.2018.2867513.
ieee: Y. Choukroun, A. Shtern, A. M. Bronstein, and R. Kimmel, “Hamiltonian operator
for spectral shape analysis,” IEEE Transactions on Visualization and Computer
Graphics, vol. 26, no. 2. Institute of Electrical and Electronics Engineers,
pp. 1320–1331, 2020.
ista: Choukroun Y, Shtern A, Bronstein AM, Kimmel R. 2020. Hamiltonian operator
for spectral shape analysis. IEEE Transactions on Visualization and Computer Graphics.
26(2), 1320–1331.
mla: Choukroun, Yoni, et al. “Hamiltonian Operator for Spectral Shape Analysis.”
IEEE Transactions on Visualization and Computer Graphics, vol. 26, no.
2, Institute of Electrical and Electronics Engineers, 2020, pp. 1320–31, doi:10.1109/tvcg.2018.2867513.
short: Y. Choukroun, A. Shtern, A.M. Bronstein, R. Kimmel, IEEE Transactions on
Visualization and Computer Graphics 26 (2020) 1320–1331.
date_created: 2024-10-08T13:05:41Z
date_published: 2020-02-01T00:00:00Z
date_updated: 2024-10-15T09:43:31Z
day: '01'
doi: 10.1109/tvcg.2018.2867513
extern: '1'
external_id:
arxiv:
- '1611.01990'
pmid:
- '30176599'
intvolume: ' 26'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.48550/arXiv.1611.01990 '
month: '02'
oa: 1
oa_version: Preprint
page: 1320-1331
pmid: 1
publication: IEEE Transactions on Visualization and Computer Graphics
publication_identifier:
eissn:
- 2160-9306
issn:
- 1077-2626
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Hamiltonian operator for spectral shape analysis
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 26
year: '2020'
...
---
_id: '18251'
abstract:
- lang: eng
text: Magnetic Resonance Imaging (MRI) has long been considered to be among the
gold standards of today’s diagnostic imaging. The most significant drawback of
MRI is long acquisition times, prohibiting its use in standard practice for some
applications. Compressed sensing (CS) proposes to subsample the k-space (the Fourier
domain dual to the physical space of spatial coordinates) leading to significantly
accelerated acquisition. However, the benefit of compressed sensing has not been
fully exploited; most of the sampling densities obtained through CS do not produce
a trajectory that obeys the stringent constraints of the MRI machine imposed in
practice. Inspired by recent success of deep learning-based approaches for image
reconstruction and ideas from computational imaging on learning-based design of
imaging systems, we introduce 3D FLAT, a novel protocol for data-driven design
of 3D non-Cartesian accelerated trajectories in MRI. Our proposal leverages the
entire 3D k-space to simultaneously learn a physically feasible acquisition trajectory
with a reconstruction method. Experimental results, performed as a proof-of-concept,
suggest that 3D FLAT achieves higher image quality for a given readout time compared
to standard trajectories such as radial, stack-of-stars, or 2D learned trajectories
(trajectories that evolve only in the 2D plane while fully sampling along the
third dimension). Furthermore, we demonstrate evidence supporting the significant
benefit of performing MRI acquisitions using non-Cartesian 3D trajectories over
2D non-Cartesian trajectories acquired slice-wise.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Jonathan
full_name: Alush-Aben, Jonathan
last_name: Alush-Aben
- first_name: Linor
full_name: Ackerman-Schraier, Linor
last_name: Ackerman-Schraier
- first_name: Tomer
full_name: Weiss, Tomer
last_name: Weiss
- first_name: Sanketh
full_name: Vedula, Sanketh
last_name: Vedula
- first_name: Ortal
full_name: Senouf, Ortal
last_name: Senouf
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Alush-Aben J, Ackerman-Schraier L, Weiss T, Vedula S, Senouf O, Bronstein
AM. 3D FLAT: Feasible learned acquisition trajectories for accelerated MRI. In:
International Workshop on Machine Learning for Medical Image Reconstruction.
Vol 12450. Springer Nature; 2020:3-16. doi:10.1007/978-3-030-61598-7_1'
apa: 'Alush-Aben, J., Ackerman-Schraier, L., Weiss, T., Vedula, S., Senouf, O.,
& Bronstein, A. M. (2020). 3D FLAT: Feasible learned acquisition trajectories
for accelerated MRI. In International Workshop on Machine Learning for Medical
Image Reconstruction (Vol. 12450, pp. 3–16). Lima, Peru: Springer Nature.
https://doi.org/10.1007/978-3-030-61598-7_1'
chicago: 'Alush-Aben, Jonathan, Linor Ackerman-Schraier, Tomer Weiss, Sanketh Vedula,
Ortal Senouf, and Alex M. Bronstein. “3D FLAT: Feasible Learned Acquisition Trajectories
for Accelerated MRI.” In International Workshop on Machine Learning for Medical
Image Reconstruction, 12450:3–16. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-61598-7_1.'
ieee: 'J. Alush-Aben, L. Ackerman-Schraier, T. Weiss, S. Vedula, O. Senouf, and
A. M. Bronstein, “3D FLAT: Feasible learned acquisition trajectories for accelerated
MRI,” in International Workshop on Machine Learning for Medical Image Reconstruction,
Lima, Peru, 2020, vol. 12450, pp. 3–16.'
ista: 'Alush-Aben J, Ackerman-Schraier L, Weiss T, Vedula S, Senouf O, Bronstein
AM. 2020. 3D FLAT: Feasible learned acquisition trajectories for accelerated MRI.
International Workshop on Machine Learning for Medical Image Reconstruction. MLMIR:
Workshop on Machine Learning for Medical Image Reconstruction, LNCS, vol. 12450,
3–16.'
mla: 'Alush-Aben, Jonathan, et al. “3D FLAT: Feasible Learned Acquisition Trajectories
for Accelerated MRI.” International Workshop on Machine Learning for Medical
Image Reconstruction, vol. 12450, Springer Nature, 2020, pp. 3–16, doi:10.1007/978-3-030-61598-7_1.'
short: J. Alush-Aben, L. Ackerman-Schraier, T. Weiss, S. Vedula, O. Senouf, A.M.
Bronstein, in:, International Workshop on Machine Learning for Medical Image Reconstruction,
Springer Nature, 2020, pp. 3–16.
conference:
end_date: 2020-10-08
location: Lima, Peru
name: 'MLMIR: Workshop on Machine Learning for Medical Image Reconstruction'
start_date: 2020-10-08
date_created: 2024-10-08T13:06:03Z
date_published: 2020-10-20T00:00:00Z
date_updated: 2025-01-23T15:13:44Z
day: '20'
doi: 10.1007/978-3-030-61598-7_1
extern: '1'
intvolume: ' 12450'
language:
- iso: eng
month: '10'
oa_version: None
page: 3 - 16
publication: International Workshop on Machine Learning for Medical Image Reconstruction
publication_identifier:
eisbn:
- '9783030615987'
eissn:
- 1611-3349
isbn:
- '9783030615970'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: '3D FLAT: Feasible learned acquisition trajectories for accelerated MRI'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 12450
year: '2020'
...
---
OA_type: closed access
_id: '18252'
abstract:
- lang: eng
text: 'Vector fields arise in a variety of quantity measure and visualization techniques,
such as fluid flow imaging, motion estimation, deformation measures, and color
imaging, leading to a better understanding of physical phenomena. Recent progress
in vector field imaging technologies has emphasized the need for efficient noise
removal and reconstruction algorithms. A key ingredient in the successful extraction
of signals from noisy measurements is prior information, which can often be represented
as a parameterized model. In this work, we extend the overparameterization variational
framework in order to perform model-based reconstruction of vector fields. The
overparameterization methodology combines local modeling of the data with global
model parameter regularization. By considering the vector field as a linear combination
of basis vector fields and appropriate scale and rotation coefficients, we can
reduce the denoising problem to a simpler form of coefficient recovery. We introduce
two versions of the overparameterization framework: a total variation-based method
and a sparsity-based method, which relies on the cosparse analysis model. We demonstrate
the efficiency of the proposed frameworks for two- and three-dimensional vector
fields with linear and quadratic overparameterization models.'
article_processing_charge: No
article_type: original
author:
- first_name: Keren
full_name: Rotker, Keren
last_name: Rotker
- first_name: Dafna Ben
full_name: Bashat, Dafna Ben
last_name: Bashat
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: Rotker K, Bashat DB, Bronstein AM. Overparameterized models for vector fields.
SIAM Journal on Imaging Sciences. 2020;13(3):1386-1414. doi:10.1137/19m1280697
apa: Rotker, K., Bashat, D. B., & Bronstein, A. M. (2020). Overparameterized
models for vector fields. SIAM Journal on Imaging Sciences. Society for
Industrial & Applied Mathematics. https://doi.org/10.1137/19m1280697
chicago: Rotker, Keren, Dafna Ben Bashat, and Alex M. Bronstein. “Overparameterized
Models for Vector Fields.” SIAM Journal on Imaging Sciences. Society for
Industrial & Applied Mathematics, 2020. https://doi.org/10.1137/19m1280697.
ieee: K. Rotker, D. B. Bashat, and A. M. Bronstein, “Overparameterized models for
vector fields,” SIAM Journal on Imaging Sciences, vol. 13, no. 3. Society
for Industrial & Applied Mathematics, pp. 1386–1414, 2020.
ista: Rotker K, Bashat DB, Bronstein AM. 2020. Overparameterized models for vector
fields. SIAM Journal on Imaging Sciences. 13(3), 1386–1414.
mla: Rotker, Keren, et al. “Overparameterized Models for Vector Fields.” SIAM
Journal on Imaging Sciences, vol. 13, no. 3, Society for Industrial &
Applied Mathematics, 2020, pp. 1386–414, doi:10.1137/19m1280697.
short: K. Rotker, D.B. Bashat, A.M. Bronstein, SIAM Journal on Imaging Sciences
13 (2020) 1386–1414.
date_created: 2024-10-08T13:06:25Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2024-10-15T10:43:38Z
day: '01'
doi: 10.1137/19m1280697
extern: '1'
intvolume: ' 13'
issue: '3'
language:
- iso: eng
month: '01'
oa_version: None
page: 1386-1414
publication: SIAM Journal on Imaging Sciences
publication_identifier:
eissn:
- 1936-4954
publication_status: published
publisher: Society for Industrial & Applied Mathematics
quality_controlled: '1'
scopus_import: '1'
status: public
title: Overparameterized models for vector fields
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2020'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '18253'
abstract:
- lang: eng
text: PCNA, the ring that encircles DNA maintaining the processivity of DNA polymerases,
is modified by ubiquitin and SUMO. Whereas ubiquitin is required for bypassing
lesions through the DNA damage tolerance (DDT) pathways, we show here that SUMOylation
represses another pathway, salvage recombination. The Srs2 helicase is recruited
to SUMOylated PCNA and prevents the salvage pathway from acting. The pathway can
be induced by overexpressing the PCNA unloader Elg1, or the homologous recombination
protein Rad52. Our results underscore the role of PCNA modifications in controlling
the various bypass and DNA repair mechanisms.
article_number: 00705-20
article_processing_charge: Yes
article_type: original
author:
- first_name: Matan
full_name: Arbel, Matan
last_name: Arbel
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Soumitra
full_name: Sau, Soumitra
last_name: Sau
- first_name: Batia
full_name: Liefshitz, Batia
last_name: Liefshitz
- first_name: Martin
full_name: Kupiec, Martin
last_name: Kupiec
citation:
ama: Arbel M, Bronstein AM, Sau S, Liefshitz B, Kupiec M. Access to PCNA by Srs2
and Elg1 controls the choice between alternative repair pathways in Saccharomyces
cerevisiae. mBio. 2020;11(3). doi:10.1128/mbio.00705-20
apa: Arbel, M., Bronstein, A. M., Sau, S., Liefshitz, B., & Kupiec, M. (2020).
Access to PCNA by Srs2 and Elg1 controls the choice between alternative repair
pathways in Saccharomyces cerevisiae. MBio. American Society for Microbiology.
https://doi.org/10.1128/mbio.00705-20
chicago: Arbel, Matan, Alex M. Bronstein, Soumitra Sau, Batia Liefshitz, and Martin
Kupiec. “Access to PCNA by Srs2 and Elg1 Controls the Choice between Alternative
Repair Pathways in Saccharomyces Cerevisiae.” MBio. American Society for
Microbiology, 2020. https://doi.org/10.1128/mbio.00705-20.
ieee: M. Arbel, A. M. Bronstein, S. Sau, B. Liefshitz, and M. Kupiec, “Access to
PCNA by Srs2 and Elg1 controls the choice between alternative repair pathways
in Saccharomyces cerevisiae,” mBio, vol. 11, no. 3. American Society for
Microbiology, 2020.
ista: Arbel M, Bronstein AM, Sau S, Liefshitz B, Kupiec M. 2020. Access to PCNA
by Srs2 and Elg1 controls the choice between alternative repair pathways in Saccharomyces
cerevisiae. mBio. 11(3), 00705-20.
mla: Arbel, Matan, et al. “Access to PCNA by Srs2 and Elg1 Controls the Choice between
Alternative Repair Pathways in Saccharomyces Cerevisiae.” MBio, vol. 11,
no. 3, 00705-20, American Society for Microbiology, 2020, doi:10.1128/mbio.00705-20.
short: M. Arbel, A.M. Bronstein, S. Sau, B. Liefshitz, M. Kupiec, MBio 11 (2020).
date_created: 2024-10-08T13:06:43Z
date_published: 2020-06-01T00:00:00Z
date_updated: 2024-10-15T10:50:42Z
day: '01'
doi: 10.1128/mbio.00705-20
extern: '1'
external_id:
pmid:
- '32371600'
intvolume: ' 11'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1128/mbio.00705-20
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
publication: mBio
publication_identifier:
eissn:
- 2150-7511
issn:
- 2161-2129
publication_status: published
publisher: American Society for Microbiology
quality_controlled: '1'
scopus_import: '1'
status: public
title: Access to PCNA by Srs2 and Elg1 controls the choice between alternative repair
pathways in Saccharomyces cerevisiae
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2020'
...
---
_id: '18255'
abstract:
- lang: eng
text: Learning an object detection or retrieval system requires a large data set
with manual annotations. Such data sets are expensive and time consuming to create
and therefore difficult to obtain on a large scale. In this work, we propose to
exploit the natural correlation in narrations and the visual presence of objects
in video, to learn an object detector and retrieval without any manual labeling
involved. We pose the problem as weakly supervised learning with noisy labels,
and propose a novel object detection paradigm under these constraints. We handle
the background rejection by using contrastive samples and confront the high level
of label noise with a new clustering score. Our evaluation is based on a set of
11 manually annotated objects in over 5000 frames. We show comparison to a weakly-supervised
approach as baseline and provide a strongly labeled upper bound.
article_number: '9022341'
article_processing_charge: No
arxiv: 1
author:
- first_name: Elad
full_name: Amrani, Elad
last_name: Amrani
- first_name: Rami
full_name: Ben-Ari, Rami
last_name: Ben-Ari
- first_name: Tal
full_name: Hakim, Tal
last_name: Hakim
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Amrani E, Ben-Ari R, Hakim T, Bronstein AM. Learning to detect and retrieve
objects from unlabeled videos. In: 2019 IEEE/CVF International Conference on
Computer Vision Workshop (ICCVW). IEEE; 2020. doi:10.1109/iccvw.2019.00567'
apa: 'Amrani, E., Ben-Ari, R., Hakim, T., & Bronstein, A. M. (2020). Learning
to detect and retrieve objects from unlabeled videos. In 2019 IEEE/CVF International
Conference on Computer Vision Workshop (ICCVW). Seoul, Korea (South): IEEE.
https://doi.org/10.1109/iccvw.2019.00567'
chicago: Amrani, Elad, Rami Ben-Ari, Tal Hakim, and Alex M. Bronstein. “Learning
to Detect and Retrieve Objects from Unlabeled Videos.” In 2019 IEEE/CVF International
Conference on Computer Vision Workshop (ICCVW). IEEE, 2020. https://doi.org/10.1109/iccvw.2019.00567.
ieee: E. Amrani, R. Ben-Ari, T. Hakim, and A. M. Bronstein, “Learning to detect
and retrieve objects from unlabeled videos,” in 2019 IEEE/CVF International
Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2020.
ista: Amrani E, Ben-Ari R, Hakim T, Bronstein AM. 2020. Learning to detect and retrieve
objects from unlabeled videos. 2019 IEEE/CVF International Conference on Computer
Vision Workshop (ICCVW). 17th IEEE/CVF International Conference on Computer Vision
Workshop, 9022341.
mla: Amrani, Elad, et al. “Learning to Detect and Retrieve Objects from Unlabeled
Videos.” 2019 IEEE/CVF International Conference on Computer Vision Workshop
(ICCVW), 9022341, IEEE, 2020, doi:10.1109/iccvw.2019.00567.
short: E. Amrani, R. Ben-Ari, T. Hakim, A.M. Bronstein, in:, 2019 IEEE/CVF International
Conference on Computer Vision Workshop (ICCVW), IEEE, 2020.
conference:
end_date: 2019-10-28
location: Seoul, Korea (South)
name: 17th IEEE/CVF International Conference on Computer Vision Workshop
start_date: 2019-10-27
date_created: 2024-10-08T13:07:16Z
date_published: 2020-03-05T00:00:00Z
date_updated: 2024-12-05T16:04:03Z
day: '05'
doi: 10.1109/iccvw.2019.00567
extern: '1'
external_id:
arxiv:
- '1905.11137'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1905.11137
month: '03'
oa: 1
oa_version: Preprint
publication: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
publication_identifier:
eissn:
- 2473-9944
isbn:
- '9781728150246'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning to detect and retrieve objects from unlabeled videos
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18258'
abstract:
- lang: eng
text: Distance metric learning (DML) has been successfully applied to object classification,
both in the standard regime of rich training data and in the few-shot scenario,
where each category is represented by only a few examples. In this work, we propose
a new method for DML that simultaneously learns the backbone network parameters,
the embedding space, and the multi-modal distribution of each of the training
categories in that space, in a single end-to-end training process. Our approach
outperforms state-of-the-art methods for DML-based object classification on a
variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness
of our approach on the problem of few-shot object detection, by incorporating
the proposed DML architecture as a classification head into a standard object
detection model. We achieve the best results on the ImageNet-LOC dataset compared
to strong baselines, when only a few training examples are available. We also
offer the community a new episodic benchmark based on the ImageNet dataset for
the few-shot object detection task.
article_number: '8953439'
article_processing_charge: No
author:
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
- first_name: Joseph
full_name: Shtok, Joseph
last_name: Shtok
- first_name: Sivan
full_name: Harary, Sivan
last_name: Harary
- first_name: Eli
full_name: Schwartz, Eli
last_name: Schwartz
- first_name: Amit
full_name: Aides, Amit
last_name: Aides
- first_name: Rogerio
full_name: Feris, Rogerio
last_name: Feris
- first_name: Raja
full_name: Giryes, Raja
last_name: Giryes
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Karlinsky L, Shtok J, Harary S, et al. Repmet: Representative-based metric
learning for classification and few-shot object detection. In: 2019 IEEE/CVF
Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2020.
doi:10.1109/cvpr.2019.00534'
apa: 'Karlinsky, L., Shtok, J., Harary, S., Schwartz, E., Aides, A., Feris, R.,
… Bronstein, A. M. (2020). Repmet: Representative-based metric learning for classification
and few-shot object detection. In 2019 IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR). Long Beach, CA, United States: IEEE. https://doi.org/10.1109/cvpr.2019.00534'
chicago: 'Karlinsky, Leonid, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides,
Rogerio Feris, Raja Giryes, and Alex M. Bronstein. “Repmet: Representative-Based
Metric Learning for Classification and Few-Shot Object Detection.” In 2019
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE,
2020. https://doi.org/10.1109/cvpr.2019.00534.'
ieee: 'L. Karlinsky et al., “Repmet: Representative-based metric learning
for classification and few-shot object detection,” in 2019 IEEE/CVF Conference
on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, United
States, 2020.'
ista: 'Karlinsky L, Shtok J, Harary S, Schwartz E, Aides A, Feris R, Giryes R, Bronstein
AM. 2020. Repmet: Representative-based metric learning for classification and
few-shot object detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR). 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition,
8953439.'
mla: 'Karlinsky, Leonid, et al. “Repmet: Representative-Based Metric Learning for
Classification and Few-Shot Object Detection.” 2019 IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR), 8953439, IEEE, 2020, doi:10.1109/cvpr.2019.00534.'
short: L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes,
A.M. Bronstein, in:, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, 2020.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition
start_date: 2019-06-15
date_created: 2024-10-08T13:08:09Z
date_published: 2020-01-09T00:00:00Z
date_updated: 2024-12-05T15:38:16Z
day: '09'
doi: 10.1109/cvpr.2019.00534
extern: '1'
language:
- iso: eng
month: '01'
oa_version: None
publication: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781728132945'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Repmet: Representative-based metric learning for classification and few-shot
object detection'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18259'
abstract:
- lang: eng
text: Example synthesis is one of the leading methods to tackle the problem of few-shot
learning, where only a small number of samples per class are available. However,
current synthesis approaches only address the scenario of a single category label
per image. In this work, we propose a novel technique for synthesizing samples
with multiple labels for the (yet unhandled) multi-label few-shot classification
scenario. We propose to combine pairs of given examples in feature space, so that
the resulting synthesized feature vectors will correspond to examples whose label
sets are obtained through certain set operations on the label sets of the corresponding
input pairs. Thus, our method is capable of producing a sample containing the
intersection, union or set-difference of labels present in two input samples.
As we show, these set operations generalize to labels unseen during training.
This enables performing augmentation on examples of novel categories, thus, facilitating
multi-label few-shot classifier learning. We conduct numerous experiments showing
promising results for the label-set manipulation capabilities of the proposed
approach, both directly (using the classification and retrieval metrics), and
in the context of performing data augmentation for multi-label few-shot learning.
We propose a benchmark for this new and challenging task and show that our method
compares favorably to all the common baselines.
article_number: '8954088'
article_processing_charge: No
arxiv: 1
author:
- first_name: Amit
full_name: Alfassy, Amit
last_name: Alfassy
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
- first_name: Amit
full_name: Aides, Amit
last_name: Aides
- first_name: Joseph
full_name: Shtok, Joseph
last_name: Shtok
- first_name: Sivan
full_name: Harary, Sivan
last_name: Harary
- first_name: Rogerio
full_name: Feris, Rogerio
last_name: Feris
- first_name: Raja
full_name: Giryes, Raja
last_name: Giryes
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Alfassy A, Karlinsky L, Aides A, et al. Laso: Label-set operations networks
for multi-label few-shot learning. In: 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR). IEEE; 2020. doi:10.1109/cvpr.2019.00671'
apa: 'Alfassy, A., Karlinsky, L., Aides, A., Shtok, J., Harary, S., Feris, R., …
Bronstein, A. M. (2020). Laso: Label-set operations networks for multi-label few-shot
learning. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR). Long Beach, CA, United States: IEEE. https://doi.org/10.1109/cvpr.2019.00671'
chicago: 'Alfassy, Amit, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary,
Rogerio Feris, Raja Giryes, and Alex M. Bronstein. “Laso: Label-Set Operations
Networks for Multi-Label Few-Shot Learning.” In 2019 IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. https://doi.org/10.1109/cvpr.2019.00671.'
ieee: 'A. Alfassy et al., “Laso: Label-set operations networks for multi-label
few-shot learning,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), Long Beach, CA, United States, 2020.'
ista: 'Alfassy A, Karlinsky L, Aides A, Shtok J, Harary S, Feris R, Giryes R, Bronstein
AM. 2020. Laso: Label-set operations networks for multi-label few-shot learning.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 32nd
IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8954088.'
mla: 'Alfassy, Amit, et al. “Laso: Label-Set Operations Networks for Multi-Label
Few-Shot Learning.” 2019 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), 8954088, IEEE, 2020, doi:10.1109/cvpr.2019.00671.'
short: A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes,
A.M. Bronstein, in:, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, 2020.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition
start_date: 2019-06-15
date_created: 2024-10-08T13:08:26Z
date_published: 2020-01-09T00:00:00Z
date_updated: 2024-12-05T15:33:21Z
day: '09'
doi: 10.1109/cvpr.2019.00671
extern: '1'
external_id:
arxiv:
- '1902.09811'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1902.09811
month: '01'
oa: 1
oa_version: Preprint
publication: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781728132945'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Laso: Label-set operations networks for multi-label few-shot learning'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...