---
_id: '17608'
abstract:
- lang: eng
text: We study the long-term evolution of the global structure of axisymmetric accretion
flows onto a black hole (BH) at rates substantially higher than the Eddington
value (M˙Edd), performing two-dimensional hydrodynamical simulations with and
without radiative diffusion. In the high-accretion optically-thick limit, where
the radiation energy is efficiently trapped within the inflow, the accretion flow
becomes adiabatic and comprises of turbulent gas in the equatorial region and
strong bipolar outflows. As a result, the mass inflow rate decreases toward the
center as M˙in∝rp with p∼0.5−0.7 and a small fraction of the inflowing gas feeds
the nuclear BH. Thus, super-Eddington accretion is sustained only when a larger
amount of gas is supplied from larger radii at >100−1000 M˙Edd. The global structure
of the flow settles down to a quasi-steady state in millions of the orbital timescale
at the BH event horizon, which is >10−100 times longer than that addressed in
previous (magneto-)RHD simulation studies. Energy transport via radiative diffusion
accelerates the outflow near the poles in the inner region but does not change
the overall properties of the accretion flow compared to the cases without diffusion.
Based on our simulation results, we provide a mechanical feedback model for super-Eddington
accreting BHs. This can be applied as a sub-grid model in large-scale cosmological
simulations that do not sufficiently resolve galactic nuclei, and to the formation
of the heaviest gravitational-wave sources via accretion in dense environments.
article_number: '132'
article_processing_charge: No
article_type: original
author:
- first_name: Haojie
full_name: Hu, Haojie
last_name: Hu
- first_name: Kohei
full_name: Inayoshi, Kohei
last_name: Inayoshi
- first_name: Zoltán
full_name: Haiman, Zoltán
id: 7c006e8c-cc0d-11ee-8322-cb904ef76f36
last_name: Haiman
- first_name: Eliot
full_name: Quataert, Eliot
last_name: Quataert
- first_name: Rolf
full_name: Kuiper, Rolf
last_name: Kuiper
citation:
ama: 'Hu H, Inayoshi K, Haiman Z, Quataert E, Kuiper R. Long-term evolution of supercritical
black hole accretion with outflows: A subgrid feedback model for cosmological
simulations. The Astrophysical Journal. 2022;934(2). doi:10.3847/1538-4357/ac75d8'
apa: 'Hu, H., Inayoshi, K., Haiman, Z., Quataert, E., & Kuiper, R. (2022). Long-term
evolution of supercritical black hole accretion with outflows: A subgrid feedback
model for cosmological simulations. The Astrophysical Journal. American
Astronomical Society. https://doi.org/10.3847/1538-4357/ac75d8'
chicago: 'Hu, Haojie, Kohei Inayoshi, Zoltán Haiman, Eliot Quataert, and Rolf Kuiper.
“Long-Term Evolution of Supercritical Black Hole Accretion with Outflows: A Subgrid
Feedback Model for Cosmological Simulations.” The Astrophysical Journal.
American Astronomical Society, 2022. https://doi.org/10.3847/1538-4357/ac75d8.'
ieee: 'H. Hu, K. Inayoshi, Z. Haiman, E. Quataert, and R. Kuiper, “Long-term evolution
of supercritical black hole accretion with outflows: A subgrid feedback model
for cosmological simulations,” The Astrophysical Journal, vol. 934, no.
2. American Astronomical Society, 2022.'
ista: 'Hu H, Inayoshi K, Haiman Z, Quataert E, Kuiper R. 2022. Long-term evolution
of supercritical black hole accretion with outflows: A subgrid feedback model
for cosmological simulations. The Astrophysical Journal. 934(2), 132.'
mla: 'Hu, Haojie, et al. “Long-Term Evolution of Supercritical Black Hole Accretion
with Outflows: A Subgrid Feedback Model for Cosmological Simulations.” The
Astrophysical Journal, vol. 934, no. 2, 132, American Astronomical Society,
2022, doi:10.3847/1538-4357/ac75d8.'
short: H. Hu, K. Inayoshi, Z. Haiman, E. Quataert, R. Kuiper, The Astrophysical
Journal 934 (2022).
date_created: 2024-09-05T13:17:38Z
date_published: 2022-08-01T00:00:00Z
date_updated: 2024-09-23T14:23:12Z
day: '01'
doi: 10.3847/1538-4357/ac75d8
extern: '1'
intvolume: ' 934'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.3847/1538-4357/ac75d8
month: '08'
oa: 1
oa_version: Published Version
publication: The Astrophysical Journal
publication_identifier:
issn:
- 0004-637X
- 1538-4357
publication_status: published
publisher: American Astronomical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Long-term evolution of supercritical black hole accretion with outflows: A
subgrid feedback model for cosmological simulations'
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 934
year: '2022'
...
---
OA_place: repository
OA_type: green
_id: '17868'
abstract:
- lang: eng
text: Reversed conductance decay describes increasing conductance of a molecular
chain series with increasing chain length. Realizing reversed conductance decay
is an important step toward making long and highly conducting molecular wires.
Recent work has shown that one-dimensional topological insulators (1D TIs) can
exhibit reversed conductance decay due to their nontrivial edge states. The Su–Schrieffer–Heeger
(SSH) model for 1D TIs relates to the electronic structure of these isolated molecules
but not their electron transport properties as single-molecule junctions. Herein,
we use a tight-binding approach to demonstrate that polyacetylene and other diradicaloid
1D TIs show a reversed conductance decay at the short chain limit. We explain
these conductance trends by analyzing the impact of the edge states in these 1D
systems on the single-molecule junction transmission. Additionally, we discuss
how the self-energy from the electrode-molecule coupling and the on-site energy
of the edge sites can be tuned to create longer wires with reversed conductance
decays.
article_processing_charge: No
article_type: original
author:
- first_name: Liang
full_name: Li, Liang
last_name: Li
- first_name: Suman
full_name: Gunasekaran, Suman
last_name: Gunasekaran
- first_name: Yujing
full_name: Wei, Yujing
last_name: Wei
- 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: Li L, Gunasekaran S, Wei Y, Nuckolls C, Venkataraman L. Reversed conductance
decay of 1D topological insulators by tight-binding analysis. The Journal of
Physical Chemistry Letters. 2022;13(41):9703-9710. doi:10.1021/acs.jpclett.2c02812
apa: Li, L., Gunasekaran, S., Wei, Y., Nuckolls, C., & Venkataraman, L. (2022).
Reversed conductance decay of 1D topological insulators by tight-binding analysis.
The Journal of Physical Chemistry Letters. American Chemical Society. https://doi.org/10.1021/acs.jpclett.2c02812
chicago: Li, Liang, Suman Gunasekaran, Yujing Wei, Colin Nuckolls, and Latha Venkataraman.
“Reversed Conductance Decay of 1D Topological Insulators by Tight-Binding Analysis.”
The Journal of Physical Chemistry Letters. American Chemical Society, 2022.
https://doi.org/10.1021/acs.jpclett.2c02812.
ieee: L. Li, S. Gunasekaran, Y. Wei, C. Nuckolls, and L. Venkataraman, “Reversed
conductance decay of 1D topological insulators by tight-binding analysis,” The
Journal of Physical Chemistry Letters, vol. 13, no. 41. American Chemical
Society, pp. 9703–9710, 2022.
ista: Li L, Gunasekaran S, Wei Y, Nuckolls C, Venkataraman L. 2022. Reversed conductance
decay of 1D topological insulators by tight-binding analysis. The Journal of Physical
Chemistry Letters. 13(41), 9703–9710.
mla: Li, Liang, et al. “Reversed Conductance Decay of 1D Topological Insulators
by Tight-Binding Analysis.” The Journal of Physical Chemistry Letters,
vol. 13, no. 41, American Chemical Society, 2022, pp. 9703–10, doi:10.1021/acs.jpclett.2c02812.
short: L. Li, S. Gunasekaran, Y. Wei, C. Nuckolls, L. Venkataraman, The Journal
of Physical Chemistry Letters 13 (2022) 9703–9710.
date_created: 2024-09-06T13:02:46Z
date_published: 2022-10-11T00:00:00Z
date_updated: 2024-12-10T09:21:49Z
day: '11'
doi: 10.1021/acs.jpclett.2c02812
extern: '1'
external_id:
pmid:
- '36219846'
intvolume: ' 13'
issue: '41'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.26434/chemrxiv-2022-b1fh9-v3
month: '10'
oa: 1
oa_version: Preprint
page: 9703-9710
pmid: 1
publication: The Journal of Physical Chemistry Letters
publication_identifier:
issn:
- 1948-7185
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Reversed conductance decay of 1D topological insulators by tight-binding analysis
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2022'
...
---
OA_place: repository
OA_type: green
_id: '17869'
abstract:
- lang: eng
text: The formation of carbon–carbon bonds with transition metal reagents serves
as a cornerstone of organic synthesis. Here, we show that the reactivity of an
otherwise kinetically inert transition metal complex can be induced by an external
electric field to affect a coupling reaction. These results highlight the importance
of electric field effects in reaction chemistry and offers a new strategy to modulate
organometallic reactivity.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Nicholas M.
full_name: Orchanian, Nicholas M.
last_name: Orchanian
- first_name: Sophia
full_name: Guizzo, Sophia
last_name: Guizzo
- first_name: Michael L.
full_name: Steigerwald, Michael L.
last_name: Steigerwald
- 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: Orchanian NM, Guizzo S, Steigerwald ML, Nuckolls C, Venkataraman L. Electric-field-induced
coupling of aryl iodides with a nickel(0) complex. Chemical Communications.
2022;58(90):12556-12559. doi:10.1039/d2cc03671a
apa: Orchanian, N. M., Guizzo, S., Steigerwald, M. L., Nuckolls, C., & Venkataraman,
L. (2022). Electric-field-induced coupling of aryl iodides with a nickel(0) complex.
Chemical Communications. Royal Society of Chemistry. https://doi.org/10.1039/d2cc03671a
chicago: Orchanian, Nicholas M., Sophia Guizzo, Michael L. Steigerwald, Colin Nuckolls,
and Latha Venkataraman. “Electric-Field-Induced Coupling of Aryl Iodides with
a Nickel(0) Complex.” Chemical Communications. Royal Society of Chemistry,
2022. https://doi.org/10.1039/d2cc03671a.
ieee: N. M. Orchanian, S. Guizzo, M. L. Steigerwald, C. Nuckolls, and L. Venkataraman,
“Electric-field-induced coupling of aryl iodides with a nickel(0) complex,” Chemical
Communications, vol. 58, no. 90. Royal Society of Chemistry, pp. 12556–12559,
2022.
ista: Orchanian NM, Guizzo S, Steigerwald ML, Nuckolls C, Venkataraman L. 2022.
Electric-field-induced coupling of aryl iodides with a nickel(0) complex. Chemical
Communications. 58(90), 12556–12559.
mla: Orchanian, Nicholas M., et al. “Electric-Field-Induced Coupling of Aryl Iodides
with a Nickel(0) Complex.” Chemical Communications, vol. 58, no. 90, Royal
Society of Chemistry, 2022, pp. 12556–59, doi:10.1039/d2cc03671a.
short: N.M. Orchanian, S. Guizzo, M.L. Steigerwald, C. Nuckolls, L. Venkataraman,
Chemical Communications 58 (2022) 12556–12559.
date_created: 2024-09-06T13:03:38Z
date_published: 2022-10-10T00:00:00Z
date_updated: 2024-12-10T09:27:04Z
day: '10'
doi: 10.1039/d2cc03671a
extern: '1'
external_id:
pmid:
- '36245392'
intvolume: ' 58'
issue: '90'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.26434/chemrxiv-2022-lfnw1
month: '10'
oa: 1
oa_version: Preprint
page: 12556-12559
pmid: 1
publication: Chemical Communications
publication_identifier:
eissn:
- 1364-548X
issn:
- 1359-7345
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1039/d2cc03671a
scopus_import: '1'
status: public
title: Electric-field-induced coupling of aryl iodides with a nickel(0) complex
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 58
year: '2022'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '17870'
abstract:
- lang: eng
text: The electric fields created at solid–liquid interfaces are important in heterogeneous
catalysis. Here we describe the Ullmann coupling of aryl iodides on rough gold
surfaces, which we monitor in situ using the scanning tunneling microscope-based
break junction (STM-BJ) and ex situ using mass spectrometry and fluorescence spectroscopy.
We find that this Ullmann coupling reaction occurs only on rough gold surfaces
in polar solvents, the latter of which implicates interfacial electric fields.
These experimental observations are supported by density functional theory calculations
that elucidate the roles of surface roughness and local electric fields on the
reaction. More broadly, this touchstone study offers a facile method to access
and probe in real time an increasingly prominent yet incompletely understood mode
of catalysis.
article_processing_charge: Yes
article_type: original
author:
- first_name: Ilana B.
full_name: Stone, Ilana B.
last_name: Stone
- first_name: Rachel L.
full_name: Starr, Rachel L.
last_name: Starr
- first_name: Norah
full_name: Hoffmann, Norah
last_name: Hoffmann
- first_name: Xiao
full_name: Wang, Xiao
last_name: Wang
- first_name: Austin M.
full_name: Evans, Austin M.
last_name: Evans
- first_name: Colin
full_name: Nuckolls, Colin
last_name: Nuckolls
- first_name: Tristan H.
full_name: Lambert, Tristan H.
last_name: Lambert
- first_name: Michael L.
full_name: Steigerwald, Michael L.
last_name: Steigerwald
- first_name: Timothy C.
full_name: Berkelbach, Timothy C.
last_name: Berkelbach
- 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: Stone IB, Starr RL, Hoffmann N, et al. Interfacial electric fields catalyze
Ullmann coupling reactions on gold surfaces. Chemical Science. 2022;13(36):10798-10805.
doi:10.1039/d2sc03780g
apa: Stone, I. B., Starr, R. L., Hoffmann, N., Wang, X., Evans, A. M., Nuckolls,
C., … Venkataraman, L. (2022). Interfacial electric fields catalyze Ullmann coupling
reactions on gold surfaces. Chemical Science. Royal Society of Chemistry.
https://doi.org/10.1039/d2sc03780g
chicago: Stone, Ilana B., Rachel L. Starr, Norah Hoffmann, Xiao Wang, Austin M.
Evans, Colin Nuckolls, Tristan H. Lambert, et al. “Interfacial Electric Fields
Catalyze Ullmann Coupling Reactions on Gold Surfaces.” Chemical Science.
Royal Society of Chemistry, 2022. https://doi.org/10.1039/d2sc03780g.
ieee: I. B. Stone et al., “Interfacial electric fields catalyze Ullmann coupling
reactions on gold surfaces,” Chemical Science, vol. 13, no. 36. Royal Society
of Chemistry, pp. 10798–10805, 2022.
ista: Stone IB, Starr RL, Hoffmann N, Wang X, Evans AM, Nuckolls C, Lambert TH,
Steigerwald ML, Berkelbach TC, Roy X, Venkataraman L. 2022. Interfacial electric
fields catalyze Ullmann coupling reactions on gold surfaces. Chemical Science.
13(36), 10798–10805.
mla: Stone, Ilana B., et al. “Interfacial Electric Fields Catalyze Ullmann Coupling
Reactions on Gold Surfaces.” Chemical Science, vol. 13, no. 36, Royal Society
of Chemistry, 2022, pp. 10798–805, doi:10.1039/d2sc03780g.
short: I.B. Stone, R.L. Starr, N. Hoffmann, X. Wang, A.M. Evans, C. Nuckolls, T.H.
Lambert, M.L. Steigerwald, T.C. Berkelbach, X. Roy, L. Venkataraman, Chemical
Science 13 (2022) 10798–10805.
date_created: 2024-09-06T13:04:27Z
date_published: 2022-09-01T00:00:00Z
date_updated: 2024-12-10T09:29:53Z
day: '01'
doi: 10.1039/d2sc03780g
extern: '1'
intvolume: ' 13'
issue: '36'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/3.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1039/D2SC03780G
month: '09'
oa: 1
oa_version: Published Version
page: 10798-10805
publication: Chemical Science
publication_identifier:
eissn:
- 2041-6539
issn:
- 2041-6520
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Interfacial electric fields catalyze Ullmann coupling reactions on gold surfaces
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: 13
year: '2022'
...
---
OA_type: closed access
_id: '17871'
abstract:
- lang: eng
text: Single-molecule topological insulators are promising candidates as conducting
wires over nanometre length scales. A key advantage is their ability to exhibit
quasi-metallic transport, in contrast to conjugated molecular wires which typically
exhibit a low conductance that decays as the wire length increases. Here, we study
a family of oligophenylene-bridged bis(triarylamines) with tunable and stable
mono- or di-radicaloid character. These wires can undergo one- and two-electron
chemical oxidations to the corresponding mono-cation and di-cation, respectively.
We show that the oxidized wires exhibit reversed conductance decay with increasing
length, consistent with the expectation for Su–Schrieffer–Heeger-type one-dimensional
topological insulators. The 2.6-nm-long di-cation reported here displays a conductance
greater than 0.1G0, where G0 is the conductance quantum, a factor of 5,400 greater
than the neutral form. The observed conductance–length relationship is similar
between the mono-cation and di-cation series. Density functional theory calculations
elucidate how the frontier orbitals and delocalization of radicals facilitate
the observed non-classical quasi-metallic behaviour.
article_processing_charge: No
article_type: original
author:
- first_name: Liang
full_name: Li, Liang
last_name: Li
- first_name: Jonathan Z.
full_name: Low, Jonathan Z.
last_name: Low
- first_name: Jan
full_name: Wilhelm, Jan
last_name: Wilhelm
- first_name: Guanming
full_name: Liao, Guanming
last_name: Liao
- first_name: Suman
full_name: Gunasekaran, Suman
last_name: Gunasekaran
- first_name: Claudia R.
full_name: Prindle, Claudia R.
last_name: Prindle
- first_name: Rachel L.
full_name: Starr, Rachel L.
last_name: Starr
- first_name: Dorothea
full_name: Golze, Dorothea
last_name: Golze
- first_name: Colin
full_name: Nuckolls, Colin
last_name: Nuckolls
- first_name: Michael L.
full_name: Steigerwald, Michael L.
last_name: Steigerwald
- first_name: Ferdinand
full_name: Evers, Ferdinand
last_name: Evers
- first_name: Luis M.
full_name: Campos, Luis M.
last_name: Campos
- first_name: Xiaodong
full_name: Yin, Xiaodong
last_name: Yin
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
citation:
ama: Li L, Low JZ, Wilhelm J, et al. Highly conducting single-molecule topological
insulators based on mono- and di-radical cations. Nature Chemistry. 2022;14(9):1061-1067.
doi:10.1038/s41557-022-00978-1
apa: Li, L., Low, J. Z., Wilhelm, J., Liao, G., Gunasekaran, S., Prindle, C. R.,
… Venkataraman, L. (2022). Highly conducting single-molecule topological insulators
based on mono- and di-radical cations. Nature Chemistry. Springer Nature.
https://doi.org/10.1038/s41557-022-00978-1
chicago: Li, Liang, Jonathan Z. Low, Jan Wilhelm, Guanming Liao, Suman Gunasekaran,
Claudia R. Prindle, Rachel L. Starr, et al. “Highly Conducting Single-Molecule
Topological Insulators Based on Mono- and Di-Radical Cations.” Nature Chemistry.
Springer Nature, 2022. https://doi.org/10.1038/s41557-022-00978-1.
ieee: L. Li et al., “Highly conducting single-molecule topological insulators
based on mono- and di-radical cations,” Nature Chemistry, vol. 14, no.
9. Springer Nature, pp. 1061–1067, 2022.
ista: Li L, Low JZ, Wilhelm J, Liao G, Gunasekaran S, Prindle CR, Starr RL, Golze
D, Nuckolls C, Steigerwald ML, Evers F, Campos LM, Yin X, Venkataraman L. 2022.
Highly conducting single-molecule topological insulators based on mono- and di-radical
cations. Nature Chemistry. 14(9), 1061–1067.
mla: Li, Liang, et al. “Highly Conducting Single-Molecule Topological Insulators
Based on Mono- and Di-Radical Cations.” Nature Chemistry, vol. 14, no.
9, Springer Nature, 2022, pp. 1061–67, doi:10.1038/s41557-022-00978-1.
short: L. Li, J.Z. Low, J. Wilhelm, G. Liao, S. Gunasekaran, C.R. Prindle, R.L.
Starr, D. Golze, C. Nuckolls, M.L. Steigerwald, F. Evers, L.M. Campos, X. Yin,
L. Venkataraman, Nature Chemistry 14 (2022) 1061–1067.
date_created: 2024-09-06T13:05:31Z
date_published: 2022-07-07T00:00:00Z
date_updated: 2024-12-10T09:37:45Z
day: '07'
doi: 10.1038/s41557-022-00978-1
extern: '1'
external_id:
pmid:
- '35798950'
intvolume: ' 14'
issue: '9'
language:
- iso: eng
month: '07'
oa_version: None
page: 1061-1067
pmid: 1
publication: Nature Chemistry
publication_identifier:
eissn:
- 1755-4349
issn:
- 1755-4330
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Highly conducting single-molecule topological insulators based on mono- and
di-radical cations
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14
year: '2022'
...
---
_id: '17872'
abstract:
- lang: eng
text: Coherent tunneling electron transport through molecular wires has been theoretically
established as a temperature-independent process. Although several experimental
studies have shown counter examples, robust models to describe this temperature
dependence have not been thoroughly developed. Here, we demonstrate that dynamic
molecular structures lead to temperature-dependent conductance within coherent
tunneling regime. Using a custom-built variable-temperature scanning tunneling
microscopy break-junction instrument, we find that oligo[n]phenylenes exhibit
clear temperature-dependent conductance. Our calculations reveal that thermally
activated dihedral rotations allow these molecular wires to have a higher probability
of being in a planar conformation. As the tunneling occurs primarily through π-orbitals,
enhanced coplanarization substantially increases the time-averaged tunneling probability.
These calculations are consistent with the observation that more rotational pivot
points in longer molecular wires leads to larger temperature-dependence on conductance.
These findings reveal that molecular conductance within coherent and off-resonant
electron transport regimes can be controlled by manipulating dynamic molecular
structure.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Woojung
full_name: Lee, Woojung
last_name: Lee
- first_name: Shayan
full_name: Louie, Shayan
last_name: Louie
- first_name: Austin M.
full_name: Evans, Austin M.
last_name: Evans
- first_name: Nicholas M.
full_name: Orchanian, Nicholas M.
last_name: Orchanian
- first_name: Ilana B.
full_name: Stone, Ilana B.
last_name: Stone
- first_name: Boyuan
full_name: Zhang, Boyuan
last_name: Zhang
- first_name: Yujing
full_name: Wei, Yujing
last_name: Wei
- first_name: Xavier
full_name: Roy, Xavier
last_name: Roy
- 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: Lee W, Louie S, Evans AM, et al. Increased molecular conductance in Oligo[n]phenylene
wires by thermally enhanced dihedral planarization. Nano Letters. 2022;22(12):4919-4924.
doi:10.1021/acs.nanolett.2c01549
apa: Lee, W., Louie, S., Evans, A. M., Orchanian, N. M., Stone, I. B., Zhang, B.,
… Venkataraman, L. (2022). Increased molecular conductance in Oligo[n]phenylene
wires by thermally enhanced dihedral planarization. Nano Letters. American
Chemical Society. https://doi.org/10.1021/acs.nanolett.2c01549
chicago: Lee, Woojung, Shayan Louie, Austin M. Evans, Nicholas M. Orchanian, Ilana
B. Stone, Boyuan Zhang, Yujing Wei, Xavier Roy, Colin Nuckolls, and Latha Venkataraman.
“Increased Molecular Conductance in Oligo[n]Phenylene Wires by Thermally Enhanced
Dihedral Planarization.” Nano Letters. American Chemical Society, 2022.
https://doi.org/10.1021/acs.nanolett.2c01549.
ieee: W. Lee et al., “Increased molecular conductance in Oligo[n]phenylene
wires by thermally enhanced dihedral planarization,” Nano Letters, vol.
22, no. 12. American Chemical Society, pp. 4919–4924, 2022.
ista: Lee W, Louie S, Evans AM, Orchanian NM, Stone IB, Zhang B, Wei Y, Roy X, Nuckolls
C, Venkataraman L. 2022. Increased molecular conductance in Oligo[n]phenylene
wires by thermally enhanced dihedral planarization. Nano Letters. 22(12), 4919–4924.
mla: Lee, Woojung, et al. “Increased Molecular Conductance in Oligo[n]Phenylene
Wires by Thermally Enhanced Dihedral Planarization.” Nano Letters, vol.
22, no. 12, American Chemical Society, 2022, pp. 4919–24, doi:10.1021/acs.nanolett.2c01549.
short: W. Lee, S. Louie, A.M. Evans, N.M. Orchanian, I.B. Stone, B. Zhang, Y. Wei,
X. Roy, C. Nuckolls, L. Venkataraman, Nano Letters 22 (2022) 4919–4924.
date_created: 2024-09-06T13:06:35Z
date_published: 2022-05-31T00:00:00Z
date_updated: 2024-12-10T09:40:39Z
day: '31'
doi: 10.1021/acs.nanolett.2c01549
extern: '1'
external_id:
pmid:
- '35640062'
intvolume: ' 22'
issue: '12'
language:
- iso: eng
month: '05'
oa_version: None
page: 4919-4924
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: Increased molecular conductance in Oligo[n]phenylene wires by thermally enhanced
dihedral planarization
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 22
year: '2022'
...
---
OA_place: repository
OA_type: green
_id: '17873'
abstract:
- lang: eng
text: AbstractA critical overview of the theory
of the chirality‐induced spin selectivity (CISS) effect, that is, phenomena in
which the chirality of molecular species imparts significant spin selectivity
to various electron processes, is provided. Based on discussions in a recently
held workshop, and further work published since, the status of CISS effects—in
electron transmission, electron transport, and chemical reactions—is reviewed.
For each, a detailed discussion of the state‐of‐the‐art in theoretical understanding
is provided and remaining challenges and research opportunities are identified.
article_number: '2106629'
article_processing_charge: No
article_type: review
arxiv: 1
author:
- first_name: Ferdinand
full_name: Evers, Ferdinand
last_name: Evers
- first_name: Amnon
full_name: Aharony, Amnon
last_name: Aharony
- first_name: Nir
full_name: Bar‐Gill, Nir
last_name: Bar‐Gill
- first_name: Ora
full_name: Entin‐Wohlman, Ora
last_name: Entin‐Wohlman
- first_name: Per
full_name: Hedegård, Per
last_name: Hedegård
- first_name: Oded
full_name: Hod, Oded
last_name: Hod
- first_name: Pavel
full_name: Jelinek, Pavel
last_name: Jelinek
- first_name: Grzegorz
full_name: Kamieniarz, Grzegorz
last_name: Kamieniarz
- first_name: Mikhail
full_name: Lemeshko, Mikhail
last_name: Lemeshko
- first_name: Karen
full_name: Michaeli, Karen
last_name: Michaeli
- first_name: Vladimiro
full_name: Mujica, Vladimiro
last_name: Mujica
- first_name: Ron
full_name: Naaman, Ron
last_name: Naaman
- first_name: Yossi
full_name: Paltiel, Yossi
last_name: Paltiel
- first_name: Sivan
full_name: Refaely‐Abramson, Sivan
last_name: Refaely‐Abramson
- first_name: Oren
full_name: Tal, Oren
last_name: Tal
- first_name: Jos
full_name: Thijssen, Jos
last_name: Thijssen
- first_name: Michael
full_name: Thoss, Michael
last_name: Thoss
- first_name: Jan M.
full_name: van Ruitenbeek, Jan M.
last_name: van Ruitenbeek
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
- first_name: David H.
full_name: Waldeck, David H.
last_name: Waldeck
- first_name: Binghai
full_name: Yan, Binghai
last_name: Yan
- first_name: Leeor
full_name: Kronik, Leeor
last_name: Kronik
citation:
ama: 'Evers F, Aharony A, Bar‐Gill N, et al. Theory of chirality induced spin selectivity:
Progress and challenges. Advanced Materials. 2022;34(13). doi:10.1002/adma.202106629'
apa: 'Evers, F., Aharony, A., Bar‐Gill, N., Entin‐Wohlman, O., Hedegård, P., Hod,
O., … Kronik, L. (2022). Theory of chirality induced spin selectivity: Progress
and challenges. Advanced Materials. Wiley. https://doi.org/10.1002/adma.202106629'
chicago: 'Evers, Ferdinand, Amnon Aharony, Nir Bar‐Gill, Ora Entin‐Wohlman, Per
Hedegård, Oded Hod, Pavel Jelinek, et al. “Theory of Chirality Induced Spin Selectivity:
Progress and Challenges.” Advanced Materials. Wiley, 2022. https://doi.org/10.1002/adma.202106629.'
ieee: 'F. Evers et al., “Theory of chirality induced spin selectivity: Progress
and challenges,” Advanced Materials, vol. 34, no. 13. Wiley, 2022.'
ista: 'Evers F, Aharony A, Bar‐Gill N, Entin‐Wohlman O, Hedegård P, Hod O, Jelinek
P, Kamieniarz G, Lemeshko M, Michaeli K, Mujica V, Naaman R, Paltiel Y, Refaely‐Abramson
S, Tal O, Thijssen J, Thoss M, van Ruitenbeek JM, Venkataraman L, Waldeck DH,
Yan B, Kronik L. 2022. Theory of chirality induced spin selectivity: Progress
and challenges. Advanced Materials. 34(13), 2106629.'
mla: 'Evers, Ferdinand, et al. “Theory of Chirality Induced Spin Selectivity: Progress
and Challenges.” Advanced Materials, vol. 34, no. 13, 2106629, Wiley, 2022,
doi:10.1002/adma.202106629.'
short: F. Evers, A. Aharony, N. Bar‐Gill, O. Entin‐Wohlman, P. Hedegård, O. Hod,
P. Jelinek, G. Kamieniarz, M. Lemeshko, K. Michaeli, V. Mujica, R. Naaman, Y.
Paltiel, S. Refaely‐Abramson, O. Tal, J. Thijssen, M. Thoss, J.M. van Ruitenbeek,
L. Venkataraman, D.H. Waldeck, B. Yan, L. Kronik, Advanced Materials 34 (2022).
date_created: 2024-09-06T13:07:43Z
date_published: 2022-04-01T00:00:00Z
date_updated: 2024-12-10T09:43:10Z
day: '01'
doi: 10.1002/adma.202106629
extern: '1'
external_id:
arxiv:
- '2108.09998'
pmid:
- '35064943'
intvolume: ' 34'
issue: '13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2108.09998
month: '04'
oa: 1
oa_version: Preprint
pmid: 1
publication: Advanced Materials
publication_identifier:
issn:
- 0935-9648
- 1521-4095
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Theory of chirality induced spin selectivity: Progress and challenges'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2022'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '17874'
abstract:
- lang: eng
text: Redox-active two-dimensional polymers (RA-2DPs) are promising lithium battery
organic cathode materials due to their regular porosities and high chemical stabilities.
However, weak electrical conductivities inherent to the non-conjugated molecular
motifs used thus far limit device performance and the practical relevance of these
materials. We herein address this problem by developing a modular approach to
construct π-conjugated RA-2DPs with a new polycyclic aromatic redox-active building
block PDI-DA. Efficient imine-condensation between PDI-DA and two polyfunctional
amine nodes followed by quantitative alkyl chain removal produced RA-2DPs TAPPy-PDI
and TAPB-PDI as conjugated, porous, polycrystalline networks. In-plane conjugation
and permanent porosity endow these materials with high electrical conductivity
and high ion diffusion rates. As such, both RA-2DPs function as organic cathode
materials with good rate performance and excellent cycling stability. Importantly,
the improved design enables higher areal mass-loadings than were previously available,
which drives a practical demonstration of TAPPy-PDI as the power source for a
series of LED lights. Collectively, this investigation discloses viable synthetic
methodologies and design principles for the realization of high-performance organic
cathode materials.
article_processing_charge: Yes
article_type: original
author:
- first_name: Zexin
full_name: Jin, Zexin
last_name: Jin
- first_name: Qian
full_name: Cheng, Qian
last_name: Cheng
- first_name: Austin M.
full_name: Evans, Austin M.
last_name: Evans
- first_name: Jesse
full_name: Gray, Jesse
last_name: Gray
- first_name: Ruiwen
full_name: Zhang, Ruiwen
last_name: Zhang
- first_name: Si Tong
full_name: Bao, Si Tong
last_name: Bao
- first_name: Fengkai
full_name: Wei, Fengkai
last_name: Wei
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
- first_name: Yuan
full_name: Yang, Yuan
last_name: Yang
- first_name: Colin
full_name: Nuckolls, Colin
last_name: Nuckolls
citation:
ama: Jin Z, Cheng Q, Evans AM, et al. π-Conjugated redox-active two-dimensional
polymers as organic cathode materials. Chemical Science. 2022;13(12):3533-3538.
doi:10.1039/d1sc07157b
apa: Jin, Z., Cheng, Q., Evans, A. M., Gray, J., Zhang, R., Bao, S. T., … Nuckolls,
C. (2022). π-Conjugated redox-active two-dimensional polymers as organic cathode
materials. Chemical Science. Royal Society of Chemistry. https://doi.org/10.1039/d1sc07157b
chicago: Jin, Zexin, Qian Cheng, Austin M. Evans, Jesse Gray, Ruiwen Zhang, Si Tong
Bao, Fengkai Wei, Latha Venkataraman, Yuan Yang, and Colin Nuckolls. “π-Conjugated
Redox-Active Two-Dimensional Polymers as Organic Cathode Materials.” Chemical
Science. Royal Society of Chemistry, 2022. https://doi.org/10.1039/d1sc07157b.
ieee: Z. Jin et al., “π-Conjugated redox-active two-dimensional polymers
as organic cathode materials,” Chemical Science, vol. 13, no. 12. Royal
Society of Chemistry, pp. 3533–3538, 2022.
ista: Jin Z, Cheng Q, Evans AM, Gray J, Zhang R, Bao ST, Wei F, Venkataraman L,
Yang Y, Nuckolls C. 2022. π-Conjugated redox-active two-dimensional polymers as
organic cathode materials. Chemical Science. 13(12), 3533–3538.
mla: Jin, Zexin, et al. “π-Conjugated Redox-Active Two-Dimensional Polymers as Organic
Cathode Materials.” Chemical Science, vol. 13, no. 12, Royal Society of
Chemistry, 2022, pp. 3533–38, doi:10.1039/d1sc07157b.
short: Z. Jin, Q. Cheng, A.M. Evans, J. Gray, R. Zhang, S.T. Bao, F. Wei, L. Venkataraman,
Y. Yang, C. Nuckolls, Chemical Science 13 (2022) 3533–3538.
date_created: 2024-09-06T13:08:38Z
date_published: 2022-03-08T00:00:00Z
date_updated: 2024-12-10T09:54:17Z
day: '08'
doi: 10.1039/d1sc07157b
extern: '1'
intvolume: ' 13'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1039/D1SC07157B
month: '03'
oa: 1
oa_version: Published Version
page: 3533-3538
publication: Chemical Science
publication_identifier:
eissn:
- 2041-6539
issn:
- 2041-6520
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: π-Conjugated redox-active two-dimensional polymers as organic cathode materials
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: 13
year: '2022'
...
---
OA_type: closed access
_id: '17875'
abstract:
- lang: eng
text: Nanoscale plasmonic structures have been primarily characterized through scattering
studies, but electroluminescence offers an exciting alternative from a technological
standpoint by removing the need for optical excitation. In sub-nanometer biased
junctions, electronic tunneling can serve as the excitation source for plasmon-coupled
electroluminescence, but the gap size dependence to this plasmonic enhancement
has not been characterized. Here, we simultaneously probe the electroluminescence
and conductance of Au tunnel junctions. We find that plasmonic enhancement increases
as the gap size is reduced for junctions biased between 1.4 and 1.8 V, consistent
with the behavior of charge transfer plasmons. At biases above 1.9 V, we see decreasing
plasmonic enhancement with the decreasing gap, showing quenching due to tunneling
in remarkable agreement with the trends observed for high energy plasmons in scattering
experiments. Critically, we find that plasmonic enhancement of electroluminescence
is gap size-dependent and, furthermore, is in agreement with the nature of modes
excited by scattering.
article_processing_charge: No
article_type: original
author:
- first_name: Angela L.
full_name: Paoletta, Angela L.
last_name: Paoletta
- first_name: E-Dean
full_name: Fung, E-Dean
last_name: Fung
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
citation:
ama: Paoletta AL, Fung E-D, Venkataraman L. Gap size-dependent plasmonic enhancement
in electroluminescent tunnel junctions. ACS Photonics. 2022;9(2):688-693.
doi:10.1021/acsphotonics.1c01757
apa: Paoletta, A. L., Fung, E.-D., & Venkataraman, L. (2022). Gap size-dependent
plasmonic enhancement in electroluminescent tunnel junctions. ACS Photonics.
American Chemical Society. https://doi.org/10.1021/acsphotonics.1c01757
chicago: Paoletta, Angela L., E-Dean Fung, and Latha Venkataraman. “Gap Size-Dependent
Plasmonic Enhancement in Electroluminescent Tunnel Junctions.” ACS Photonics.
American Chemical Society, 2022. https://doi.org/10.1021/acsphotonics.1c01757.
ieee: A. L. Paoletta, E.-D. Fung, and L. Venkataraman, “Gap size-dependent plasmonic
enhancement in electroluminescent tunnel junctions,” ACS Photonics, vol.
9, no. 2. American Chemical Society, pp. 688–693, 2022.
ista: Paoletta AL, Fung E-D, Venkataraman L. 2022. Gap size-dependent plasmonic
enhancement in electroluminescent tunnel junctions. ACS Photonics. 9(2), 688–693.
mla: Paoletta, Angela L., et al. “Gap Size-Dependent Plasmonic Enhancement in Electroluminescent
Tunnel Junctions.” ACS Photonics, vol. 9, no. 2, American Chemical Society,
2022, pp. 688–93, doi:10.1021/acsphotonics.1c01757.
short: A.L. Paoletta, E.-D. Fung, L. Venkataraman, ACS Photonics 9 (2022) 688–693.
date_created: 2024-09-06T13:09:45Z
date_published: 2022-01-14T00:00:00Z
date_updated: 2024-12-10T10:01:03Z
day: '14'
doi: 10.1021/acsphotonics.1c01757
extern: '1'
intvolume: ' 9'
issue: '2'
language:
- iso: eng
month: '01'
oa_version: None
page: 688-693
publication: ACS Photonics
publication_identifier:
issn:
- 2330-4022
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Gap size-dependent plasmonic enhancement in electroluminescent tunnel junctions
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9
year: '2022'
...
---
_id: '18191'
abstract:
- lang: eng
text: 'Large-scale quantum devices provide insights beyond the reach of classical
simulations. However, for a reliable and verifiable quantum simulation, the building
blocks of the quantum device require exquisite benchmarking. This benchmarking
of large-scale dynamical quantum systems represents a major challenge due to lack
of efficient tools for their simulation. Here, we present a scalable algorithm
based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum
systems. We illustrate our approach using a model for a forefront quantum simulation
platform: ultracold atoms in optical lattices. Specifically, we show that our
algorithm is able to reconstruct the Hamiltonian of an arbitrary sized bosonic
ladder system using an accessible amount of experimental measurements. We are
able to significantly increase the previously known parameter precision.'
article_number: '023302'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Agnes
full_name: Valenti, Agnes
last_name: Valenti
- first_name: Guliuxin
full_name: Jin, Guliuxin
last_name: Jin
- first_name: Julian
full_name: Leonard, Julian
id: b75b3f45-7995-11ef-9bfd-9a9cd02c3577
last_name: Leonard
- first_name: Sebastian D.
full_name: Huber, Sebastian D.
last_name: Huber
- first_name: Eliska
full_name: Greplova, Eliska
last_name: Greplova
citation:
ama: Valenti A, Jin G, Leonard J, Huber SD, Greplova E. Scalable Hamiltonian learning
for large-scale out-of-equilibrium quantum dynamics. Physical Review A.
2022;105(2). doi:10.1103/physreva.105.023302
apa: Valenti, A., Jin, G., Leonard, J., Huber, S. D., & Greplova, E. (2022).
Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics.
Physical Review A. American Physical Society. https://doi.org/10.1103/physreva.105.023302
chicago: Valenti, Agnes, Guliuxin Jin, Julian Leonard, Sebastian D. Huber, and Eliska
Greplova. “Scalable Hamiltonian Learning for Large-Scale out-of-Equilibrium Quantum
Dynamics.” Physical Review A. American Physical Society, 2022. https://doi.org/10.1103/physreva.105.023302.
ieee: A. Valenti, G. Jin, J. Leonard, S. D. Huber, and E. Greplova, “Scalable Hamiltonian
learning for large-scale out-of-equilibrium quantum dynamics,” Physical Review
A, vol. 105, no. 2. American Physical Society, 2022.
ista: Valenti A, Jin G, Leonard J, Huber SD, Greplova E. 2022. Scalable Hamiltonian
learning for large-scale out-of-equilibrium quantum dynamics. Physical Review
A. 105(2), 023302.
mla: Valenti, Agnes, et al. “Scalable Hamiltonian Learning for Large-Scale out-of-Equilibrium
Quantum Dynamics.” Physical Review A, vol. 105, no. 2, 023302, American
Physical Society, 2022, doi:10.1103/physreva.105.023302.
short: A. Valenti, G. Jin, J. Leonard, S.D. Huber, E. Greplova, Physical Review
A 105 (2022).
date_created: 2024-10-07T11:46:53Z
date_published: 2022-02-01T00:00:00Z
date_updated: 2024-10-08T10:00:23Z
day: '01'
doi: 10.1103/physreva.105.023302
extern: '1'
external_id:
arxiv:
- '2103.01240'
intvolume: ' 105'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2103.01240
month: '02'
oa: 1
oa_version: Preprint
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: Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 105
year: '2022'
...
---
_id: '18211'
abstract:
- lang: eng
text: 'Deep neural networks are known to be vulnerable to malicious perturbations.
Current methods for improving adversarial robustness make use of either implicit
or explicit regularization, with the latter is usually based on adversarial training.
Randomized smoothing, the averaging of the classifier outputs over a random distribution
centered in the sample, has been shown to guarantee a classifier’s performance
subject to bounded perturbations of the input. In this work, we study the application
of randomized smoothing to improve performance on unperturbed data and increase
robustness to adversarial attacks. We propose to combine smoothing along with
adversarial training and randomization approaches, and find that doing so significantly
improves the resilience compared to the baseline. We examine our method’s performance
on common whitebox (FGSM, PGD) and black-box (transferable attack and NAttack)
attacks on CIFAR-10 and CIFAR-100, and determine that for a low number of iterations,
smoothing provides a significant performance boost that persists even for perturbations
with a high attack norm, . For example, under a PGD-10 attack on CIFAR-10 using
Wide-ResNet28-4, we achieve 60.3% accuracy for infinity norm ∞ = 8/255 and 13.1%
accuracy for ∞ = 35/255 – outperforming previous art by 3% and 6%, respectively.
We achieve nearly twice the accuracy on ∞ = 35/255 and even more so for perturbations
with higher infinity norm. A reference implementation of the proposed method is
provided. '
article_processing_charge: No
article_type: original
author:
- first_name: Yaniv
full_name: Nemcovsky, Yaniv
last_name: Nemcovsky
- first_name: Evgenii
full_name: Zheltonozhskii, Evgenii
last_name: Zheltonozhskii
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Brian
full_name: Chmiel, Brian
last_name: Chmiel
- 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: Nemcovsky Y, Zheltonozhskii E, Baskin C, Chmiel B, Bronstein AM, Mendelson
A. Adversarial robustness via noise injection in smoothed models. Applied Intelligence.
2022;53(8):9483-9498. doi:10.1007/s10489-022-03423-5
apa: Nemcovsky, Y., Zheltonozhskii, E., Baskin, C., Chmiel, B., Bronstein, A. M.,
& Mendelson, A. (2022). Adversarial robustness via noise injection in smoothed
models. Applied Intelligence. Springer Nature. https://doi.org/10.1007/s10489-022-03423-5
chicago: Nemcovsky, Yaniv, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alex
M. Bronstein, and Avi Mendelson. “Adversarial Robustness via Noise Injection in
Smoothed Models.” Applied Intelligence. Springer Nature, 2022. https://doi.org/10.1007/s10489-022-03423-5.
ieee: Y. Nemcovsky, E. Zheltonozhskii, C. Baskin, B. Chmiel, A. M. Bronstein, and
A. Mendelson, “Adversarial robustness via noise injection in smoothed models,”
Applied Intelligence, vol. 53, no. 8. Springer Nature, pp. 9483–9498, 2022.
ista: Nemcovsky Y, Zheltonozhskii E, Baskin C, Chmiel B, Bronstein AM, Mendelson
A. 2022. Adversarial robustness via noise injection in smoothed models. Applied
Intelligence. 53(8), 9483–9498.
mla: Nemcovsky, Yaniv, et al. “Adversarial Robustness via Noise Injection in Smoothed
Models.” Applied Intelligence, vol. 53, no. 8, Springer Nature, 2022, pp.
9483–98, doi:10.1007/s10489-022-03423-5.
short: Y. Nemcovsky, E. Zheltonozhskii, C. Baskin, B. Chmiel, A.M. Bronstein, A.
Mendelson, Applied Intelligence 53 (2022) 9483–9498.
date_created: 2024-10-08T12:47:53Z
date_published: 2022-08-09T00:00:00Z
date_updated: 2024-10-09T11:04:54Z
day: '09'
doi: 10.1007/s10489-022-03423-5
extern: '1'
intvolume: ' 53'
issue: '8'
language:
- iso: eng
month: '08'
oa_version: None
page: 9483-9498
publication: Applied Intelligence
publication_identifier:
eissn:
- 1573-7497
issn:
- 0924-669X
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Adversarial robustness via noise injection in smoothed models
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 53
year: '2022'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '18220'
abstract:
- lang: eng
text: Synonymous codons translate into the same amino acid. Although the identity
of synonymous codons is often considered inconsequential to the final protein
structure, there is mounting evidence for an association between the two. Our
study examined this association using regression and classification models, finding
that codon sequences predict protein backbone dihedral angles with a lower error
than amino acid sequences, and that models trained with true dihedral angles have
better classification of synonymous codons given structural information than models
trained with random dihedral angles. Using this classification approach, we investigated
local codon–codon dependencies and tested whether synonymous codon identity can
be predicted more accurately from codon context than amino acid context alone,
and most specifically which codon context position carries the most predictive
power.
article_number: '21968'
article_processing_charge: Yes
article_type: original
author:
- first_name: Linor
full_name: Ackerman-Schraier, Linor
last_name: Ackerman-Schraier
- first_name: Aviv A.
full_name: Rosenberg, Aviv A.
last_name: Rosenberg
- first_name: Ailie
full_name: Marx, Ailie
last_name: Marx
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: Ackerman-Schraier L, Rosenberg AA, Marx A, Bronstein AM. Machine learning approaches
demonstrate that protein structures carry information about their genetic coding.
Scientific Reports. 2022;12. doi:10.1038/s41598-022-25874-z
apa: Ackerman-Schraier, L., Rosenberg, A. A., Marx, A., & Bronstein, A. M. (2022).
Machine learning approaches demonstrate that protein structures carry information
about their genetic coding. Scientific Reports. Springer Nature. https://doi.org/10.1038/s41598-022-25874-z
chicago: Ackerman-Schraier, Linor, Aviv A. Rosenberg, Ailie Marx, and Alex M. Bronstein.
“Machine Learning Approaches Demonstrate That Protein Structures Carry Information
about Their Genetic Coding.” Scientific Reports. Springer Nature, 2022.
https://doi.org/10.1038/s41598-022-25874-z.
ieee: L. Ackerman-Schraier, A. A. Rosenberg, A. Marx, and A. M. Bronstein, “Machine
learning approaches demonstrate that protein structures carry information about
their genetic coding,” Scientific Reports, vol. 12. Springer Nature, 2022.
ista: Ackerman-Schraier L, Rosenberg AA, Marx A, Bronstein AM. 2022. Machine learning
approaches demonstrate that protein structures carry information about their genetic
coding. Scientific Reports. 12, 21968.
mla: Ackerman-Schraier, Linor, et al. “Machine Learning Approaches Demonstrate That
Protein Structures Carry Information about Their Genetic Coding.” Scientific
Reports, vol. 12, 21968, Springer Nature, 2022, doi:10.1038/s41598-022-25874-z.
short: L. Ackerman-Schraier, A.A. Rosenberg, A. Marx, A.M. Bronstein, Scientific
Reports 12 (2022).
date_created: 2024-10-08T12:52:29Z
date_published: 2022-12-20T00:00:00Z
date_updated: 2024-10-14T09:46:06Z
day: '20'
doi: 10.1038/s41598-022-25874-z
extern: '1'
external_id:
pmid:
- '36539476'
intvolume: ' 12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41598-022-25874-z
month: '12'
oa: 1
oa_version: Published Version
pmid: 1
publication: Scientific Reports
publication_identifier:
issn:
- 2045-2322
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Machine learning approaches demonstrate that protein structures carry information
about their genetic coding
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 12
year: '2022'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '18221'
abstract:
- lang: eng
text: Synonymous codons translate into chemically identical amino acids. Once considered
inconsequential to the formation of the protein product, there is evidence to
suggest that codon usage affects co-translational protein folding and the final
structure of the expressed protein. Here we develop a method for computing and
comparing codon-specific Ramachandran plots and demonstrate that the backbone
dihedral angle distributions of some synonymous codons are distinguishable with
statistical significance for some secondary structures. This shows that there
exists a dependence between codon identity and backbone torsion of the translated
amino acid. Although these findings cannot pinpoint the causal direction of this
dependence, we discuss the vast biological implications should coding be shown
to directly shape protein conformation and demonstrate the usefulness of this
method as a tool for probing associations between codon usage and protein structure.
Finally, we urge for the inclusion of exact genetic information into structural
databases.
article_number: '2815'
article_processing_charge: Yes
article_type: original
author:
- first_name: Aviv A.
full_name: Rosenberg, Aviv A.
last_name: Rosenberg
- first_name: Ailie
full_name: Marx, Ailie
last_name: Marx
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: Rosenberg AA, Marx A, Bronstein AM. Codon-specific Ramachandran plots show
amino acid backbone conformation depends on identity of the translated codon.
Nature Communications. 2022;13. doi:10.1038/s41467-022-30390-9
apa: Rosenberg, A. A., Marx, A., & Bronstein, A. M. (2022). Codon-specific Ramachandran
plots show amino acid backbone conformation depends on identity of the translated
codon. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-022-30390-9
chicago: Rosenberg, Aviv A., Ailie Marx, and Alex M. Bronstein. “Codon-Specific
Ramachandran Plots Show Amino Acid Backbone Conformation Depends on Identity of
the Translated Codon.” Nature Communications. Springer Nature, 2022. https://doi.org/10.1038/s41467-022-30390-9.
ieee: A. A. Rosenberg, A. Marx, and A. M. Bronstein, “Codon-specific Ramachandran
plots show amino acid backbone conformation depends on identity of the translated
codon,” Nature Communications, vol. 13. Springer Nature, 2022.
ista: Rosenberg AA, Marx A, Bronstein AM. 2022. Codon-specific Ramachandran plots
show amino acid backbone conformation depends on identity of the translated codon.
Nature Communications. 13, 2815.
mla: Rosenberg, Aviv A., et al. “Codon-Specific Ramachandran Plots Show Amino Acid
Backbone Conformation Depends on Identity of the Translated Codon.” Nature
Communications, vol. 13, 2815, Springer Nature, 2022, doi:10.1038/s41467-022-30390-9.
short: A.A. Rosenberg, A. Marx, A.M. Bronstein, Nature Communications 13 (2022).
date_created: 2024-10-08T12:53:01Z
date_published: 2022-05-20T00:00:00Z
date_updated: 2024-10-14T09:49:02Z
day: '20'
doi: 10.1038/s41467-022-30390-9
extern: '1'
external_id:
pmid:
- '35595777'
intvolume: ' 13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41467-022-30390-9
month: '05'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nature Communications
publication_identifier:
issn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Codon-specific Ramachandran plots show amino acid backbone conformation depends
on identity of the translated codon
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2022'
...
---
OA_place: publisher
OA_type: free access
_id: '18222'
abstract:
- lang: eng
text: "STUDY QUESTION: What is the accuracy and agreement of embryologists when
assessing the implantation probability of blastocysts using time-lapse imaging
(TLI), and can it be improved with a data-driven algorithm?\r\n\r\nSUMMARY ANSWER:
The overall interobserver agreement of a large panel of embryologists was moderate
and prediction accuracy was modest, while the purpose-built artificial intelligence
model generally resulted in higher performance metrics.\r\n\r\nWHAT IS KNOWN ALREADY:
Previous studies have demonstrated significant interobserver variability amongst
embryologists when assessing embryo quality. However, data concerning embryologists’
ability to predict implantation probability using TLI is still lacking. Emerging
technologies based on data-driven tools have shown great promise for improving
embryo selection and predicting clinical outcomes.\r\n\r\nSTUDY DESIGN, SIZE,
DURATION: TLI video files of 136 embryos with known implantation data were retrospectively
collected from two clinical sites between 2018 and 2019 for the performance assessment
of 36 embryologists and comparison with a deep neural network (DNN).\r\n\r\nPARTICIPANTS/MATERIALS,
SETTING, METHODS: We recruited 39 embryologists from 13 different countries. All
participants were blinded to clinical outcomes. A total of 136 TLI videos of embryos
that reached the blastocyst stage were used for this experiment. Each embryo’s
likelihood of successfully implanting was assessed by 36 embryologists, providing
implantation probability grades (IPGs) from 1 to 5, where 1 indicates a very low
likelihood of implantation and 5 indicates a very high likelihood. Subsequently,
three embryologists with over 5 years of experience provided Gardner scores. All
136 blastocysts were categorized into three quality groups based on their Gardner
scores. Embryologist predictions were then converted into predictions of implantation
(IPG ≥ 3) and no implantation (IPG ≤ 2). Embryologists’ performance and agreement
were assessed using Fleiss kappa coefficient. A 10-fold cross-validation DNN was
developed to provide IPGs for TLI video files. The model’s performance was compared
to that of the embryologists.\r\n\r\nMAIN RESULTS AND THE ROLE OF CHANCE: Logistic
regression was employed for the following confounding variables: country of residence,
academic level, embryo scoring system, log years of experience and experience
using TLI. None were found to have a statistically significant impact on embryologist
performance at α = 0.05. The average implantation prediction accuracy for the
embryologists was 51.9% for all embryos (N = 136). The average accuracy of the
embryologists when assessing top quality and poor quality embryos (according to
the Gardner score categorizations) was 57.5% and 57.4%, respectively, and 44.6%
for fair quality embryos. Overall interobserver agreement was moderate (κ = 0.56,
N = 136). The best agreement was achieved in the poor + top quality group (κ = 0.65,
N = 77), while the agreement in the fair quality group was lower (κ = 0.25, N = 59).
The DNN showed an overall accuracy rate of 62.5%, with accuracies of 62.2%, 61%
and 65.6% for the poor, fair and top quality groups, respectively. The AUC for
the DNN was higher than that of the embryologists overall (0.70 DNN vs 0.61 embryologists)
as well as in all of the Gardner groups (DNN vs embryologists—Poor: 0.69 vs 0.62;
Fair: 0.67 vs 0.53; Top: 0.77 vs 0.54).\r\n\r\nLIMITATIONS, REASONS FOR CAUTION:
Blastocyst assessment was performed using video files acquired from time-lapse
incubators, where each video contained data from a single focal plane. Clinical
data regarding the underlying cause of infertility and endometrial thickness before
the transfer was not available, yet may explain implantation failure and lower
accuracy of IPGs. Implantation was defined as the presence of a gestational sac,
whereas the detection of fetal heartbeat is a more robust marker of embryo viability.
The raw data were anonymized to the extent that it was not possible to quantify
the number of unique patients and cycles included in the study, potentially masking
the effect of bias from a limited patient pool. Furthermore, the lack of demographic
data makes it difficult to draw conclusions on how representative the dataset
was of the wider population. Finally, embryologists were required to assess the
implantation potential, not embryo quality. Although this is not the traditional
approach to embryo evaluation, morphology/morphokinetics as a means of assessing
embryo quality is believed to be strongly correlated with viability and, for some
methods, implantation potential.\r\n\r\nWIDER IMPLICATIONS OF THE FINDINGS: Embryo
selection is a key element in IVF success and continues to be a challenge. Improving
the predictive ability could assist in optimizing implantation success rates and
other clinical outcomes and could minimize the financial and emotional burden
on the patient. This study demonstrates moderate agreement rates between embryologists,
likely due to the subjective nature of embryo assessment. In particular, we found
that average embryologist accuracy and agreement were significantly lower for
fair quality embryos when compared with that for top and poor quality embryos.
Using data-driven algorithms as an assistive tool may help IVF professionals increase
success rates and promote much needed standardization in the IVF clinic. Our results
indicate a need for further research regarding technological advancement in this
field."
article_processing_charge: No
article_type: original
author:
- first_name: Daniel E
full_name: Fordham, Daniel E
last_name: Fordham
- first_name: Dror
full_name: Rosentraub, Dror
last_name: Rosentraub
- first_name: Avital L
full_name: Polsky, Avital L
last_name: Polsky
- first_name: Talia
full_name: Aviram, Talia
last_name: Aviram
- first_name: Yotam
full_name: Wolf, Yotam
last_name: Wolf
- first_name: Oriel
full_name: Perl, Oriel
last_name: Perl
- first_name: Asnat
full_name: Devir, Asnat
last_name: Devir
- first_name: Shahar
full_name: Rosentraub, Shahar
last_name: Rosentraub
- first_name: David H
full_name: Silver, David H
last_name: Silver
- first_name: Yael
full_name: Gold Zamir, Yael
last_name: Gold Zamir
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Miguel
full_name: Lara Lara, Miguel
last_name: Lara Lara
- first_name: Jara
full_name: Ben Nagi, Jara
last_name: Ben Nagi
- first_name: Adrian
full_name: Alvarez, Adrian
last_name: Alvarez
- first_name: Santiago
full_name: Munné, Santiago
last_name: Munné
citation:
ama: 'Fordham DE, Rosentraub D, Polsky AL, et al. Embryologist agreement when assessing
blastocyst implantation probability: Is data-driven prediction the solution to
embryo assessment subjectivity? Human Reproduction. 2022;37(10):2275-2290.
doi:10.1093/humrep/deac171'
apa: 'Fordham, D. E., Rosentraub, D., Polsky, A. L., Aviram, T., Wolf, Y., Perl,
O., … Munné, S. (2022). Embryologist agreement when assessing blastocyst implantation
probability: Is data-driven prediction the solution to embryo assessment subjectivity?
Human Reproduction. Oxford University Press. https://doi.org/10.1093/humrep/deac171'
chicago: 'Fordham, Daniel E, Dror Rosentraub, Avital L Polsky, Talia Aviram, Yotam
Wolf, Oriel Perl, Asnat Devir, et al. “Embryologist Agreement When Assessing Blastocyst
Implantation Probability: Is Data-Driven Prediction the Solution to Embryo Assessment
Subjectivity?” Human Reproduction. Oxford University Press, 2022. https://doi.org/10.1093/humrep/deac171.'
ieee: 'D. E. Fordham et al., “Embryologist agreement when assessing blastocyst
implantation probability: Is data-driven prediction the solution to embryo assessment
subjectivity?,” Human Reproduction, vol. 37, no. 10. Oxford University
Press, pp. 2275–2290, 2022.'
ista: 'Fordham DE, Rosentraub D, Polsky AL, Aviram T, Wolf Y, Perl O, Devir A, Rosentraub
S, Silver DH, Gold Zamir Y, Bronstein AM, Lara Lara M, Ben Nagi J, Alvarez A,
Munné S. 2022. Embryologist agreement when assessing blastocyst implantation probability:
Is data-driven prediction the solution to embryo assessment subjectivity? Human
Reproduction. 37(10), 2275–2290.'
mla: 'Fordham, Daniel E., et al. “Embryologist Agreement When Assessing Blastocyst
Implantation Probability: Is Data-Driven Prediction the Solution to Embryo Assessment
Subjectivity?” Human Reproduction, vol. 37, no. 10, Oxford University Press,
2022, pp. 2275–90, doi:10.1093/humrep/deac171.'
short: D.E. Fordham, D. Rosentraub, A.L. Polsky, T. Aviram, Y. Wolf, O. Perl, A.
Devir, S. Rosentraub, D.H. Silver, Y. Gold Zamir, A.M. Bronstein, M. Lara Lara,
J. Ben Nagi, A. Alvarez, S. Munné, Human Reproduction 37 (2022) 2275–2290.
date_created: 2024-10-08T12:53:20Z
date_published: 2022-10-01T00:00:00Z
date_updated: 2024-10-14T09:54:40Z
day: '01'
doi: 10.1093/humrep/deac171
extern: '1'
intvolume: ' 37'
issue: '10'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1093/humrep/deac171
month: '10'
oa: 1
oa_version: Published Version
page: 2275-2290
publication: Human Reproduction
publication_identifier:
eissn:
- 1460-2350
issn:
- 0268-1161
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Embryologist agreement when assessing blastocyst implantation probability:
Is data-driven prediction the solution to embryo assessment subjectivity?'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2022'
...
---
OA_type: closed access
_id: '18223'
abstract:
- lang: eng
text: The term silent mutation is commonly used to describe (1) a change in the
DNA sequence that does not result in an observable effect on the organism’s phenotype;
and (2) a synonymous mutation where the nucleotide change leaves the translated
amino acid sequence unchanged. When Christian Anfinsen showed that a folded and
active protein could be denatured to lose structure and activity and then subsequently
renatured to regain the same structure and activity it appeared that the native,
thermodynamically stable, structure of a protein depends only on the amino acid
sequence and solution conditions (Anfinsen and Haber 1961). This experiment suggested
that, once translated, proteins carry no memory of the genetic sequence and led
to one of the most erroneous assumptions in modern science; synonymous codons
were long considered silent, a mutation of the type that has no effect on an organism’s
phenotype.
article_processing_charge: No
author:
- first_name: Aviv A.
full_name: Rosenberg, Aviv A.
last_name: Rosenberg
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Ailie
full_name: Marx, Ailie
last_name: Marx
citation:
ama: 'Rosenberg AA, Bronstein AM, Marx A. Recording Silence – Accurate Annotation
of the Genetic Sequence Is Required to Better Understand How Synonymous Coding
Affects Protein Structure and Disease. In: Sauna ZE, Kimchi-Sarfaty C, eds. Single
Nucleotide Polymorphisms. Cham: Springer Nature; 2022:37-47. doi:10.1007/978-3-031-05616-1_3'
apa: 'Rosenberg, A. A., Bronstein, A. M., & Marx, A. (2022). Recording Silence
– Accurate Annotation of the Genetic Sequence Is Required to Better Understand
How Synonymous Coding Affects Protein Structure and Disease. In Z. E. Sauna &
C. Kimchi-Sarfaty (Eds.), Single Nucleotide Polymorphisms (pp. 37–47).
Cham: Springer Nature. https://doi.org/10.1007/978-3-031-05616-1_3'
chicago: 'Rosenberg, Aviv A., Alex M. Bronstein, and Ailie Marx. “Recording Silence
– Accurate Annotation of the Genetic Sequence Is Required to Better Understand
How Synonymous Coding Affects Protein Structure and Disease.” In Single Nucleotide
Polymorphisms, edited by Zuben E. Sauna and Chava Kimchi-Sarfaty, 37–47. Cham:
Springer Nature, 2022. https://doi.org/10.1007/978-3-031-05616-1_3.'
ieee: 'A. A. Rosenberg, A. M. Bronstein, and A. Marx, “Recording Silence – Accurate
Annotation of the Genetic Sequence Is Required to Better Understand How Synonymous
Coding Affects Protein Structure and Disease,” in Single Nucleotide Polymorphisms,
Z. E. Sauna and C. Kimchi-Sarfaty, Eds. Cham: Springer Nature, 2022, pp. 37–47.'
ista: 'Rosenberg AA, Bronstein AM, Marx A. 2022.Recording Silence – Accurate Annotation
of the Genetic Sequence Is Required to Better Understand How Synonymous Coding
Affects Protein Structure and Disease. In: Single Nucleotide Polymorphisms. ,
37–47.'
mla: Rosenberg, Aviv A., et al. “Recording Silence – Accurate Annotation of the
Genetic Sequence Is Required to Better Understand How Synonymous Coding Affects
Protein Structure and Disease.” Single Nucleotide Polymorphisms, edited
by Zuben E. Sauna and Chava Kimchi-Sarfaty, Springer Nature, 2022, pp. 37–47,
doi:10.1007/978-3-031-05616-1_3.
short: A.A. Rosenberg, A.M. Bronstein, A. Marx, in:, Z.E. Sauna, C. Kimchi-Sarfaty
(Eds.), Single Nucleotide Polymorphisms, Springer Nature, Cham, 2022, pp. 37–47.
date_created: 2024-10-08T12:53:44Z
date_published: 2022-08-10T00:00:00Z
date_updated: 2024-10-14T09:58:21Z
day: '10'
doi: 10.1007/978-3-031-05616-1_3
editor:
- first_name: Zuben E.
full_name: Sauna, Zuben E.
last_name: Sauna
- first_name: Chava
full_name: Kimchi-Sarfaty, Chava
last_name: Kimchi-Sarfaty
extern: '1'
language:
- iso: eng
month: '08'
oa_version: None
page: 37-47
place: Cham
publication: Single Nucleotide Polymorphisms
publication_identifier:
eisbn:
- '9783031056161'
isbn:
- '9783031056147'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Recording Silence – Accurate Annotation of the Genetic Sequence Is Required
to Better Understand How Synonymous Coding Affects Protein Structure and Disease
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
OA_place: repository
_id: '18224'
abstract:
- lang: eng
text: Learning from one or few visual examples is one of the key capabilities of
humans since early infancy, but is still a significant challenge for modern AI
systems. While considerable progress has been achieved in few-shot learning from
a few image examples, much less attention has been given to the verbal descriptions
that are usually provided to infants when they are presented with a new object.
In this paper, we focus on the role of additional semantics that can significantly
facilitate few-shot visual learning. Building upon recent advances in few-shot
learning with additional semantic information, we demonstrate that further improvements
are possible by combining multiple and richer semantics (category labels, attributes,
and natural language descriptions). Using these ideas, we offer the community
new results on the popular miniImageNet and CUB few-shot benchmarks, comparing
favorably to the previous state-of-the-art results for both visual only and visual
plus semantics-based approaches. We also performed an ablation study investigating
the components and design choices of our approach. Code available on github.com/EliSchwartz/mutiple-semantics.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Eli
full_name: Schwartz, Eli
last_name: Schwartz
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
- 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: Schwartz E, Karlinsky L, Feris R, Giryes R, Bronstein AM. Baby steps towards
few-shot learning with multiple semantics. Pattern Recognition Letters.
2022;160:142-147. doi:10.1016/j.patrec.2022.06.012
apa: Schwartz, E., Karlinsky, L., Feris, R., Giryes, R., & Bronstein, A. M.
(2022). Baby steps towards few-shot learning with multiple semantics. Pattern
Recognition Letters. Elsevier. https://doi.org/10.1016/j.patrec.2022.06.012
chicago: Schwartz, Eli, Leonid Karlinsky, Rogerio Feris, Raja Giryes, and Alex M.
Bronstein. “Baby Steps towards Few-Shot Learning with Multiple Semantics.” Pattern
Recognition Letters. Elsevier, 2022. https://doi.org/10.1016/j.patrec.2022.06.012.
ieee: E. Schwartz, L. Karlinsky, R. Feris, R. Giryes, and A. M. Bronstein, “Baby
steps towards few-shot learning with multiple semantics,” Pattern Recognition
Letters, vol. 160. Elsevier, pp. 142–147, 2022.
ista: Schwartz E, Karlinsky L, Feris R, Giryes R, Bronstein AM. 2022. Baby steps
towards few-shot learning with multiple semantics. Pattern Recognition Letters.
160, 142–147.
mla: Schwartz, Eli, et al. “Baby Steps towards Few-Shot Learning with Multiple Semantics.”
Pattern Recognition Letters, vol. 160, Elsevier, 2022, pp. 142–47, doi:10.1016/j.patrec.2022.06.012.
short: E. Schwartz, L. Karlinsky, R. Feris, R. Giryes, A.M. Bronstein, Pattern Recognition
Letters 160 (2022) 142–147.
date_created: 2024-10-08T12:54:03Z
date_published: 2022-08-01T00:00:00Z
date_updated: 2024-10-14T10:58:20Z
day: '01'
doi: 10.1016/j.patrec.2022.06.012
extern: '1'
external_id:
arxiv:
- '1906.01905'
intvolume: ' 160'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1906.01905
month: '08'
oa: 1
oa_version: Preprint
page: 142-147
publication: Pattern Recognition Letters
publication_identifier:
issn:
- 0167-8655
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Baby steps towards few-shot learning with multiple semantics
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 160
year: '2022'
...
---
OA_place: publisher
_id: '18225'
abstract:
- lang: eng
text: "Isometric feature mapping is an established time-honored algorithm in manifold
learning and non-linear dimensionality reduction. Its prominence can be attributed
to the output of a coherent global low-dimensional representation of data by preserving
intrinsic distances. In order to enable an efficient and more applicable isometric
feature mapping, a diverse set of sophisticated advancements have been proposed
to the original algorithm to incorporate important factors like sparsity of computation,
conformality, topological constraints and spectral geometry. However, a significant
shortcoming of most approaches is the dependence on large-scale dense-spectral
decompositions and the inability to generalize to points far away from the sampling
of the manifold.\r\nIn this paper, we explore an unsupervised deep learning approach
for computing distance-preserving maps for non-linear dimensionality reduction.
We demonstrate that our framework is general enough to incorporate all previous
advancements and show a significantly improved local and non-local generalization
of the isometric mapping. Our approach involves training with only a few landmark
points and avoids the need for population of dense matrices as well as computing
their spectral decomposition."
article_number: '104461'
article_processing_charge: No
article_type: original
author:
- first_name: Gautam
full_name: Pai, Gautam
last_name: Pai
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Ronen
full_name: Talmon, Ronen
last_name: Talmon
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Pai G, Bronstein AM, Talmon R, Kimmel R. Deep isometric maps. Image and
Vision Computing. 2022;123. doi:10.1016/j.imavis.2022.104461
apa: Pai, G., Bronstein, A. M., Talmon, R., & Kimmel, R. (2022). Deep isometric
maps. Image and Vision Computing. Elsevier. https://doi.org/10.1016/j.imavis.2022.104461
chicago: Pai, Gautam, Alex M. Bronstein, Ronen Talmon, and Ron Kimmel. “Deep Isometric
Maps.” Image and Vision Computing. Elsevier, 2022. https://doi.org/10.1016/j.imavis.2022.104461.
ieee: G. Pai, A. M. Bronstein, R. Talmon, and R. Kimmel, “Deep isometric maps,”
Image and Vision Computing, vol. 123. Elsevier, 2022.
ista: Pai G, Bronstein AM, Talmon R, Kimmel R. 2022. Deep isometric maps. Image
and Vision Computing. 123, 104461.
mla: Pai, Gautam, et al. “Deep Isometric Maps.” Image and Vision Computing,
vol. 123, 104461, Elsevier, 2022, doi:10.1016/j.imavis.2022.104461.
short: G. Pai, A.M. Bronstein, R. Talmon, R. Kimmel, Image and Vision Computing
123 (2022).
date_created: 2024-10-08T12:54:22Z
date_published: 2022-07-01T00:00:00Z
date_updated: 2024-10-14T11:03:26Z
day: '01'
doi: 10.1016/j.imavis.2022.104461
extern: '1'
intvolume: ' 123'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.imavis.2022.104461
month: '07'
oa: 1
oa_version: Published Version
publication: Image and Vision Computing
publication_identifier:
issn:
- 0262-8856
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Deep isometric maps
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 123
year: '2022'
...
---
OA_place: publisher
OA_type: hybrid
_id: '18226'
abstract:
- lang: eng
text: Spontaneous parametric downconversion (SPDC) in quantum optics is an invaluable
resource for the realization of high-dimensional qudits with spatial modes of
light. One of the main open challenges is how to directly generate a desirable
qudit state in the SPDC process. This problem can be addressed through advanced
computational learning methods; however, due to difficulties in modeling the SPDC
process by a fully differentiable algorithm, progress has been limited. Here,
we overcome these limitations and introduce a physically constrained and differentiable
model, validated against experimental results for shaped pump beams and structured
crystals, capable of learning the relevant interaction parameters in the process.
We avoid any restrictions induced by the stochastic nature of our physical model
and integrate the dynamic equations governing the evolution under the SPDC Hamiltonian.
We solve the inverse problem of designing a nonlinear quantum optical system that
achieves the desired quantum state of downconverted photon pairs. The desired
states are defined using either the second-order correlations between different
spatial modes or by specifying the required density matrix. By learning nonlinear
photonic crystal structures as well as different pump shapes, we successfully
show how to generate maximally entangled states. Furthermore, we simulate all-optical
coherent control over the generated quantum state by actively changing the profile
of the pump beam. Our work can be useful for applications such as novel designs
of high-dimensional quantum key distribution and quantum information processing
protocols. In addition, our method can be readily applied for controlling other
degrees of freedom of light in the SPDC process, such as spectral and temporal
properties, and may even be used in condensed-matter systems having a similar
interaction Hamiltonian.
article_processing_charge: No
article_type: original
author:
- first_name: Eyal
full_name: Rozenberg, Eyal
last_name: Rozenberg
- first_name: Aviv
full_name: Karnieli, Aviv
last_name: Karnieli
- first_name: Ofir
full_name: Yesharim, Ofir
last_name: Yesharim
- first_name: Joshua
full_name: Foley-Comer, Joshua
last_name: Foley-Comer
- first_name: Sivan
full_name: Trajtenberg-Mills, Sivan
last_name: Trajtenberg-Mills
- first_name: Daniel
full_name: Freedman, Daniel
last_name: Freedman
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Ady
full_name: Arie, Ady
last_name: Arie
citation:
ama: Rozenberg E, Karnieli A, Yesharim O, et al. Inverse design of spontaneous parametric
downconversion for generation of high-dimensional qudits. Optica. 2022;9(6):602-615.
doi:10.1364/optica.451115
apa: Rozenberg, E., Karnieli, A., Yesharim, O., Foley-Comer, J., Trajtenberg-Mills,
S., Freedman, D., … Arie, A. (2022). Inverse design of spontaneous parametric
downconversion for generation of high-dimensional qudits. Optica. Optica
Publishing Group. https://doi.org/10.1364/optica.451115
chicago: Rozenberg, Eyal, Aviv Karnieli, Ofir Yesharim, Joshua Foley-Comer, Sivan
Trajtenberg-Mills, Daniel Freedman, Alex M. Bronstein, and Ady Arie. “Inverse
Design of Spontaneous Parametric Downconversion for Generation of High-Dimensional
Qudits.” Optica. Optica Publishing Group, 2022. https://doi.org/10.1364/optica.451115.
ieee: E. Rozenberg et al., “Inverse design of spontaneous parametric downconversion
for generation of high-dimensional qudits,” Optica, vol. 9, no. 6. Optica
Publishing Group, pp. 602–615, 2022.
ista: Rozenberg E, Karnieli A, Yesharim O, Foley-Comer J, Trajtenberg-Mills S, Freedman
D, Bronstein AM, Arie A. 2022. Inverse design of spontaneous parametric downconversion
for generation of high-dimensional qudits. Optica. 9(6), 602–615.
mla: Rozenberg, Eyal, et al. “Inverse Design of Spontaneous Parametric Downconversion
for Generation of High-Dimensional Qudits.” Optica, vol. 9, no. 6, Optica
Publishing Group, 2022, pp. 602–15, doi:10.1364/optica.451115.
short: E. Rozenberg, A. Karnieli, O. Yesharim, J. Foley-Comer, S. Trajtenberg-Mills,
D. Freedman, A.M. Bronstein, A. Arie, Optica 9 (2022) 602–615.
date_created: 2024-10-08T12:54:43Z
date_published: 2022-06-06T00:00:00Z
date_updated: 2024-10-14T11:07:29Z
day: '06'
doi: 10.1364/optica.451115
extern: '1'
intvolume: ' 9'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1364/OPTICA.451115
month: '06'
oa: 1
oa_version: Published Version
page: 602-615
publication: Optica
publication_identifier:
issn:
- 2334-2536
publication_status: published
publisher: Optica Publishing Group
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inverse design of spontaneous parametric downconversion for generation of high-dimensional
qudits
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9
year: '2022'
...
---
_id: '18227'
abstract:
- lang: eng
text: Existing cross-modal hashing methods ignore the informative multimodal joint
information and cannot fully exploit the semantic labels. In this paper, we propose
a deep fused two-step cross-modal hashing (DFTH) framework with multiple semantic
supervision. In the first step, DFTH learns unified hash codes for instances by
a fusion network. Semantic label and similarity reconstruction have been introduced
to acquire binary codes that are informative, discriminative and semantic similarity
preserving. In the second step, two modality-specific hash networks are learned
under the supervision of common hash codes reconstruction, label reconstruction,
and intra-modal and inter-modal semantic similarity reconstruction. The modality-specific
hash networks can generate semantic preserving binary codes for out-of-sample
queries. To deal with the vanishing gradients of binarization, continuous differentiable
tanh is introduced to approximate the discrete sign function, making the networks
able to back-propagate by automatic gradient computation. Extensive experiments
on MIRFlickr25K and NUS-WIDE show the superiority of DFTH over state-of-the-art
methods.
article_processing_charge: No
article_type: original
author:
- first_name: Peipei
full_name: Kang, Peipei
last_name: Kang
- first_name: Zehang
full_name: Lin, Zehang
last_name: Lin
- first_name: Zhenguo
full_name: Yang, Zhenguo
last_name: Yang
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Qing
full_name: Li, Qing
last_name: Li
- first_name: Wenyin
full_name: Liu, Wenyin
last_name: Liu
citation:
ama: Kang P, Lin Z, Yang Z, Bronstein AM, Li Q, Liu W. Deep fused two-step cross-modal
hashing with multiple semantic supervision. Multimedia Tools and Applications.
2022;81(11):15653-15670. doi:10.1007/s11042-022-12187-6
apa: Kang, P., Lin, Z., Yang, Z., Bronstein, A. M., Li, Q., & Liu, W. (2022).
Deep fused two-step cross-modal hashing with multiple semantic supervision. Multimedia
Tools and Applications. Springer Nature. https://doi.org/10.1007/s11042-022-12187-6
chicago: Kang, Peipei, Zehang Lin, Zhenguo Yang, Alex M. Bronstein, Qing Li, and
Wenyin Liu. “Deep Fused Two-Step Cross-Modal Hashing with Multiple Semantic Supervision.”
Multimedia Tools and Applications. Springer Nature, 2022. https://doi.org/10.1007/s11042-022-12187-6.
ieee: P. Kang, Z. Lin, Z. Yang, A. M. Bronstein, Q. Li, and W. Liu, “Deep fused
two-step cross-modal hashing with multiple semantic supervision,” Multimedia
Tools and Applications, vol. 81, no. 11. Springer Nature, pp. 15653–15670,
2022.
ista: Kang P, Lin Z, Yang Z, Bronstein AM, Li Q, Liu W. 2022. Deep fused two-step
cross-modal hashing with multiple semantic supervision. Multimedia Tools and Applications.
81(11), 15653–15670.
mla: Kang, Peipei, et al. “Deep Fused Two-Step Cross-Modal Hashing with Multiple
Semantic Supervision.” Multimedia Tools and Applications, vol. 81, no.
11, Springer Nature, 2022, pp. 15653–70, doi:10.1007/s11042-022-12187-6.
short: P. Kang, Z. Lin, Z. Yang, A.M. Bronstein, Q. Li, W. Liu, Multimedia Tools
and Applications 81 (2022) 15653–15670.
date_created: 2024-10-08T12:55:04Z
date_published: 2022-05-01T00:00:00Z
date_updated: 2024-10-14T11:10:00Z
day: '01'
doi: 10.1007/s11042-022-12187-6
extern: '1'
intvolume: ' 81'
issue: '11'
language:
- iso: eng
month: '05'
oa_version: None
page: 15653-15670
publication: Multimedia Tools and Applications
publication_identifier:
eissn:
- 1573-7721
issn:
- 1380-7501
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Deep fused two-step cross-modal hashing with multiple semantic supervision
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 81
year: '2022'
...
---
_id: '18229'
abstract:
- lang: eng
text: We present Self-Classifier – a novel self-supervised end-to-end classification
learning approach. Self-Classifier learns labels and representations simultaneously
in a single-stage end-to-end manner by optimizing for same-class prediction of
two augmented views of the same sample. To guarantee non-degenerate solutions
(i.e., solutions where all labels are assigned to the same class) we propose a
mathematically motivated variant of the cross-entropy loss that has a uniform
prior asserted on the predicted labels. In our theoretical analysis, we prove
that degenerate solutions are not in the set of optimal solutions of our approach.
Self-Classifier is simple to implement and scalable. Unlike other popular unsupervised
classification and contrastive representation learning approaches, it does not
require any form of pre-training, expectation-maximization, pseudo-labeling, external
clustering, a second network, stop-gradient operation, or negative pairs. Despite
its simplicity, our approach sets a new state of the art for unsupervised classification
of ImageNet; and even achieves comparable to state-of-the-art results for unsupervised
representation learning. Code is available at https://github.com/elad-amrani/self-classifier.
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Elad
full_name: Amrani, Elad
last_name: Amrani
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
- 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, Karlinsky L, Bronstein AM. Self-supervised classification network.
In: 17th European Conference on Computer Vision. Vol 13691. Springer Nature;
2022:116-132. doi:10.1007/978-3-031-19821-2_7'
apa: 'Amrani, E., Karlinsky, L., & Bronstein, A. M. (2022). Self-supervised
classification network. In 17th European Conference on Computer Vision
(Vol. 13691, pp. 116–132). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19821-2_7'
chicago: Amrani, Elad, Leonid Karlinsky, and Alex M. Bronstein. “Self-Supervised
Classification Network.” In 17th European Conference on Computer Vision,
13691:116–32. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-19821-2_7.
ieee: E. Amrani, L. Karlinsky, and A. M. Bronstein, “Self-supervised classification
network,” in 17th European Conference on Computer Vision, Tel Aviv, Israel,
2022, vol. 13691, pp. 116–132.
ista: 'Amrani E, Karlinsky L, Bronstein AM. 2022. Self-supervised classification
network. 17th European Conference on Computer Vision. ECCV: European Conference
on Computer Vision, LNCS, vol. 13691, 116–132.'
mla: Amrani, Elad, et al. “Self-Supervised Classification Network.” 17th European
Conference on Computer Vision, vol. 13691, Springer Nature, 2022, pp. 116–32,
doi:10.1007/978-3-031-19821-2_7.
short: E. Amrani, L. Karlinsky, A.M. Bronstein, in:, 17th European Conference on
Computer Vision, Springer Nature, 2022, pp. 116–132.
conference:
end_date: 2022-10-27
location: Tel Aviv, Israel
name: 'ECCV: European Conference on Computer Vision'
start_date: 2022-10-23
date_created: 2024-10-08T12:55:44Z
date_published: 2022-10-23T00:00:00Z
date_updated: 2024-10-15T07:04:39Z
day: '23'
doi: 10.1007/978-3-031-19821-2_7
extern: '1'
external_id:
arxiv:
- '2103.10994'
intvolume: ' 13691'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2103.10994
month: '10'
oa: 1
oa_version: None
page: 116-132
publication: 17th European Conference on Computer Vision
publication_identifier:
eisbn:
- '9783031198212'
eissn:
- 1611-3349
isbn:
- '9783031198205'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/elad-amrani/self-classifier
scopus_import: '1'
status: public
title: Self-supervised classification network
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13691
year: '2022'
...