---
_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
license: https://creativecommons.org/licenses/by-nc/3.0/
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'
...
---
_id: '18230'
abstract:
- lang: eng
text: 'A variety of complex systems, including social and communication networks,
financial markets, biology, and neuroscience are modeled using temporal graphs
that contain a set of nodes and directed timestamped edges. Temporal motifs in
temporal graphs are generalized from subgraph patterns in static graphs in that
they also account for edge ordering and time duration, in addition to the graph
structure. Mining temporal motifs is a fundamental problem used in several application
domains. However, existing software frameworks offer suboptimal performance due
to high algorithmic complexity and irregular memory accesses of temporal motif
mining.This paper presents Mint—a novel accelerator architecture and a programming
model for mining temporal motifs efficiently. We first divide this workload into
three fundamental tasks: search, book-keeping, and backtracking. Based on this,
we propose a task-centric programming model that enables decoupled, asynchronous
execution. This model unlocks massive opportunities for parallelism, and allows
storing task context information on-chip. To best utilize the proposed programming
model, we design a domain-specific hardware accelerator using its data path and
memory subsystem design to cater to the unique workload characteristics of temporal
motif mining. To further improve performance, we propose a novel optimization
called search index memoization that significantly reduces memory traffic. We
comprehensively compare the performance of Mint with state-of-the-art temporal
motif mining software frameworks (both approximate and exact) running on both
CPU and GPU, and show 9×−2576× benefit in performance.'
article_processing_charge: No
author:
- first_name: Nishil
full_name: Talati, Nishil
last_name: Talati
- first_name: Haojie
full_name: Ye, Haojie
last_name: Ye
- first_name: Sanketh
full_name: Vedula, Sanketh
last_name: Vedula
- first_name: Kuan-Yu
full_name: Chen, Kuan-Yu
last_name: Chen
- first_name: Yuhan
full_name: Chen, Yuhan
last_name: Chen
- first_name: Daniel
full_name: Liu, Daniel
last_name: Liu
- first_name: Yichao
full_name: Yuan, Yichao
last_name: Yuan
- first_name: David
full_name: Blaauw, David
last_name: Blaauw
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Trevor
full_name: Mudge, Trevor
last_name: Mudge
- first_name: Ronald
full_name: Dreslinski, Ronald
last_name: Dreslinski
citation:
ama: 'Talati N, Ye H, Vedula S, et al. Mint: An accelerator for mining temporal
motifs. In: 55th IEEE/ACM International Symposium on Microarchitecture.
Institute of Electrical and Electronics Engineers; 2022. doi:10.1109/micro56248.2022.00089'
apa: 'Talati, N., Ye, H., Vedula, S., Chen, K.-Y., Chen, Y., Liu, D., … Dreslinski,
R. (2022). Mint: An accelerator for mining temporal motifs. In 55th IEEE/ACM
International Symposium on Microarchitecture. Chicago, IL, United States:
Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/micro56248.2022.00089'
chicago: 'Talati, Nishil, Haojie Ye, Sanketh Vedula, Kuan-Yu Chen, Yuhan Chen, Daniel
Liu, Yichao Yuan, et al. “Mint: An Accelerator for Mining Temporal Motifs.” In
55th IEEE/ACM International Symposium on Microarchitecture. Institute of
Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/micro56248.2022.00089.'
ieee: 'N. Talati et al., “Mint: An accelerator for mining temporal motifs,”
in 55th IEEE/ACM International Symposium on Microarchitecture, Chicago,
IL, United States, 2022.'
ista: 'Talati N, Ye H, Vedula S, Chen K-Y, Chen Y, Liu D, Yuan Y, Blaauw D, Bronstein
AM, Mudge T, Dreslinski R. 2022. Mint: An accelerator for mining temporal motifs.
55th IEEE/ACM International Symposium on Microarchitecture. MICRO: Symposium on
Microarchitecture.'
mla: 'Talati, Nishil, et al. “Mint: An Accelerator for Mining Temporal Motifs.”
55th IEEE/ACM International Symposium on Microarchitecture, Institute of
Electrical and Electronics Engineers, 2022, doi:10.1109/micro56248.2022.00089.'
short: N. Talati, H. Ye, S. Vedula, K.-Y. Chen, Y. Chen, D. Liu, Y. Yuan, D. Blaauw,
A.M. Bronstein, T. Mudge, R. Dreslinski, in:, 55th IEEE/ACM International Symposium
on Microarchitecture, Institute of Electrical and Electronics Engineers, 2022.
conference:
end_date: 2022-10-05
location: Chicago, IL, United States
name: 'MICRO: Symposium on Microarchitecture'
start_date: 2022-10-01
date_created: 2024-10-08T12:56:03Z
date_published: 2022-10-01T00:00:00Z
date_updated: 2024-10-15T07:14:02Z
day: '01'
doi: 10.1109/micro56248.2022.00089
extern: '1'
language:
- iso: eng
month: '10'
oa_version: None
publication: 55th IEEE/ACM International Symposium on Microarchitecture
publication_identifier:
eisbn:
- '9781665462723'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Mint: An accelerator for mining temporal motifs'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
OA_place: repository
OA_type: green
_id: '18231'
abstract:
- lang: eng
text: 'The success of learning with noisy labels (LNL) methods relies heavily on
the success of a warm-up stage where standard supervised training is performed
using the full (noisy) training set. In this paper, we identify a "warm-up obstacle":
the inability of standard warm-up stages to train high quality feature extractors
and avert memorization of noisy labels. We propose "Contrast to Divide" (C2D),
a simple framework that solves this problem by pre-training the feature extractor
in a self-supervised fashion. Using self-supervised pre-training boosts the performance
of existing LNL approaches by drastically reducing the warm-up stage''s susceptibility
to noise level, shortening its duration, and improving extracted feature quality.
C2D works out of the box with existing methods and demonstrates markedly improved
performance, especially in the high noise regime, where we get a boost of more
than 27% for CIFAR-100 with 90% noise over the previous state of the art. In real-life
noise settings, C2D trained on mini-WebVision outperforms previous works both
in WebVision and ImageNet validation sets by 3% top-1 accuracy. We perform an
in-depth analysis of the framework, including investigating the performance of
different pre-training approaches and estimating the effective upper bound of
the LNL performance with semi-supervised learning. Code for reproducing our experiments
is available at https://github.com/ContrastToDivide/C2D.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Evgenii
full_name: Zheltonozhskii, Evgenii
last_name: Zheltonozhskii
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Avi
full_name: Mendelson, Avi
last_name: Mendelson
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Or
full_name: Litany, Or
last_name: Litany
citation:
ama: 'Zheltonozhskii E, Baskin C, Mendelson A, Bronstein AM, Litany O. Contrast
to divide: Self-supervised pre-training for learning with noisy labels. In: IEEE/CVF
Winter Conference on Applications of Computer Vision. Institute of Electrical
and Electronics Engineers; 2022:387-397. doi:10.1109/wacv51458.2022.00046'
apa: 'Zheltonozhskii, E., Baskin, C., Mendelson, A., Bronstein, A. M., & Litany,
O. (2022). Contrast to divide: Self-supervised pre-training for learning with
noisy labels. In IEEE/CVF Winter Conference on Applications of Computer Vision
(pp. 387–397). Waikoloa, HI, United States: Institute of Electrical and Electronics
Engineers. https://doi.org/10.1109/wacv51458.2022.00046'
chicago: 'Zheltonozhskii, Evgenii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein,
and Or Litany. “Contrast to Divide: Self-Supervised Pre-Training for Learning
with Noisy Labels.” In IEEE/CVF Winter Conference on Applications of Computer
Vision, 387–97. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/wacv51458.2022.00046.'
ieee: 'E. Zheltonozhskii, C. Baskin, A. Mendelson, A. M. Bronstein, and O. Litany,
“Contrast to divide: Self-supervised pre-training for learning with noisy labels,”
in IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa,
HI, United States, 2022, pp. 387–397.'
ista: 'Zheltonozhskii E, Baskin C, Mendelson A, Bronstein AM, Litany O. 2022. Contrast
to divide: Self-supervised pre-training for learning with noisy labels. IEEE/CVF
Winter Conference on Applications of Computer Vision. WACV: Winter Conference
on Applications of Computer Vision, 387–397.'
mla: 'Zheltonozhskii, Evgenii, et al. “Contrast to Divide: Self-Supervised Pre-Training
for Learning with Noisy Labels.” IEEE/CVF Winter Conference on Applications
of Computer Vision, Institute of Electrical and Electronics Engineers, 2022,
pp. 387–97, doi:10.1109/wacv51458.2022.00046.'
short: E. Zheltonozhskii, C. Baskin, A. Mendelson, A.M. Bronstein, O. Litany, in:,
IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical
and Electronics Engineers, 2022, pp. 387–397.
conference:
end_date: 2022-01-08
location: Waikoloa, HI, United States
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2022-01-03
date_created: 2024-10-08T12:56:20Z
date_published: 2022-02-15T00:00:00Z
date_updated: 2024-10-15T07:27:12Z
day: '15'
doi: 10.1109/wacv51458.2022.00046
extern: '1'
external_id:
arxiv:
- '2103.13646'
language:
- iso: eng
month: '02'
oa_version: Preprint
page: 387-397
publication: IEEE/CVF Winter Conference on Applications of Computer Vision
publication_identifier:
eisbn:
- '9781665409155'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ContrastToDivide/C2D
scopus_import: '1'
status: public
title: 'Contrast to divide: Self-supervised pre-training for learning with noisy labels'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '18232'
abstract:
- lang: eng
text: Cross-modal retrieval aims to retrieve related items across different modalities,
for example, using an image query to retrieve related text. The existing deep
methods ignore both the intra-modal and inter-modal intra-class low-rank structures
when fusing various modalities, which decreases the retrieval performance. In
this paper, two deep models (denoted as ILCMR and Semi-ILCMR) based on intra-class
low-rank regularization are proposed for supervised and semi-supervised cross-modal
retrieval, respectively. Specifically, ILCMR integrates the image network and
text network into a unified framework to learn a common feature space by imposing
three regularization terms to fuse the cross-modal data. First, to align them
in the label space, we utilize semantic consistency regularization to convert
the data representations to probability distributions over the classes. Second,
we introduce an intra-modal low-rank regularization, which encourages the intra-class
samples that originate from the same space to be more relevant in the common feature
space. Third, an inter-modal low-rank regularization is applied to reduce the
cross-modal discrepancy. To enable the low-rank regularization to be optimized
using automatic gradients during network back-propagation, we propose the rank-r
approximation and specify the explicit gradients for theoretical completeness.
In addition to the three regularization terms that rely on label information incorporated
by ILCMR, we propose Semi-ILCMR in the semi-supervised regime, which introduces
a low-rank constraint before projecting the general representations into the common
feature space. Extensive experiments on four public cross-modal datasets demonstrate
the superiority of ILCMR and Semi-ILCMR over other 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: Xiaozhao
full_name: Fang, Xiaozhao
last_name: Fang
- 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, et al. Intra-class low-rank regularization for supervised
and semi-supervised cross-modal retrieval. Applied Intelligence. 2022;52:33-54.
doi:10.1007/s10489-021-02308-3
apa: Kang, P., Lin, Z., Yang, Z., Fang, X., Bronstein, A. M., Li, Q., & Liu,
W. (2022). Intra-class low-rank regularization for supervised and semi-supervised
cross-modal retrieval. Applied Intelligence. Springer Nature. https://doi.org/10.1007/s10489-021-02308-3
chicago: Kang, Peipei, Zehang Lin, Zhenguo Yang, Xiaozhao Fang, Alex M. Bronstein,
Qing Li, and Wenyin Liu. “Intra-Class Low-Rank Regularization for Supervised and
Semi-Supervised Cross-Modal Retrieval.” Applied Intelligence. Springer
Nature, 2022. https://doi.org/10.1007/s10489-021-02308-3.
ieee: P. Kang et al., “Intra-class low-rank regularization for supervised
and semi-supervised cross-modal retrieval,” Applied Intelligence, vol.
52. Springer Nature, pp. 33–54, 2022.
ista: Kang P, Lin Z, Yang Z, Fang X, Bronstein AM, Li Q, Liu W. 2022. Intra-class
low-rank regularization for supervised and semi-supervised cross-modal retrieval.
Applied Intelligence. 52, 33–54.
mla: Kang, Peipei, et al. “Intra-Class Low-Rank Regularization for Supervised and
Semi-Supervised Cross-Modal Retrieval.” Applied Intelligence, vol. 52,
Springer Nature, 2022, pp. 33–54, doi:10.1007/s10489-021-02308-3.
short: P. Kang, Z. Lin, Z. Yang, X. Fang, A.M. Bronstein, Q. Li, W. Liu, Applied
Intelligence 52 (2022) 33–54.
date_created: 2024-10-08T12:56:38Z
date_published: 2022-01-01T00:00:00Z
date_updated: 2024-10-15T07:30:00Z
day: '01'
doi: 10.1007/s10489-021-02308-3
extern: '1'
intvolume: ' 52'
language:
- iso: eng
month: '01'
oa_version: None
page: 33-54
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: Intra-class low-rank regularization for supervised and semi-supervised cross-modal
retrieval
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 52
year: '2022'
...
---
_id: '18291'
article_processing_charge: No
author:
- first_name: Georgios
full_name: Katsaros, Georgios
id: 38DB5788-F248-11E8-B48F-1D18A9856A87
last_name: Katsaros
orcid: 0000-0001-8342-202X
- first_name: Daniel
full_name: Jirovec, Daniel
id: 4C473F58-F248-11E8-B48F-1D18A9856A87
last_name: Jirovec
orcid: 0000-0002-7197-4801
citation:
ama: "Katsaros G, Jirovec D. Dynamics of Hole Singlet-Triplet Qubits with Large
\U0001D454-Factor Differences. 2022. doi:10.15479/AT:ISTA:18291"
apa: "Katsaros, G., & Jirovec, D. (2022). Dynamics of Hole Singlet-Triplet Qubits
with Large \U0001D454-Factor Differences. Institute of Science and Technology
Austria. https://doi.org/10.15479/AT:ISTA:18291"
chicago: "Katsaros, Georgios, and Daniel Jirovec. “Dynamics of Hole Singlet-Triplet
Qubits with Large \U0001D454-Factor Differences.” Institute of Science and Technology
Austria, 2022. https://doi.org/10.15479/AT:ISTA:18291."
ieee: "G. Katsaros and D. Jirovec, “Dynamics of Hole Singlet-Triplet Qubits with
Large \U0001D454-Factor Differences.” Institute of Science and Technology Austria,
2022."
ista: "Katsaros G, Jirovec D. 2022. Dynamics of Hole Singlet-Triplet Qubits with
Large \U0001D454-Factor Differences, Institute of Science and Technology Austria,
10.15479/AT:ISTA:18291."
mla: "Katsaros, Georgios, and Daniel Jirovec. Dynamics of Hole Singlet-Triplet
Qubits with Large \U0001D454-Factor Differences. Institute of Science and
Technology Austria, 2022, doi:10.15479/AT:ISTA:18291."
short: G. Katsaros, D. Jirovec, (2022).
corr_author: '1'
date_created: 2024-10-09T19:35:03Z
date_published: 2022-03-01T00:00:00Z
date_updated: 2025-04-15T07:15:24Z
day: '01'
department:
- _id: GeKa
doi: 10.15479/AT:ISTA:18291
file:
- access_level: open_access
checksum: 3128dffbd09267b93c2d0b1425fd3ba2
content_type: application/x-zip-compressed
creator: gkatsaro
date_created: 2024-10-09T19:31:35Z
date_updated: 2024-10-09T19:31:35Z
file_id: '18292'
file_name: SOIPaper.zip
file_size: 25566516
relation: main_file
success: 1
- access_level: open_access
checksum: df077d2f4652afeb3bf100068e88aa48
content_type: text/plain
creator: gkatsaro
date_created: 2024-10-14T18:11:45Z
date_updated: 2024-10-14T18:11:45Z
file_id: '18442'
file_name: Readme.txt
file_size: 6776
relation: main_file
success: 1
file_date_updated: 2024-10-14T18:11:45Z
has_accepted_license: '1'
month: '03'
oa: 1
oa_version: None
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '10920'
relation: research_paper
status: public
status: public
title: "Dynamics of Hole Singlet-Triplet Qubits with Large \U0001D454-Factor Differences"
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: research_data
user_id: 68b8ca59-c5b3-11ee-8790-cd641c68093d
year: '2022'
...
---
OA_place: repository
OA_type: green
_id: '22043'
abstract:
- lang: eng
text: We prove that multisoliton solutions of the Korteweg–de Vries equation are
orbitally stable in H^-1(R). We introduce a variational characterization of multisolitons
that remains meaningful at such low regularity and show that all optimizing sequences
converge to the manifold of multisolitons. The proximity required at the initial
time is uniform across the entire manifold of multisolitons; this had not been
demonstrated previously, even in H^-1.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Rowan
full_name: Killip, Rowan
last_name: Killip
- first_name: Monica
full_name: Visan, Monica
id: 056daca0-b8d1-11f0-964f-f91054abf8ca
last_name: Visan
citation:
ama: Killip R, Vişan M. Orbital stability of KdV multisolitons in H-1. Communications
in Mathematical Physics. 2022;389(3):1445-1473. doi:10.1007/s00220-021-04280-y
apa: Killip, R., & Vişan, M. (2022). Orbital stability of KdV multisolitons
in H-1. Communications in Mathematical Physics. Springer Nature. https://doi.org/10.1007/s00220-021-04280-y
chicago: Killip, Rowan, and Monica Vişan. “Orbital Stability of KdV Multisolitons
in H-1.” Communications in Mathematical Physics. Springer Nature, 2022.
https://doi.org/10.1007/s00220-021-04280-y.
ieee: R. Killip and M. Vişan, “Orbital stability of KdV multisolitons in H-1,” Communications
in Mathematical Physics, vol. 389, no. 3. Springer Nature, pp. 1445–1473,
2022.
ista: Killip R, Vişan M. 2022. Orbital stability of KdV multisolitons in H-1. Communications
in Mathematical Physics. 389(3), 1445–1473.
mla: Killip, Rowan, and Monica Vişan. “Orbital Stability of KdV Multisolitons in
H-1.” Communications in Mathematical Physics, vol. 389, no. 3, Springer
Nature, 2022, pp. 1445–73, doi:10.1007/s00220-021-04280-y.
short: R. Killip, M. Vişan, Communications in Mathematical Physics 389 (2022) 1445–1473.
das_tickbox: '1'
date_created: 2026-06-19T07:46:39Z
date_published: 2022-02-01T00:00:00Z
date_updated: 2026-06-25T07:41:51Z
day: '01'
doi: 10.1007/s00220-021-04280-y
extern: '1'
external_id:
arxiv:
- '2009.06746'
intvolume: ' 389'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2009.06746
month: '02'
oa: 1
oa_version: Preprint
page: 1445-1473
publication: Communications in Mathematical Physics
publication_identifier:
eissn:
- 1432-0916
issn:
- 0010-3616
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Orbital stability of KdV multisolitons in H-1
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 389
year: '2022'
...