---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '17899'
abstract:
- lang: eng
text: Designing highly insulating sub-nanometer molecules is difficult because tunneling
conductance increases exponentially with decreasing molecular length. This challenge
is further enhanced by the fact that most molecules cannot achieve full conductance
suppression with destructive quantum interference. Here, we present results for
a series of small saturated heterocyclic alkanes where we show that conductance
is suppressed due to destructive interference. Using the STM-BJ technique and
density functional theory calculations, we confirm that their single-molecule
junction conductance is lower than analogous alkanes of similar length. We rationalize
the suppression of conductance in the junctions through analysis of the computed
ballistic current density. We find there are highly symmetric ring currents, which
reverse direction at the antiresonance in the Landauer transmission near the Fermi
energy. This pattern has not been seen in earlier studies of larger bicyclic systems
exhibiting interference effects and constitutes clear-cut evidence of destructive
σ-interference. The finding of heterocyclic alkanes with destructive quantum interference
charts a pathway for chemical design of short molecular insulators using organic
molecules.
article_processing_charge: Yes
article_type: original
author:
- first_name: Boyuan
full_name: Zhang, Boyuan
last_name: Zhang
- first_name: Marc H.
full_name: Garner, Marc H.
last_name: Garner
- first_name: Liang
full_name: Li, Liang
last_name: Li
- first_name: Luis M.
full_name: Campos, Luis M.
last_name: Campos
- first_name: Gemma C.
full_name: Solomon, Gemma C.
last_name: Solomon
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
citation:
ama: 'Zhang B, Garner MH, Li L, Campos LM, Solomon GC, Venkataraman L. Destructive
quantum interference in heterocyclic alkanes: The search for ultra-short molecular
insulators. Chemical Science. 2021;12(30):10299-10305. doi:10.1039/d1sc02287c'
apa: 'Zhang, B., Garner, M. H., Li, L., Campos, L. M., Solomon, G. C., & Venkataraman,
L. (2021). Destructive quantum interference in heterocyclic alkanes: The search
for ultra-short molecular insulators. Chemical Science. Royal Society of
Chemistry. https://doi.org/10.1039/d1sc02287c'
chicago: 'Zhang, Boyuan, Marc H. Garner, Liang Li, Luis M. Campos, Gemma C. Solomon,
and Latha Venkataraman. “Destructive Quantum Interference in Heterocyclic Alkanes:
The Search for Ultra-Short Molecular Insulators.” Chemical Science. Royal
Society of Chemistry, 2021. https://doi.org/10.1039/d1sc02287c.'
ieee: 'B. Zhang, M. H. Garner, L. Li, L. M. Campos, G. C. Solomon, and L. Venkataraman,
“Destructive quantum interference in heterocyclic alkanes: The search for ultra-short
molecular insulators,” Chemical Science, vol. 12, no. 30. Royal Society
of Chemistry, pp. 10299–10305, 2021.'
ista: 'Zhang B, Garner MH, Li L, Campos LM, Solomon GC, Venkataraman L. 2021. Destructive
quantum interference in heterocyclic alkanes: The search for ultra-short molecular
insulators. Chemical Science. 12(30), 10299–10305.'
mla: 'Zhang, Boyuan, et al. “Destructive Quantum Interference in Heterocyclic Alkanes:
The Search for Ultra-Short Molecular Insulators.” Chemical Science, vol.
12, no. 30, Royal Society of Chemistry, 2021, pp. 10299–305, doi:10.1039/d1sc02287c.'
short: B. Zhang, M.H. Garner, L. Li, L.M. Campos, G.C. Solomon, L. Venkataraman,
Chemical Science 12 (2021) 10299–10305.
date_created: 2024-09-09T06:42:54Z
date_published: 2021-06-30T00:00:00Z
date_updated: 2024-12-10T10:11:16Z
day: '30'
doi: 10.1039/d1sc02287c
extern: '1'
external_id:
pmid:
- '34476051'
intvolume: ' 12'
issue: '30'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1039/D1SC02287C
month: '06'
oa: 1
oa_version: Published Version
page: 10299-10305
pmid: 1
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: 'Destructive quantum interference in heterocyclic alkanes: The search for ultra-short
molecular insulators'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 12
year: '2021'
...
---
OA_type: closed access
_id: '17900'
abstract:
- lang: eng
text: To rival the performance of modern integrated circuits, single-molecule devices
must be designed to exhibit extremely nonlinear current–voltage (I–V) characteristics1,2,3,4.
A common approach is to design molecular backbones where destructive quantum interference
(QI) between the highest occupied molecular orbital (HOMO) and the lowest unoccupied
molecular orbital (LUMO) produces a nonlinear energy-dependent tunnelling probability
near the electrode Fermi energy (EF)5,6,7,8. However, tuning such systems is not
straightforward, as aligning the frontier orbitals to EF is hard to control9.
Here, we instead create a molecular system where constructive QI between the HOMO
and LUMO is suppressed and destructive QI between the HOMO and strongly coupled
occupied orbitals of opposite phase is enhanced. We use a series of fluorene oligomers
containing a central benzothiadiazole10 unit to demonstrate that this strategy
can be used to create highly nonlinear single-molecule circuits. Notably, we are
able to reproducibly modulate the conductance of a 6-nm molecule by a factor of
more than 10^4.
article_processing_charge: No
author:
- first_name: Julia E.
full_name: Greenwald, Julia E.
last_name: Greenwald
- first_name: Joseph
full_name: Cameron, Joseph
last_name: Cameron
- first_name: Neil J.
full_name: Findlay, Neil J.
last_name: Findlay
- first_name: Tianren
full_name: Fu, Tianren
last_name: Fu
- first_name: Suman
full_name: Gunasekaran, Suman
last_name: Gunasekaran
- first_name: Peter J.
full_name: Skabara, Peter J.
last_name: Skabara
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
citation:
ama: Greenwald JE, Cameron J, Findlay NJ, et al. Highly nonlinear transport across
single-molecule junctions via destructive quantum interference. Nature Nanotechnology.
2021;16(3):313-317. doi:10.1038/s41565-020-00807-x
apa: Greenwald, J. E., Cameron, J., Findlay, N. J., Fu, T., Gunasekaran, S., Skabara,
P. J., & Venkataraman, L. (2021). Highly nonlinear transport across single-molecule
junctions via destructive quantum interference. Nature Nanotechnology.
Springer Nature. https://doi.org/10.1038/s41565-020-00807-x
chicago: Greenwald, Julia E., Joseph Cameron, Neil J. Findlay, Tianren Fu, Suman
Gunasekaran, Peter J. Skabara, and Latha Venkataraman. “Highly Nonlinear Transport
across Single-Molecule Junctions via Destructive Quantum Interference.” Nature
Nanotechnology. Springer Nature, 2021. https://doi.org/10.1038/s41565-020-00807-x.
ieee: J. E. Greenwald et al., “Highly nonlinear transport across single-molecule
junctions via destructive quantum interference,” Nature Nanotechnology,
vol. 16, no. 3. Springer Nature, pp. 313–317, 2021.
ista: Greenwald JE, Cameron J, Findlay NJ, Fu T, Gunasekaran S, Skabara PJ, Venkataraman
L. 2021. Highly nonlinear transport across single-molecule junctions via destructive
quantum interference. Nature Nanotechnology. 16(3), 313–317.
mla: Greenwald, Julia E., et al. “Highly Nonlinear Transport across Single-Molecule
Junctions via Destructive Quantum Interference.” Nature Nanotechnology,
vol. 16, no. 3, Springer Nature, 2021, pp. 313–17, doi:10.1038/s41565-020-00807-x.
short: J.E. Greenwald, J. Cameron, N.J. Findlay, T. Fu, S. Gunasekaran, P.J. Skabara,
L. Venkataraman, Nature Nanotechnology 16 (2021) 313–317.
date_created: 2024-09-09T06:43:51Z
date_published: 2021-03-01T00:00:00Z
date_updated: 2024-12-10T10:20:32Z
day: '01'
doi: 10.1038/s41565-020-00807-x
extern: '1'
external_id:
pmid:
- '33288949'
intvolume: ' 16'
issue: '3'
language:
- iso: eng
month: '03'
oa_version: None
page: 313-317
pmid: 1
publication: Nature Nanotechnology
publication_identifier:
eissn:
- 1748-3395
issn:
- 1748-3387
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Highly nonlinear transport across single-molecule junctions via destructive
quantum interference
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16
year: '2021'
...
---
OA_type: closed access
_id: '17901'
abstract:
- lang: eng
text: A 1,1,2,2-tetrakis(4-aminophenyl)ethene with three paths of π-conjugation,
linear-cis, linear-trans and a cross-conjugation, has been prepared. The molecule
is able to bind to gold electrodes forming molecular junctions for single-molecule
conductance measurements. Only two regimes of conduction are found experimentally.
The modelling of the conductance allows to assign them to through-bond transmission
in the linear case, while the cross-conjugated channel is further assisted by
through-space transmission, partially alleviating the destructive quantum interference.
article_processing_charge: No
article_type: original
author:
- first_name: Samara
full_name: Medina Rivero, Samara
last_name: Medina Rivero
- first_name: Paloma
full_name: García Arroyo, Paloma
last_name: García Arroyo
- first_name: Liang
full_name: Li, Liang
last_name: Li
- first_name: Suman
full_name: Gunasekaran, Suman
last_name: Gunasekaran
- first_name: Thijs
full_name: Stuyver, Thijs
last_name: Stuyver
- first_name: María José
full_name: Mancheño, María José
last_name: Mancheño
- first_name: Mercedes
full_name: Alonso, Mercedes
last_name: Alonso
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
- first_name: José L.
full_name: Segura, José L.
last_name: Segura
- first_name: Juan
full_name: Casado, Juan
last_name: Casado
citation:
ama: Medina Rivero S, García Arroyo P, Li L, et al. Single-molecule conductance
in a unique cross-conjugated tetra(aminoaryl)ethene. Chemical Communications.
2021;57(5):591-594. doi:10.1039/d0cc07124b
apa: Medina Rivero, S., García Arroyo, P., Li, L., Gunasekaran, S., Stuyver, T.,
Mancheño, M. J., … Casado, J. (2021). Single-molecule conductance in a unique
cross-conjugated tetra(aminoaryl)ethene. Chemical Communications. Royal
Society of Chemistry. https://doi.org/10.1039/d0cc07124b
chicago: Medina Rivero, Samara, Paloma García Arroyo, Liang Li, Suman Gunasekaran,
Thijs Stuyver, María José Mancheño, Mercedes Alonso, Latha Venkataraman, José
L. Segura, and Juan Casado. “Single-Molecule Conductance in a Unique Cross-Conjugated
Tetra(Aminoaryl)Ethene.” Chemical Communications. Royal Society of Chemistry,
2021. https://doi.org/10.1039/d0cc07124b.
ieee: S. Medina Rivero et al., “Single-molecule conductance in a unique cross-conjugated
tetra(aminoaryl)ethene,” Chemical Communications, vol. 57, no. 5. Royal
Society of Chemistry, pp. 591–594, 2021.
ista: Medina Rivero S, García Arroyo P, Li L, Gunasekaran S, Stuyver T, Mancheño
MJ, Alonso M, Venkataraman L, Segura JL, Casado J. 2021. Single-molecule conductance
in a unique cross-conjugated tetra(aminoaryl)ethene. Chemical Communications.
57(5), 591–594.
mla: Medina Rivero, Samara, et al. “Single-Molecule Conductance in a Unique Cross-Conjugated
Tetra(Aminoaryl)Ethene.” Chemical Communications, vol. 57, no. 5, Royal
Society of Chemistry, 2021, pp. 591–94, doi:10.1039/d0cc07124b.
short: S. Medina Rivero, P. García Arroyo, L. Li, S. Gunasekaran, T. Stuyver, M.J.
Mancheño, M. Alonso, L. Venkataraman, J.L. Segura, J. Casado, Chemical Communications
57 (2021) 591–594.
date_created: 2024-09-09T06:44:58Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2024-12-10T10:23:49Z
day: '01'
doi: 10.1039/d0cc07124b
extern: '1'
external_id:
pmid:
- '33325935'
intvolume: ' 57'
issue: '5'
language:
- iso: eng
month: '05'
oa_version: None
page: 591-594
pmid: 1
publication: Chemical Communications
publication_identifier:
eissn:
- 1364-548X
issn:
- 1359-7345
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Single-molecule conductance in a unique cross-conjugated tetra(aminoaryl)ethene
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 57
year: '2021'
...
---
_id: '18192'
abstract:
- lang: eng
text: Current quantum simulation experiments are starting to explore nonequilibrium
many-body dynamics in previously inaccessible regimes in terms of system sizes
and timescales. Therefore, the question emerges as to which observables are best
suited to study the dynamics in such quantum many-body systems. Using machine
learning techniques, we investigate the dynamics and, in particular, the thermalization
behavior of an interacting quantum system that undergoes a nonequilibrium phase
transition from an ergodic to a many-body localized phase. We employ supervised
and unsupervised training methods to distinguish nonequilibrium from equilibrium
data, using the network performance as a probe for the thermalization behavior
of the system. We test our methods with experimental snapshots of ultracold atoms
taken with a quantum gas microscope. Our results provide a path to analyze highly
entangled large-scale quantum states for system sizes where numerical calculations
of conventional observables become challenging.
article_number: '150504'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: A.
full_name: Bohrdt, A.
last_name: Bohrdt
- first_name: S.
full_name: Kim, S.
last_name: Kim
- first_name: A.
full_name: Lukin, A.
last_name: Lukin
- first_name: M.
full_name: Rispoli, M.
last_name: Rispoli
- first_name: R.
full_name: Schittko, R.
last_name: Schittko
- first_name: M.
full_name: Knap, M.
last_name: Knap
- first_name: M.
full_name: Greiner, M.
last_name: Greiner
- first_name: Julian
full_name: Leonard, Julian
id: b75b3f45-7995-11ef-9bfd-9a9cd02c3577
last_name: Leonard
citation:
ama: Bohrdt A, Kim S, Lukin A, et al. Analyzing nonequilibrium quantum states through
snapshots with artificial neural networks. Physical Review Letters. 2021;127(15).
doi:10.1103/physrevlett.127.150504
apa: Bohrdt, A., Kim, S., Lukin, A., Rispoli, M., Schittko, R., Knap, M., … Leonard,
J. (2021). Analyzing nonequilibrium quantum states through snapshots with artificial
neural networks. Physical Review Letters. American Physical Society. https://doi.org/10.1103/physrevlett.127.150504
chicago: Bohrdt, A., S. Kim, A. Lukin, M. Rispoli, R. Schittko, M. Knap, M. Greiner,
and Julian Leonard. “Analyzing Nonequilibrium Quantum States through Snapshots
with Artificial Neural Networks.” Physical Review Letters. American Physical
Society, 2021. https://doi.org/10.1103/physrevlett.127.150504.
ieee: A. Bohrdt et al., “Analyzing nonequilibrium quantum states through
snapshots with artificial neural networks,” Physical Review Letters, vol.
127, no. 15. American Physical Society, 2021.
ista: Bohrdt A, Kim S, Lukin A, Rispoli M, Schittko R, Knap M, Greiner M, Leonard
J. 2021. Analyzing nonequilibrium quantum states through snapshots with artificial
neural networks. Physical Review Letters. 127(15), 150504.
mla: Bohrdt, A., et al. “Analyzing Nonequilibrium Quantum States through Snapshots
with Artificial Neural Networks.” Physical Review Letters, vol. 127, no.
15, 150504, American Physical Society, 2021, doi:10.1103/physrevlett.127.150504.
short: A. Bohrdt, S. Kim, A. Lukin, M. Rispoli, R. Schittko, M. Knap, M. Greiner,
J. Leonard, Physical Review Letters 127 (2021).
date_created: 2024-10-07T11:47:11Z
date_published: 2021-10-08T00:00:00Z
date_updated: 2024-10-08T09:58:03Z
day: '08'
doi: 10.1103/physrevlett.127.150504
extern: '1'
external_id:
arxiv:
- '2012.11586'
intvolume: ' 127'
issue: '15'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2012.11586
month: '10'
oa: 1
oa_version: Preprint
publication: Physical Review Letters
publication_identifier:
issn:
- 0031-9007
- 1079-7114
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Analyzing nonequilibrium quantum states through snapshots with artificial neural
networks
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 127
year: '2021'
...
---
_id: '18193'
abstract:
- lang: eng
text: "Topological states of matter, such as fractional quantum Hall states, are
an active field of research due to their exotic excitations. In particular, ultracold
atoms in optical lattices provide a highly controllable and adaptable platform
to study such new types of quantum matter. However, finding a clear route to realize
non-Abelian quantum Hall states in these systems remains challenging. Here we
use the density-matrix renormalization-group (DMRG) method to study the Hofstadter-Bose-Hubbard
model at filling factor \U0001D708=1 and find strong indications that at \U0001D6FC=1/6
magnetic flux quanta per plaquette the ground state is a lattice analog of the
continuum non-Abelian Pfaffian. We study the on-site correlations of the ground
state, which indicate its paired nature at \U0001D708=1, and find an incompressible
state characterized by a charge gap in the bulk. We argue that the emergence of
a charge density wave on thin cylinders and the behavior of the two- and three-particle
correlation functions at short distances provide evidence for the state being
closely related to the continuum Pfaffian. The signatures discussed in this letter
are accessible in current cold atom experiments and we show that the Pfaffian-like
state is readily realizable in few-body systems using adiabatic preparation schemes."
article_number: L161101
article_processing_charge: No
article_type: letter_note
arxiv: 1
author:
- first_name: F. A.
full_name: Palm, F. A.
last_name: Palm
- first_name: M.
full_name: Buser, M.
last_name: Buser
- first_name: Julian
full_name: Leonard, Julian
id: b75b3f45-7995-11ef-9bfd-9a9cd02c3577
last_name: Leonard
- first_name: M.
full_name: Aidelsburger, M.
last_name: Aidelsburger
- first_name: U.
full_name: Schollwöck, U.
last_name: Schollwöck
- first_name: F.
full_name: Grusdt, F.
last_name: Grusdt
citation:
ama: Palm FA, Buser M, Leonard J, Aidelsburger M, Schollwöck U, Grusdt F. Bosonic
Pfaffian state in the Hofstadter-Bose-Hubbard model. Physical Review B.
2021;103(16). doi:10.1103/physrevb.103.l161101
apa: Palm, F. A., Buser, M., Leonard, J., Aidelsburger, M., Schollwöck, U., &
Grusdt, F. (2021). Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model.
Physical Review B. American Physical Society. https://doi.org/10.1103/physrevb.103.l161101
chicago: Palm, F. A., M. Buser, Julian Leonard, M. Aidelsburger, U. Schollwöck,
and F. Grusdt. “Bosonic Pfaffian State in the Hofstadter-Bose-Hubbard Model.”
Physical Review B. American Physical Society, 2021. https://doi.org/10.1103/physrevb.103.l161101.
ieee: F. A. Palm, M. Buser, J. Leonard, M. Aidelsburger, U. Schollwöck, and F. Grusdt,
“Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model,” Physical Review
B, vol. 103, no. 16. American Physical Society, 2021.
ista: Palm FA, Buser M, Leonard J, Aidelsburger M, Schollwöck U, Grusdt F. 2021.
Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model. Physical Review B.
103(16), L161101.
mla: Palm, F. A., et al. “Bosonic Pfaffian State in the Hofstadter-Bose-Hubbard
Model.” Physical Review B, vol. 103, no. 16, L161101, American Physical
Society, 2021, doi:10.1103/physrevb.103.l161101.
short: F.A. Palm, M. Buser, J. Leonard, M. Aidelsburger, U. Schollwöck, F. Grusdt,
Physical Review B 103 (2021).
date_created: 2024-10-07T11:47:51Z
date_published: 2021-04-15T00:00:00Z
date_updated: 2024-10-08T09:55:46Z
day: '15'
doi: 10.1103/physrevb.103.l161101
extern: '1'
external_id:
arxiv:
- '2011.02477'
intvolume: ' 103'
issue: '16'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2011.02477
month: '04'
oa: 1
oa_version: Preprint
publication: Physical Review B
publication_identifier:
eissn:
- 2469-9969
issn:
- 2469-9950
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 103
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '18233'
abstract:
- lang: eng
text: Neural network quantization enables the deployment of large models on resource-constrained
devices. Current post-training quantization methods fall short in terms of accuracy
for INT4 (or lower) but provide reasonable accuracy for INT8 (or above). In this
work, we study the effect of quantization on the structure of the loss landscape.
We show that the structure is flat and separable for mild quantization, enabling
straightforward post-training quantization methods to achieve good results. We
show that with more aggressive quantization, the loss landscape becomes highly
non-separable with steep curvature, making the selection of quantization parameters
more challenging. Armed with this understanding, we design a method that quantizes
the layer parameters jointly, enabling significant accuracy improvement over current
post-training quantization methods. Reference implementation is available at https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yury
full_name: Nahshan, Yury
last_name: Nahshan
- first_name: Brian
full_name: Chmiel, Brian
last_name: Chmiel
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Evgenii
full_name: Zheltonozhskii, Evgenii
last_name: Zheltonozhskii
- first_name: Ron
full_name: Banner, Ron
last_name: Banner
- first_name: 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: Nahshan Y, Chmiel B, Baskin C, et al. Loss aware post-training quantization.
Machine Learning. 2021;110(11-12):3245-3262. doi:10.1007/s10994-021-06053-z
apa: Nahshan, Y., Chmiel, B., Baskin, C., Zheltonozhskii, E., Banner, R., Bronstein,
A. M., & Mendelson, A. (2021). Loss aware post-training quantization. Machine
Learning. Springer Nature. https://doi.org/10.1007/s10994-021-06053-z
chicago: Nahshan, Yury, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron
Banner, Alex M. Bronstein, and Avi Mendelson. “Loss Aware Post-Training Quantization.”
Machine Learning. Springer Nature, 2021. https://doi.org/10.1007/s10994-021-06053-z.
ieee: Y. Nahshan et al., “Loss aware post-training quantization,” Machine
Learning, vol. 110, no. 11–12. Springer Nature, pp. 3245–3262, 2021.
ista: Nahshan Y, Chmiel B, Baskin C, Zheltonozhskii E, Banner R, Bronstein AM, Mendelson
A. 2021. Loss aware post-training quantization. Machine Learning. 110(11–12),
3245–3262.
mla: Nahshan, Yury, et al. “Loss Aware Post-Training Quantization.” Machine Learning,
vol. 110, no. 11–12, Springer Nature, 2021, pp. 3245–62, doi:10.1007/s10994-021-06053-z.
short: Y. Nahshan, B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, A.M. Bronstein,
A. Mendelson, Machine Learning 110 (2021) 3245–3262.
date_created: 2024-10-08T12:57:05Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2024-10-15T07:33:28Z
day: '01'
doi: 10.1007/s10994-021-06053-z
extern: '1'
external_id:
arxiv:
- '1911.07190'
intvolume: ' 110'
issue: 11-12
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1911.07190
month: '12'
oa: 1
oa_version: Preprint
page: 3245-3262
publication: Machine Learning
publication_identifier:
eissn:
- 1573-0565
issn:
- 0885-6125
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq
scopus_import: '1'
status: public
title: Loss aware post-training quantization
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 110
year: '2021'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '18234'
abstract:
- lang: eng
text: Convolutional Neural Networks (CNNs) are very popular in many fields including
computer vision, speech recognition, natural language processing, etc. Though
deep learning leads to groundbreaking performance in those domains, the networks
used are very computationally demanding and are far from being able to perform
in real-time applications even on a GPU, which is not power efficient and therefore
does not suit low power systems such as mobile devices. To overcome this challenge,
some solutions have been proposed for quantizing the weights and activations of
these networks, which accelerate the runtime significantly. Yet, this acceleration
comes at the cost of a larger error unless spatial adjustments are carried out.
The method proposed in this work trains quantized neural networks by noise injection
and a learned clamping, which improve accuracy. This leads to state-of-the-art
results on various regression and classification tasks, e.g., ImageNet classification
with architectures such as ResNet-18/34/50 with as low as 3 bit weights and activations.
We implement the proposed solution on an FPGA to demonstrate its applicability
for low-power real-time applications. The quantization code will become publicly
available upon acceptance.
article_number: '2144'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Evgenii
full_name: Zheltonozhkii, Evgenii
last_name: Zheltonozhkii
- first_name: Tal
full_name: Rozen, Tal
last_name: Rozen
- first_name: Natan
full_name: Liss, Natan
last_name: Liss
- first_name: Yoav
full_name: Chai, Yoav
last_name: Chai
- first_name: Eli
full_name: Schwartz, Eli
last_name: Schwartz
- 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
- first_name: Avi
full_name: Mendelson, Avi
last_name: Mendelson
citation:
ama: 'Baskin C, Zheltonozhkii E, Rozen T, et al. NICE: Noise Injection and Clamping
Estimation for neural network quantization. Mathematics. 2021;9(17). doi:10.3390/math9172144'
apa: 'Baskin, C., Zheltonozhkii, E., Rozen, T., Liss, N., Chai, Y., Schwartz, E.,
… Mendelson, A. (2021). NICE: Noise Injection and Clamping Estimation for neural
network quantization. Mathematics. MDPI. https://doi.org/10.3390/math9172144'
chicago: 'Baskin, Chaim, Evgenii Zheltonozhkii, Tal Rozen, Natan Liss, Yoav Chai,
Eli Schwartz, Raja Giryes, Alex M. Bronstein, and Avi Mendelson. “NICE: Noise
Injection and Clamping Estimation for Neural Network Quantization.” Mathematics.
MDPI, 2021. https://doi.org/10.3390/math9172144.'
ieee: 'C. Baskin et al., “NICE: Noise Injection and Clamping Estimation for
neural network quantization,” Mathematics, vol. 9, no. 17. MDPI, 2021.'
ista: 'Baskin C, Zheltonozhkii E, Rozen T, Liss N, Chai Y, Schwartz E, Giryes R,
Bronstein AM, Mendelson A. 2021. NICE: Noise Injection and Clamping Estimation
for neural network quantization. Mathematics. 9(17), 2144.'
mla: 'Baskin, Chaim, et al. “NICE: Noise Injection and Clamping Estimation for Neural
Network Quantization.” Mathematics, vol. 9, no. 17, 2144, MDPI, 2021, doi:10.3390/math9172144.'
short: C. Baskin, E. Zheltonozhkii, T. Rozen, N. Liss, Y. Chai, E. Schwartz, R.
Giryes, A.M. Bronstein, A. Mendelson, Mathematics 9 (2021).
date_created: 2024-10-08T12:57:24Z
date_published: 2021-09-02T00:00:00Z
date_updated: 2024-10-15T07:37:39Z
day: '02'
doi: 10.3390/math9172144
extern: '1'
external_id:
arxiv:
- '1810.00162'
intvolume: ' 9'
issue: '17'
language:
- iso: eng
month: '09'
oa_version: Published Version
publication: Mathematics
publication_identifier:
issn:
- 2227-7390
publication_status: published
publisher: MDPI
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'NICE: Noise Injection and Clamping Estimation for neural network quantization'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '18235'
abstract:
- lang: eng
text: 'Recently, great progress has been made in the field of Few-Shot Learning
(FSL). While many different methods have been proposed, one of the key factors
leading to higher FSL performance is surprisingly simple. It is the backbone network
architecture used to embed the images of the few-shot tasks. While first works
on FSL resorted to small architectures with just a few convolution layers, recent
works show that large architectures pre-trained on the training portion of FSL
datasets produce strong features that are more easily transferable to novel few-shot
tasks, thus attaining significant gains to methods using them. Despite these observations,
little to no work has been done towards finding the right backbone for FSL. In
this paper we propose MetAdapt that not only meta-searches for an optimized architecture
for FSL using Network Architecture Search (NAS), but also results in a model that
can adaptively ‘re-wire’ itself predicting the better architecture for a given
novel few-shot task. Using the proposed approach we observe strong results on
two popular few-shot benchmarks: miniImageNet and FC100.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Sivan
full_name: Doveh, Sivan
last_name: Doveh
- first_name: Eli
full_name: Schwartz, Eli
last_name: Schwartz
- first_name: Chao
full_name: Xue, Chao
last_name: Xue
- first_name: Rogerio
full_name: Feris, Rogerio
last_name: Feris
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Raja
full_name: Giryes, Raja
last_name: Giryes
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
citation:
ama: 'Doveh S, Schwartz E, Xue C, et al. MetAdapt: Meta-learned task-adaptive architecture
for few-shot classification. Pattern Recognition Letters. 2021;149:130-136.
doi:10.1016/j.patrec.2021.05.010'
apa: 'Doveh, S., Schwartz, E., Xue, C., Feris, R., Bronstein, A. M., Giryes, R.,
& Karlinsky, L. (2021). MetAdapt: Meta-learned task-adaptive architecture
for few-shot classification. Pattern Recognition Letters. Elsevier. https://doi.org/10.1016/j.patrec.2021.05.010'
chicago: 'Doveh, Sivan, Eli Schwartz, Chao Xue, Rogerio Feris, Alex M. Bronstein,
Raja Giryes, and Leonid Karlinsky. “MetAdapt: Meta-Learned Task-Adaptive Architecture
for Few-Shot Classification.” Pattern Recognition Letters. Elsevier, 2021.
https://doi.org/10.1016/j.patrec.2021.05.010.'
ieee: 'S. Doveh et al., “MetAdapt: Meta-learned task-adaptive architecture
for few-shot classification,” Pattern Recognition Letters, vol. 149. Elsevier,
pp. 130–136, 2021.'
ista: 'Doveh S, Schwartz E, Xue C, Feris R, Bronstein AM, Giryes R, Karlinsky L.
2021. MetAdapt: Meta-learned task-adaptive architecture for few-shot classification.
Pattern Recognition Letters. 149, 130–136.'
mla: 'Doveh, Sivan, et al. “MetAdapt: Meta-Learned Task-Adaptive Architecture for
Few-Shot Classification.” Pattern Recognition Letters, vol. 149, Elsevier,
2021, pp. 130–36, doi:10.1016/j.patrec.2021.05.010.'
short: S. Doveh, E. Schwartz, C. Xue, R. Feris, A.M. Bronstein, R. Giryes, L. Karlinsky,
Pattern Recognition Letters 149 (2021) 130–136.
date_created: 2024-10-08T12:57:53Z
date_published: 2021-09-01T00:00:00Z
date_updated: 2024-10-15T07:39:56Z
day: '01'
doi: 10.1016/j.patrec.2021.05.010
extern: '1'
external_id:
arxiv:
- '1912.00412'
intvolume: ' 149'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1912.00412
month: '09'
oa: 1
oa_version: Preprint
page: 130-136
publication: Pattern Recognition Letters
publication_identifier:
issn:
- 0167-8655
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'MetAdapt: Meta-learned task-adaptive architecture for few-shot classification'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 149
year: '2021'
...
---
OA_place: publisher
OA_type: free access
_id: '18236'
abstract:
- lang: eng
text: 'Despite their great promise, artificial intelligence (AI) systems have yet
to become ubiquitous in the daily practice of medicine largely due to several
crucial unmet needs of healthcare practitioners. These include lack of explanations
in clinically meaningful terms, handling the presence of unknown medical conditions,
and transparency regarding the system’s limitations, both in terms of statistical
performance as well as recognizing situations for which the system’s predictions
are irrelevant. We articulate these unmet clinical needs as machine-learning (ML)
problems and systematically address them with cutting-edge ML techniques. We focus
on electrocardiogram (ECG) analysis as an example domain in which AI has great
potential and tackle two challenging tasks: the detection of a heterogeneous mix
of known and unknown arrhythmias from ECG and the identification of underlying
cardio-pathology from segments annotated as normal sinus rhythm recorded in patients
with an intermittent arrhythmia. We validate our methods by simulating a screening
for arrhythmias in a large-scale population while adhering to statistical significance
requirements. Specifically, our system 1) visualizes the relative importance of
each part of an ECG segment for the final model decision; 2) upholds specified
statistical constraints on its out-of-sample performance and provides uncertainty
estimation for its predictions; 3) handles inputs containing unknown rhythm types;
and 4) handles data from unseen patients while also flagging cases in which the
model’s outputs are not usable for a specific patient. This work represents a
significant step toward overcoming the limitations currently impeding the integration
of AI into clinical practice in cardiology and medicine in general.'
article_number: e2020620118
article_processing_charge: No
article_type: original
author:
- first_name: Yonatan
full_name: Elul, Yonatan
last_name: Elul
- first_name: Aviv A.
full_name: Rosenberg, Aviv A.
last_name: Rosenberg
- first_name: Assaf
full_name: Schuster, Assaf
last_name: Schuster
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Yael
full_name: Yaniv, Yael
last_name: Yaniv
citation:
ama: Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. Meeting the unmet
needs of clinicians from AI systems showcased for cardiology with deep-learning–based
ECG analysis. Proceedings of the National Academy of Sciences. 2021;118(24).
doi:10.1073/pnas.2020620118
apa: Elul, Y., Rosenberg, A. A., Schuster, A., Bronstein, A. M., & Yaniv, Y.
(2021). Meeting the unmet needs of clinicians from AI systems showcased for cardiology
with deep-learning–based ECG analysis. Proceedings of the National Academy
of Sciences. National Academy of Sciences. https://doi.org/10.1073/pnas.2020620118
chicago: Elul, Yonatan, Aviv A. Rosenberg, Assaf Schuster, Alex M. Bronstein, and
Yael Yaniv. “Meeting the Unmet Needs of Clinicians from AI Systems Showcased for
Cardiology with Deep-Learning–Based ECG Analysis.” Proceedings of the National
Academy of Sciences. National Academy of Sciences, 2021. https://doi.org/10.1073/pnas.2020620118.
ieee: Y. Elul, A. A. Rosenberg, A. Schuster, A. M. Bronstein, and Y. Yaniv, “Meeting
the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based
ECG analysis,” Proceedings of the National Academy of Sciences, vol. 118,
no. 24. National Academy of Sciences, 2021.
ista: Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. 2021. Meeting the
unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based
ECG analysis. Proceedings of the National Academy of Sciences. 118(24), e2020620118.
mla: Elul, Yonatan, et al. “Meeting the Unmet Needs of Clinicians from AI Systems
Showcased for Cardiology with Deep-Learning–Based ECG Analysis.” Proceedings
of the National Academy of Sciences, vol. 118, no. 24, e2020620118, National
Academy of Sciences, 2021, doi:10.1073/pnas.2020620118.
short: Y. Elul, A.A. Rosenberg, A. Schuster, A.M. Bronstein, Y. Yaniv, Proceedings
of the National Academy of Sciences 118 (2021).
date_created: 2024-10-08T12:58:09Z
date_published: 2021-06-07T00:00:00Z
date_updated: 2024-10-15T07:43:01Z
day: '07'
doi: 10.1073/pnas.2020620118
extern: '1'
external_id:
pmid:
- '34099565'
intvolume: ' 118'
issue: '24'
language:
- iso: eng
month: '06'
oa_version: Published Version
pmid: 1
publication: Proceedings of the National Academy of Sciences
publication_identifier:
eissn:
- 1091-6490
issn:
- 0027-8424
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: Meeting the unmet needs of clinicians from AI systems showcased for cardiology
with deep-learning–based ECG analysis
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 118
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '18237'
abstract:
- lang: eng
text: We present a novel method for neural network quantization. Our method, named
UNIQ, emulates a non-uniform k-quantile quantizer and adapts the model to perform
well with quantized weights by injecting noise to the weights at training time.
As a by-product of injecting noise to weights, we find that activations can also
be quantized to as low as 8-bit with only a minor accuracy degradation. Our non-uniform
quantization approach provides a novel alternative to the existing uniform quantization
techniques for neural networks. We further propose a novel complexity metric of
number of bit operations performed (BOPs), and we show that this metric has a
linear relation with logic utilization and power. We suggest evaluating the trade-off
of accuracy vs. complexity (BOPs). The proposed method, when evaluated on ResNet18/34/50
and MobileNet on ImageNet, outperforms the prior state of the art both in the
low-complexity regime and the high accuracy regime. We demonstrate the practical
applicability of this approach, by implementing our non-uniformly quantized CNN
on FPGA.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Natan
full_name: Liss, Natan
last_name: Liss
- first_name: Eli
full_name: Schwartz, Eli
last_name: Schwartz
- first_name: Evgenii
full_name: Zheltonozhskii, Evgenii
last_name: Zheltonozhskii
- 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
- first_name: Avi
full_name: Mendelson, Avi
last_name: Mendelson
citation:
ama: 'Baskin C, Liss N, Schwartz E, et al. UNIQ: Uniform Noise Injection for Non-Uniform
Quantization of neural networks. ACM Transactions on Computer Systems.
2021;37(1-4):1-15. doi:10.1145/3444943'
apa: 'Baskin, C., Liss, N., Schwartz, E., Zheltonozhskii, E., Giryes, R., Bronstein,
A. M., & Mendelson, A. (2021). UNIQ: Uniform Noise Injection for Non-Uniform
Quantization of neural networks. ACM Transactions on Computer Systems.
Association for Computing Machinery. https://doi.org/10.1145/3444943'
chicago: 'Baskin, Chaim, Natan Liss, Eli Schwartz, Evgenii Zheltonozhskii, Raja
Giryes, Alex M. Bronstein, and Avi Mendelson. “UNIQ: Uniform Noise Injection for
Non-Uniform Quantization of Neural Networks.” ACM Transactions on Computer
Systems. Association for Computing Machinery, 2021. https://doi.org/10.1145/3444943.'
ieee: 'C. Baskin et al., “UNIQ: Uniform Noise Injection for Non-Uniform Quantization
of neural networks,” ACM Transactions on Computer Systems, vol. 37, no.
1–4. Association for Computing Machinery, pp. 1–15, 2021.'
ista: 'Baskin C, Liss N, Schwartz E, Zheltonozhskii E, Giryes R, Bronstein AM, Mendelson
A. 2021. UNIQ: Uniform Noise Injection for Non-Uniform Quantization of neural
networks. ACM Transactions on Computer Systems. 37(1–4), 1–15.'
mla: 'Baskin, Chaim, et al. “UNIQ: Uniform Noise Injection for Non-Uniform Quantization
of Neural Networks.” ACM Transactions on Computer Systems, vol. 37, no.
1–4, Association for Computing Machinery, 2021, pp. 1–15, doi:10.1145/3444943.'
short: C. Baskin, N. Liss, E. Schwartz, E. Zheltonozhskii, R. Giryes, A.M. Bronstein,
A. Mendelson, ACM Transactions on Computer Systems 37 (2021) 1–15.
date_created: 2024-10-08T12:58:26Z
date_published: 2021-03-26T00:00:00Z
date_updated: 2024-10-15T07:47:22Z
day: '26'
doi: 10.1145/3444943
extern: '1'
external_id:
arxiv:
- '1804.10969'
intvolume: ' 37'
issue: 1-4
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1804.10969
month: '03'
oa: 1
oa_version: Preprint
page: 1-15
publication: ACM Transactions on Computer Systems
publication_identifier:
eissn:
- 1557-7333
issn:
- 0734-2071
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'UNIQ: Uniform Noise Injection for Non-Uniform Quantization of neural networks'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2021'
...
---
OA_place: publisher
OA_type: gold
_id: '18238'
abstract:
- lang: eng
text: The demand for running NNs in embedded environments has increased significantly
in recent years due to the significant success of convolutional neural network
(CNN) approaches in various tasks, including image recognition and generation.
The task of achieving high accuracy on resource-restricted devices, however, is
still considered to be challenging, which is mainly due to the vast number of
design parameters that need to be balanced. While the quantization of CNN parameters
leads to a reduction of power and area, it can also generate unexpected changes
in the balance between communication and computation. This change is hard to evaluate,
and the lack of balance may lead to lower utilization of either memory bandwidth
or computational resources, thereby reducing performance. This paper introduces
a hardware performance analysis framework for identifying bottlenecks in the early
stages of CNN hardware design. We demonstrate how the proposed method can help
in evaluating different architecture alternatives of resource-restricted CNN accelerators
(e.g., part of real-time embedded systems) early in design stages and, thus, prevent
making design mistakes.
article_number: '717'
article_processing_charge: No
article_type: original
author:
- first_name: Alex
full_name: Karbachevsky, Alex
last_name: Karbachevsky
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Evgenii
full_name: Zheltonozhskii, Evgenii
last_name: Zheltonozhskii
- first_name: Yevgeny
full_name: Yermolin, Yevgeny
last_name: Yermolin
- first_name: Freddy
full_name: Gabbay, Freddy
last_name: Gabbay
- 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: Karbachevsky A, Baskin C, Zheltonozhskii E, et al. Early-stage neural network
hardware performance analysis. Sustainability. 2021;13(2). doi:10.3390/su13020717
apa: Karbachevsky, A., Baskin, C., Zheltonozhskii, E., Yermolin, Y., Gabbay, F.,
Bronstein, A. M., & Mendelson, A. (2021). Early-stage neural network hardware
performance analysis. Sustainability. MDPI. https://doi.org/10.3390/su13020717
chicago: Karbachevsky, Alex, Chaim Baskin, Evgenii Zheltonozhskii, Yevgeny Yermolin,
Freddy Gabbay, Alex M. Bronstein, and Avi Mendelson. “Early-Stage Neural Network
Hardware Performance Analysis.” Sustainability. MDPI, 2021. https://doi.org/10.3390/su13020717.
ieee: A. Karbachevsky et al., “Early-stage neural network hardware performance
analysis,” Sustainability, vol. 13, no. 2. MDPI, 2021.
ista: Karbachevsky A, Baskin C, Zheltonozhskii E, Yermolin Y, Gabbay F, Bronstein
AM, Mendelson A. 2021. Early-stage neural network hardware performance analysis.
Sustainability. 13(2), 717.
mla: Karbachevsky, Alex, et al. “Early-Stage Neural Network Hardware Performance
Analysis.” Sustainability, vol. 13, no. 2, 717, MDPI, 2021, doi:10.3390/su13020717.
short: A. Karbachevsky, C. Baskin, E. Zheltonozhskii, Y. Yermolin, F. Gabbay, A.M.
Bronstein, A. Mendelson, Sustainability 13 (2021).
date_created: 2024-10-08T12:58:47Z
date_published: 2021-01-13T00:00:00Z
date_updated: 2024-10-15T08:17:49Z
day: '13'
doi: 10.3390/su13020717
extern: '1'
intvolume: ' 13'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.3390/su13020717
month: '01'
oa: 1
oa_version: Published Version
publication: Sustainability
publication_identifier:
issn:
- 2071-1050
publication_status: published
publisher: MDPI
quality_controlled: '1'
scopus_import: '1'
status: public
title: Early-stage neural network hardware performance analysis
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '18239'
abstract:
- lang: eng
text: Nowadays, there is an abundance of data involving images and surrounding free-form
text weakly corresponding to those images. Weakly Supervised phrase-Grounding
(WSG) deals with the task of using this data to learn to localize (or to ground)
arbitrary text phrases in images without any additional annotations. However,
most recent SotA methods for WSG assume an existence of a pre-trained object detector,
relying on it to produce the ROIs for localization. In this work, we focus on
the task of Detector-Free WSG (DF-WSG) to solve WSG without relying on a pre-trained
detector. The key idea behind our proposed Grounding by Separation (GbS) method
is synthesizing ‘text to image-regions’ associations by random alpha-blending
of arbitrary image pairs and using the corresponding texts of the pair as conditions
to recover the alpha map from the blended image via a segmentation network. At
test time, this allows using the query phrase as a condition for a non-blended
query image, thus interpreting the test image as a composition of a region corresponding
to the phrase and the complement region. Our GbS shows an 8.5% accuracy improvement
over previous DF-WSG SotA, for a range of benchmarks including Flickr30K, Visual
Genome, and ReferIt, as well as a complementary improvement (above 7%) over the
detector-based approaches for WSG.
article_processing_charge: No
arxiv: 1
author:
- first_name: Assaf
full_name: Arbelle, Assaf
last_name: Arbelle
- first_name: Sivan
full_name: Doveh, Sivan
last_name: Doveh
- first_name: Amit
full_name: Alfassy, Amit
last_name: Alfassy
- first_name: Joseph
full_name: Shtok, Joseph
last_name: Shtok
- first_name: Guy
full_name: Lev, Guy
last_name: Lev
- first_name: Eli
full_name: Schwartz, Eli
last_name: Schwartz
- first_name: Hilde
full_name: Kuehne, Hilde
last_name: Kuehne
- first_name: Hila Barak
full_name: Levi, Hila Barak
last_name: Levi
- first_name: Prasanna
full_name: Sattigeri, Prasanna
last_name: Sattigeri
- first_name: Rameswar
full_name: Panda, Rameswar
last_name: Panda
- first_name: Chun-Fu
full_name: Chen, Chun-Fu
last_name: Chen
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Kate
full_name: Saenko, Kate
last_name: Saenko
- first_name: Shimon
full_name: Ullman, Shimon
last_name: Ullman
- first_name: Raja
full_name: Giryes, Raja
last_name: Giryes
- first_name: Rogerio
full_name: Feris, Rogerio
last_name: Feris
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
citation:
ama: 'Arbelle A, Doveh S, Alfassy A, et al. Detector-free weakly supervised grounding
by separation. In: IEEE/CVF International Conference on Computer Vision.
Vol 15. Institute of Electrical and Electronics Engineers; 2021. doi:10.1109/iccv48922.2021.00182'
apa: 'Arbelle, A., Doveh, S., Alfassy, A., Shtok, J., Lev, G., Schwartz, E., … Karlinsky,
L. (2021). Detector-free weakly supervised grounding by separation. In IEEE/CVF
International Conference on Computer Vision (Vol. 15). Montreal, Canada: Institute
of Electrical and Electronics Engineers. https://doi.org/10.1109/iccv48922.2021.00182'
chicago: Arbelle, Assaf, Sivan Doveh, Amit Alfassy, Joseph Shtok, Guy Lev, Eli Schwartz,
Hilde Kuehne, et al. “Detector-Free Weakly Supervised Grounding by Separation.”
In IEEE/CVF International Conference on Computer Vision, Vol. 15. Institute
of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/iccv48922.2021.00182.
ieee: A. Arbelle et al., “Detector-free weakly supervised grounding by separation,”
in IEEE/CVF International Conference on Computer Vision, Montreal, Canada,
2021, vol. 15.
ista: 'Arbelle A, Doveh S, Alfassy A, Shtok J, Lev G, Schwartz E, Kuehne H, Levi
HB, Sattigeri P, Panda R, Chen C-F, Bronstein AM, Saenko K, Ullman S, Giryes R,
Feris R, Karlinsky L. 2021. Detector-free weakly supervised grounding by separation.
IEEE/CVF International Conference on Computer Vision. ICCV: International Conference
on Computer Vision vol. 15.'
mla: Arbelle, Assaf, et al. “Detector-Free Weakly Supervised Grounding by Separation.”
IEEE/CVF International Conference on Computer Vision, vol. 15, Institute
of Electrical and Electronics Engineers, 2021, doi:10.1109/iccv48922.2021.00182.
short: A. Arbelle, S. Doveh, A. Alfassy, J. Shtok, G. Lev, E. Schwartz, H. Kuehne,
H.B. Levi, P. Sattigeri, R. Panda, C.-F. Chen, A.M. Bronstein, K. Saenko, S. Ullman,
R. Giryes, R. Feris, L. Karlinsky, in:, IEEE/CVF International Conference on Computer
Vision, Institute of Electrical and Electronics Engineers, 2021.
conference:
end_date: 2021-10-17
location: Montreal, Canada
name: 'ICCV: International Conference on Computer Vision'
start_date: 2021-10-10
date_created: 2024-10-08T13:02:34Z
date_published: 2021-10-20T00:00:00Z
date_updated: 2024-10-15T08:22:47Z
day: '20'
doi: 10.1109/iccv48922.2021.00182
extern: '1'
external_id:
arxiv:
- '2104.09829'
intvolume: ' 15'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2104.09829
month: '10'
oa: 1
oa_version: Preprint
publication: IEEE/CVF International Conference on Computer Vision
publication_identifier:
eisbn:
- '9781665428125'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Detector-free weakly supervised grounding by separation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 15
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '18240'
abstract:
- lang: eng
text: Mechanical image stabilization using actuated gimbals enables capturing long-exposure
shots without suffering from blur due to camera motion. These devices, however,
are often physically cumbersome and expensive, limiting their widespread use.
In this work, we propose to digitally emulate a mechanically stabilized system
from the input of a fast unstabilized camera. To exploit the trade-off between
motion blur at long exposures and low SNR at short exposures, we train a CNN that
estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure
frames, related by unknown motion. We further suggest learning the burst’s exposure
times in an end-to-end manner, thus balancing the noise and blur across the frames.
We demonstrate this method’s advantage over the traditional approach of deblurring
a single image or denoising a fixed-exposure burst on both synthetic and real
data.
article_processing_charge: No
arxiv: 1
author:
- first_name: Omer
full_name: Dahary, Omer
last_name: Dahary
- first_name: Matan
full_name: Jacoby, Matan
last_name: Jacoby
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Dahary O, Jacoby M, Bronstein AM. Digital gimbal: End-to-end deep image stabilization
with learnable exposure times. In: IEEE/CVF Conference on Computer Vision and
Pattern Recognition. Vol 38. Institute of Electrical and Electronics Engineers;
2021. doi:10.1109/cvpr46437.2021.01176'
apa: 'Dahary, O., Jacoby, M., & Bronstein, A. M. (2021). Digital gimbal: End-to-end
deep image stabilization with learnable exposure times. In IEEE/CVF Conference
on Computer Vision and Pattern Recognition (Vol. 38). Nashville, TN, United
States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr46437.2021.01176'
chicago: 'Dahary, Omer, Matan Jacoby, and Alex M. Bronstein. “Digital Gimbal: End-to-End
Deep Image Stabilization with Learnable Exposure Times.” In IEEE/CVF Conference
on Computer Vision and Pattern Recognition, Vol. 38. Institute of Electrical
and Electronics Engineers, 2021. https://doi.org/10.1109/cvpr46437.2021.01176.'
ieee: 'O. Dahary, M. Jacoby, and A. M. Bronstein, “Digital gimbal: End-to-end deep
image stabilization with learnable exposure times,” in IEEE/CVF Conference
on Computer Vision and Pattern Recognition, Nashville, TN, United States,
2021, vol. 38.'
ista: 'Dahary O, Jacoby M, Bronstein AM. 2021. Digital gimbal: End-to-end deep image
stabilization with learnable exposure times. IEEE/CVF Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition
vol. 38.'
mla: 'Dahary, Omer, et al. “Digital Gimbal: End-to-End Deep Image Stabilization
with Learnable Exposure Times.” IEEE/CVF Conference on Computer Vision and
Pattern Recognition, vol. 38, Institute of Electrical and Electronics Engineers,
2021, doi:10.1109/cvpr46437.2021.01176.'
short: O. Dahary, M. Jacoby, A.M. Bronstein, in:, IEEE/CVF Conference on Computer
Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers,
2021.
conference:
end_date: 2021-06-25
location: Nashville, TN, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2021-06-20
date_created: 2024-10-08T13:02:53Z
date_published: 2021-06-30T00:00:00Z
date_updated: 2024-10-15T08:42:37Z
day: '30'
doi: 10.1109/cvpr46437.2021.01176
extern: '1'
external_id:
arxiv:
- '2012.04515'
intvolume: ' 38'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2012.04515
month: '06'
oa: 1
oa_version: Preprint
publication: IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eisbn:
- '9781665445092'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Digital gimbal: End-to-end deep image stabilization with learnable exposure
times'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '18241'
abstract:
- lang: eng
text: Multiple-input multiple-output (MIMO) radar is one of the leading depth sensing
modalities. However, the usage of multiple receive channels lead to relative high
costs and prevent the penetration of MIMOs in many areas such as the automotive
industry. Over the last years, few studies concentrated on designing reduced measurement
schemes and image reconstruction schemes for MIMO radars, however these problems
have been so far addressed separately. On the other hand, recent works in optical
computational imaging have demonstrated growing success of simultaneous learning-based
design of the acquisition and reconstruction schemes, manifesting significant
improvement in the reconstruction quality. Inspired by these successes, in this
work, we propose to learn MIMO acquisition parameters in the form of receive (Rx)
antenna elements locations jointly with an image neural-network based reconstruction.
To this end, we propose an algorithm for training the combined acquisition-reconstruction
pipeline end-to-end in a differentiable way. We demonstrate the significance of
using our learned acquisition parameters with and without the neural-network reconstruction.
Code and datasets will be released upon publication.
article_processing_charge: No
arxiv: 1
author:
- first_name: Tomer
full_name: Weiss, Tomer
last_name: Weiss
- first_name: Nissim
full_name: Peretz, Nissim
last_name: Peretz
- first_name: Sanketh
full_name: Vedula, Sanketh
last_name: Vedula
- first_name: Arie
full_name: Feuer, Arie
last_name: Feuer
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Weiss T, Peretz N, Vedula S, Feuer A, Bronstein AM. Joint optimization of
system design and reconstruction in MIMO radar imaging. In: 31st International
Workshop on Machine Learning for Signal Processing. Vol 4. Institute of Electrical
and Electronics Engineers; 2021. doi:10.1109/mlsp52302.2021.9596168'
apa: 'Weiss, T., Peretz, N., Vedula, S., Feuer, A., & Bronstein, A. M. (2021).
Joint optimization of system design and reconstruction in MIMO radar imaging.
In 31st International Workshop on Machine Learning for Signal Processing
(Vol. 4). Gold Coast, Australia: Institute of Electrical and Electronics Engineers.
https://doi.org/10.1109/mlsp52302.2021.9596168'
chicago: Weiss, Tomer, Nissim Peretz, Sanketh Vedula, Arie Feuer, and Alex M. Bronstein.
“Joint Optimization of System Design and Reconstruction in MIMO Radar Imaging.”
In 31st International Workshop on Machine Learning for Signal Processing,
Vol. 4. Institute of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/mlsp52302.2021.9596168.
ieee: T. Weiss, N. Peretz, S. Vedula, A. Feuer, and A. M. Bronstein, “Joint optimization
of system design and reconstruction in MIMO radar imaging,” in 31st International
Workshop on Machine Learning for Signal Processing, Gold Coast, Australia,
2021, vol. 4.
ista: 'Weiss T, Peretz N, Vedula S, Feuer A, Bronstein AM. 2021. Joint optimization
of system design and reconstruction in MIMO radar imaging. 31st International
Workshop on Machine Learning for Signal Processing. MLSP: Machine Learning for
Signal Processing vol. 4.'
mla: Weiss, Tomer, et al. “Joint Optimization of System Design and Reconstruction
in MIMO Radar Imaging.” 31st International Workshop on Machine Learning for
Signal Processing, vol. 4, Institute of Electrical and Electronics Engineers,
2021, doi:10.1109/mlsp52302.2021.9596168.
short: T. Weiss, N. Peretz, S. Vedula, A. Feuer, A.M. Bronstein, in:, 31st International
Workshop on Machine Learning for Signal Processing, Institute of Electrical and
Electronics Engineers, 2021.
conference:
end_date: 2021-10-28
location: Gold Coast, Australia
name: 'MLSP: Machine Learning for Signal Processing'
start_date: 2021-10-25
date_created: 2024-10-08T13:03:09Z
date_published: 2021-10-01T00:00:00Z
date_updated: 2024-10-16T09:41:11Z
day: '01'
doi: 10.1109/mlsp52302.2021.9596168
extern: '1'
external_id:
arxiv:
- '2110.03218'
intvolume: ' 4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2110.03218
month: '10'
oa: 1
oa_version: Preprint
publication: 31st International Workshop on Machine Learning for Signal Processing
publication_identifier:
eisbn:
- '9781728163383'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Joint optimization of system design and reconstruction in MIMO radar imaging
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '18242'
abstract:
- lang: eng
text: Fiber tractography is an important tool of computational neuroscience that
enables reconstructing the spatial connectivity and organization of white matter
of the brain. Fiber tractography takes advantage of diffusion Magnetic Resonance
Imaging (dMRI) which allows measuring the apparent diffusivity of cerebral water
along different spatial directions. Unfortunately, collecting such data comes
at the price of reduced spatial resolution and substantially elevated acquisition
times, which limits the clinical applicability of dMRI. This problem has been
thus far addressed using two principal strategies. Most of the efforts have been
extended towards improving the quality of signal estimation for any, yet fixed
sampling scheme (defined through the choice of diffusion-encoding gradients).
On the other hand, optimization over the sampling scheme has also proven to be
effective. Inspired by the previous results, the present work consolidates the
above strategies into a unified estimation framework, in which the optimization
is carried out with respect to both estimation model and sampling design concurrently.
The proposed solution offers substantial improvements in the quality of signal
estimation as well as the accuracy of ensuing analysis by means of fiber tractography.
While proving the optimality of the learned estimation models would probably need
more extensive evaluation, we nevertheless claim that the learned sampling schemes
can be of immediate use, offering a way to improve the dMRI analysis without the
necessity of deploying the neural network used for their estimation. We present
a comprehensive comparative analysis based on the Human Connectome Project data.
Code and learned sampling designs available at https://github.com/tomer196/Learned_dMRI.
alternative_title:
- Mathematics and Visualization
article_processing_charge: No
arxiv: 1
author:
- first_name: Tomer
full_name: Weiss, Tomer
last_name: Weiss
- first_name: Sanketh
full_name: Vedula, Sanketh
last_name: Vedula
- first_name: Ortal
full_name: Senouf, Ortal
last_name: Senouf
- first_name: Oleg
full_name: Michailovich, Oleg
last_name: Michailovich
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Weiss T, Vedula S, Senouf O, Michailovich O, Bronstein AM. Towards learned
optimal q-space sampling in diffusion MRI. In: Gyori N, Hutter J, Nath V, Palombo
M, Pizzolato M, Zhang F, eds. Computational Diffusion MRI. Cham: Springer
Nature; 2021:13-28. doi:10.1007/978-3-030-73018-5_2'
apa: 'Weiss, T., Vedula, S., Senouf, O., Michailovich, O., & Bronstein, A. M.
(2021). Towards learned optimal q-space sampling in diffusion MRI. In N. Gyori,
J. Hutter, V. Nath, M. Palombo, M. Pizzolato, & F. Zhang (Eds.), Computational
Diffusion MRI (pp. 13–28). Cham: Springer Nature. https://doi.org/10.1007/978-3-030-73018-5_2'
chicago: 'Weiss, Tomer, Sanketh Vedula, Ortal Senouf, Oleg Michailovich, and Alex
M. Bronstein. “Towards Learned Optimal Q-Space Sampling in Diffusion MRI.” In
Computational Diffusion MRI, edited by Noemi Gyori, Jana Hutter, Vishwesh
Nath, Marco Palombo, Marco Pizzolato, and Fan Zhang, 13–28. Cham: Springer Nature,
2021. https://doi.org/10.1007/978-3-030-73018-5_2.'
ieee: 'T. Weiss, S. Vedula, O. Senouf, O. Michailovich, and A. M. Bronstein, “Towards
learned optimal q-space sampling in diffusion MRI,” in Computational Diffusion
MRI, N. Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, and F. Zhang,
Eds. Cham: Springer Nature, 2021, pp. 13–28.'
ista: 'Weiss T, Vedula S, Senouf O, Michailovich O, Bronstein AM. 2021.Towards learned
optimal q-space sampling in diffusion MRI. In: Computational Diffusion MRI. Mathematics
and Visualization, , 13–28.'
mla: Weiss, Tomer, et al. “Towards Learned Optimal Q-Space Sampling in Diffusion
MRI.” Computational Diffusion MRI, edited by Noemi Gyori et al., Springer
Nature, 2021, pp. 13–28, doi:10.1007/978-3-030-73018-5_2.
short: T. Weiss, S. Vedula, O. Senouf, O. Michailovich, A.M. Bronstein, in:, N.
Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, F. Zhang (Eds.), Computational
Diffusion MRI, Springer Nature, Cham, 2021, pp. 13–28.
conference:
end_date: 2020-10-08
location: Lima, Peru/Virtual
name: 'MICCAI: Conference on Medical Image Computing and Computer-Assisted Intervention'
start_date: 2020-10-08
date_created: 2024-10-08T13:03:26Z
date_published: 2021-09-30T00:00:00Z
date_updated: 2024-10-16T09:51:45Z
day: '30'
doi: 10.1007/978-3-030-73018-5_2
editor:
- first_name: Noemi
full_name: Gyori, Noemi
last_name: Gyori
- first_name: Jana
full_name: Hutter, Jana
last_name: Hutter
- first_name: Vishwesh
full_name: Nath, Vishwesh
last_name: Nath
- first_name: Marco
full_name: Palombo, Marco
last_name: Palombo
- first_name: Marco
full_name: Pizzolato, Marco
last_name: Pizzolato
- first_name: Fan
full_name: Zhang, Fan
last_name: Zhang
extern: '1'
external_id:
arxiv:
- '2009.03008'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2009.03008
month: '09'
oa: 1
oa_version: Preprint
page: 13-28
place: Cham
publication: Computational Diffusion MRI
publication_identifier:
eisbn:
- '9783030730185'
isbn:
- '9783030730178'
issn:
- 1612-3786
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/tomer196/Learned_dMRI
scopus_import: '1'
status: public
title: Towards learned optimal q-space sampling in diffusion MRI
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '18243'
abstract:
- lang: eng
text: "What is the best way to match the nodes of two graphs? This graph alignment
problem generalizes graph isomorphism and arises in applications from social network
analysis to bioinformatics. Existing solutions either require auxiliary information
such as node attributes, or provide a single-scale view of the graph by translating
the problem into aligning node embeddings.\r\n\r\nIn this paper, we transfer the
shape-analysis concept of functional maps from the continuous to the discrete
case, and treat the graph alignment problem as a special case of the problem of
finding a mapping between functions on graphs. We present GRASP, a method that
captures multiscale structural characteristics from the eigenvectors of the graph’s
Laplacian and uses this information to align two graphs.Our experimental study,
featuring noise levels higher than anything used in previous studies, shows that
GRASP outperforms state-of-the-art methods for graph alignment across noise levels
and graph types."
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Judith
full_name: Hermanns, Judith
last_name: Hermanns
- first_name: Anton
full_name: Tsitsulin, Anton
last_name: Tsitsulin
- first_name: Marina
full_name: Munkhoeva, Marina
last_name: Munkhoeva
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Davide
full_name: Mottin, Davide
last_name: Mottin
- first_name: Panagiotis
full_name: Karras, Panagiotis
last_name: Karras
citation:
ama: 'Hermanns J, Tsitsulin A, Munkhoeva M, Bronstein AM, Mottin D, Karras P. GRASP:
Graph alignment through spectral signatures. In: International Joint Conference
on Asia-Pacific Web and Web-Age Information Management. Vol 12858. Springer
Nature; 2021:44-52. doi:10.1007/978-3-030-85896-4_4'
apa: 'Hermanns, J., Tsitsulin, A., Munkhoeva, M., Bronstein, A. M., Mottin, D.,
& Karras, P. (2021). GRASP: Graph alignment through spectral signatures. In
International Joint Conference on Asia-Pacific Web and Web-Age Information Management
(Vol. 12858, pp. 44–52). Guangzhou, China: Springer Nature. https://doi.org/10.1007/978-3-030-85896-4_4'
chicago: 'Hermanns, Judith, Anton Tsitsulin, Marina Munkhoeva, Alex M. Bronstein,
Davide Mottin, and Panagiotis Karras. “GRASP: Graph Alignment through Spectral
Signatures.” In International Joint Conference on Asia-Pacific Web and Web-Age
Information Management, 12858:44–52. Springer Nature, 2021. https://doi.org/10.1007/978-3-030-85896-4_4.'
ieee: 'J. Hermanns, A. Tsitsulin, M. Munkhoeva, A. M. Bronstein, D. Mottin, and
P. Karras, “GRASP: Graph alignment through spectral signatures,” in International
Joint Conference on Asia-Pacific Web and Web-Age Information Management, Guangzhou,
China, 2021, vol. 12858, no. Part I, pp. 44–52.'
ista: 'Hermanns J, Tsitsulin A, Munkhoeva M, Bronstein AM, Mottin D, Karras P. 2021.
GRASP: Graph alignment through spectral signatures. International Joint Conference
on Asia-Pacific Web and Web-Age Information Management. APWeb-WAIM: International
Joint Conference on Asia-Pacific Web and Web-Age Information Management, LNCS,
vol. 12858, 44–52.'
mla: 'Hermanns, Judith, et al. “GRASP: Graph Alignment through Spectral Signatures.”
International Joint Conference on Asia-Pacific Web and Web-Age Information Management,
vol. 12858, no. Part I, Springer Nature, 2021, pp. 44–52, doi:10.1007/978-3-030-85896-4_4.'
short: J. Hermanns, A. Tsitsulin, M. Munkhoeva, A.M. Bronstein, D. Mottin, P. Karras,
in:, International Joint Conference on Asia-Pacific Web and Web-Age Information
Management, Springer Nature, 2021, pp. 44–52.
conference:
end_date: 2021-08-25
location: Guangzhou, China
name: 'APWeb-WAIM: International Joint Conference on Asia-Pacific Web and Web-Age
Information Management'
start_date: 2021-08-23
date_created: 2024-10-08T13:03:44Z
date_published: 2021-08-19T00:00:00Z
date_updated: 2025-01-29T09:57:31Z
day: '19'
doi: 10.1007/978-3-030-85896-4_4
extern: '1'
intvolume: ' 12858'
issue: Part I
language:
- iso: eng
month: '08'
oa_version: None
page: 44 - 52
publication: International Joint Conference on Asia-Pacific Web and Web-Age Information
Management
publication_identifier:
isbn:
- '9783030858957'
- '9783030858964'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'GRASP: Graph alignment through spectral signatures'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 12858
year: '2021'
...
---
_id: '18244'
abstract:
- lang: eng
text: Some face recognition methods are designed to utilize geometric information
extracted from depth sensors to overcome the weaknesses of single-image based
recognition technologies. However, the accurate acquisition of the depth profile
is an expensive and challenging process. Here, we introduce a novel method that
learns to recognize faces from stereo camera systems without the need to explicitly
compute the facial surface or depth map. The raw face stereo images along with
the location in the image from which the face is extracted allow the proposed
CNN to improve the recognition task while avoiding the need to explicitly handle
the geometric structure of the face. This way, we keep the simplicity and cost
efficiency of identity authentication from a single image, while enjoying the
benefits of geometric data without explicitly reconstructing it. We demonstrate
that the suggested method outperforms both existing single-image and explicit
depth based methods on largescale benchmarks, and even capable of recognize spoofing
attacks. We also provide an ablation study that shows that the suggested method
uses the face locations in the left and right images to encode informative features
that improve the overall performance.
article_number: '9320359'
article_processing_charge: No
arxiv: 1
author:
- first_name: Amir
full_name: Livne, Amir
last_name: Livne
- first_name: Ziv
full_name: Aviv, Ziv
last_name: Aviv
- first_name: Shahaf
full_name: Grofit, Shahaf
last_name: Grofit
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: 'Livne A, Aviv Z, Grofit S, Bronstein AM, Kimmel R. Do we need depth in state-uf-the-art
face authentication? In: 2020 International Conference on 3D Vision (3DV).
IEEE; 2021. doi:10.1109/3dv50981.2020.00099'
apa: 'Livne, A., Aviv, Z., Grofit, S., Bronstein, A. M., & Kimmel, R. (2021).
Do we need depth in state-uf-the-art face authentication? In 2020 International
Conference on 3D Vision (3DV). Fukuoka, Japan: IEEE. https://doi.org/10.1109/3dv50981.2020.00099'
chicago: Livne, Amir, Ziv Aviv, Shahaf Grofit, Alex M. Bronstein, and Ron Kimmel.
“Do We Need Depth in State-Uf-the-Art Face Authentication?” In 2020 International
Conference on 3D Vision (3DV). IEEE, 2021. https://doi.org/10.1109/3dv50981.2020.00099.
ieee: A. Livne, Z. Aviv, S. Grofit, A. M. Bronstein, and R. Kimmel, “Do we need
depth in state-uf-the-art face authentication?,” in 2020 International Conference
on 3D Vision (3DV), Fukuoka, Japan, 2021.
ista: Livne A, Aviv Z, Grofit S, Bronstein AM, Kimmel R. 2021. Do we need depth
in state-uf-the-art face authentication? 2020 International Conference on 3D Vision
(3DV). 8th International Conference on 3D Vision, 9320359.
mla: Livne, Amir, et al. “Do We Need Depth in State-Uf-the-Art Face Authentication?”
2020 International Conference on 3D Vision (3DV), 9320359, IEEE, 2021,
doi:10.1109/3dv50981.2020.00099.
short: A. Livne, Z. Aviv, S. Grofit, A.M. Bronstein, R. Kimmel, in:, 2020 International
Conference on 3D Vision (3DV), IEEE, 2021.
conference:
end_date: 2020-11-28
location: Fukuoka, Japan
name: 8th International Conference on 3D Vision
start_date: 2020-11-25
date_created: 2024-10-08T13:04:02Z
date_published: 2021-01-19T00:00:00Z
date_updated: 2024-12-12T10:10:29Z
day: '19'
doi: 10.1109/3dv50981.2020.00099
extern: '1'
external_id:
arxiv:
- '2003.10895'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2003.10895
month: '01'
oa: 1
oa_version: Preprint
publication: 2020 International Conference on 3D Vision (3DV)
publication_identifier:
eissn:
- 2475-7888
isbn:
- '9781728181295'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Do we need depth in state-uf-the-art face authentication?
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '22063'
abstract:
- lang: eng
text: 'We consider two discrete completely integrable evolutions: the Toda Lattice
and the Ablowitz–Ladik system. The principal thrust of the paper is the development
of microscopic conservation laws that witness the conservation of the perturbation
determinant under these dynamics. In this way, we obtain discrete analogues of
objects that we found essential in our recent analyses of KdV, NLS, and mKdV.
In concert with this, we revisit the classical topic of microscopic conservation
laws attendant to the (renormalized) trace of the Green’s function.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Benjamin
full_name: Harrop-Griffiths, Benjamin
last_name: Harrop-Griffiths
- 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: Harrop-Griffiths B, Killip R, Vişan M. Microscopic conservation laws for integrable
lattice models. Monatshefte für Mathematik. 2021;196(3):477-504. doi:10.1007/s00605-021-01529-5
apa: Harrop-Griffiths, B., Killip, R., & Vişan, M. (2021). Microscopic conservation
laws for integrable lattice models. Monatshefte Für Mathematik. Springer
Nature. https://doi.org/10.1007/s00605-021-01529-5
chicago: Harrop-Griffiths, Benjamin, Rowan Killip, and Monica Vişan. “Microscopic
Conservation Laws for Integrable Lattice Models.” Monatshefte Für Mathematik.
Springer Nature, 2021. https://doi.org/10.1007/s00605-021-01529-5.
ieee: B. Harrop-Griffiths, R. Killip, and M. Vişan, “Microscopic conservation laws
for integrable lattice models,” Monatshefte für Mathematik, vol. 196, no.
3. Springer Nature, pp. 477–504, 2021.
ista: Harrop-Griffiths B, Killip R, Vişan M. 2021. Microscopic conservation laws
for integrable lattice models. Monatshefte für Mathematik. 196(3), 477–504.
mla: Harrop-Griffiths, Benjamin, et al. “Microscopic Conservation Laws for Integrable
Lattice Models.” Monatshefte Für Mathematik, vol. 196, no. 3, Springer
Nature, 2021, pp. 477–504, doi:10.1007/s00605-021-01529-5.
short: B. Harrop-Griffiths, R. Killip, M. Vişan, Monatshefte Für Mathematik 196
(2021) 477–504.
das_tickbox: '1'
date_created: 2026-06-19T08:12:18Z
date_published: 2021-11-01T00:00:00Z
date_updated: 2026-06-30T07:03:12Z
day: '01'
doi: 10.1007/s00605-021-01529-5
extern: '1'
external_id:
arxiv:
- '2012.04782'
intvolume: ' 196'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2012.04782
month: '11'
oa: 1
oa_version: Preprint
page: 477-504
publication: Monatshefte für Mathematik
publication_identifier:
eissn:
- 1436-5081
issn:
- 0026-9255
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Microscopic conservation laws for integrable lattice models
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 196
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '22084'
abstract:
- lang: eng
text: We consider the nonlinear Schrödinger equation in three space dimensions
with combined focusing cubic and defocusing quintic nonlinearity. This problem
was considered previously by Killip et al. [Arch. Ration. Mech. Anal., 225 (2017),
pp. 469--548], who proved scattering for the whole region of the mass/energy plane
where the virial quantity is guaranteed to be positive. In this paper, we prove
scattering in a slightly larger region where, in particular, the virial quantity
is no longer guaranteed to be sign definite.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Rowan
full_name: Killip, Rowan
last_name: Killip
- first_name: Jason
full_name: Murphy, Jason
last_name: Murphy
- first_name: Monica
full_name: Visan, Monica
id: 056daca0-b8d1-11f0-964f-f91054abf8ca
last_name: Visan
citation:
ama: 'Killip R, Murphy J, Vişan M. Scattering for the cubic-quintic NLS: Crossing
the virial threshold. SIAM Journal on Mathematical Analysis. 2021;53(5):5803-5812.
doi:10.1137/20m1381824'
apa: 'Killip, R., Murphy, J., & Vişan, M. (2021). Scattering for the cubic-quintic
NLS: Crossing the virial threshold. SIAM Journal on Mathematical Analysis.
Society for Industrial & Applied Mathematics. https://doi.org/10.1137/20m1381824'
chicago: 'Killip, Rowan, Jason Murphy, and Monica Vişan. “Scattering for the Cubic-Quintic
NLS: Crossing the Virial Threshold.” SIAM Journal on Mathematical Analysis.
Society for Industrial & Applied Mathematics, 2021. https://doi.org/10.1137/20m1381824.'
ieee: 'R. Killip, J. Murphy, and M. Vişan, “Scattering for the cubic-quintic NLS:
Crossing the virial threshold,” SIAM Journal on Mathematical Analysis,
vol. 53, no. 5. Society for Industrial & Applied Mathematics, pp. 5803–5812,
2021.'
ista: 'Killip R, Murphy J, Vişan M. 2021. Scattering for the cubic-quintic NLS:
Crossing the virial threshold. SIAM Journal on Mathematical Analysis. 53(5), 5803–5812.'
mla: 'Killip, Rowan, et al. “Scattering for the Cubic-Quintic NLS: Crossing the
Virial Threshold.” SIAM Journal on Mathematical Analysis, vol. 53, no.
5, Society for Industrial & Applied Mathematics, 2021, pp. 5803–12, doi:10.1137/20m1381824.'
short: R. Killip, J. Murphy, M. Vişan, SIAM Journal on Mathematical Analysis 53
(2021) 5803–5812.
das_tickbox: '1'
date_created: 2026-06-19T08:28:23Z
date_published: 2021-01-01T00:00:00Z
date_updated: 2026-07-01T07:37:46Z
day: '01'
doi: 10.1137/20m1381824
extern: '1'
external_id:
arxiv:
- '2007.07406'
intvolume: ' 53'
issue: '5'
keyword:
- NLS
- scattering
- viral
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2007.07406
mathsc:
- 35Q55
month: '01'
oa: 1
oa_version: Preprint
page: 5803-5812
publication: SIAM Journal on Mathematical Analysis
publication_identifier:
eissn:
- 1095-7154
issn:
- 0036-1410
publication_status: published
publisher: Society for Industrial & Applied Mathematics
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Scattering for the cubic-quintic NLS: Crossing the virial threshold'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 53
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '22087'
abstract:
- lang: eng
text: "We prove that solutions of the cubic nonlinear Schrödinger equation on $\\Bbb{R}^2$
can be approximated by a finite-dimensional Hamiltonian system, uniformly on bounded
sets of initial data. This is despite the wealth of non-compact symmetries: scaling,
translation, and Galilei boosts.\r\n\r\nComplementing this approximation result,
we show that all solutions of the finite-dimensional Hamiltonian system we use
can be approximated by the full PDE.\r\n\r\nA key ingredient in these results
is the development of a general methodology for transfering uniform global space-time
bounds to suitable Fourier truncations of dispersive PDE models.\r\n\r\nAs an
application, we prove symplectic non-squeezing (in the sense of Gromov) for the
cubic NLS on $\\Bbb{R}^2$. This is the first symplectic non-squeezing result for
a Hamiltonian PDE in infinite volume. It is also the first unconditional symplectic
non-squeezing result in a scaling-critical setting.\r\n\r\nFinally, we discuss
implications of non-squeezing on the nature of scattering."
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
- first_name: Xiaoyi
full_name: Zhang, Xiaoyi
last_name: Zhang
citation:
ama: Killip R, Vişan M, Zhang X. Finite-dimensional approximation and non-squeezing
for the cubic nonlinear Schrödinger equation on ℝ2. American Journal of Mathematics.
2021;143(2):613-680. doi:10.1353/ajm.2021.0014
apa: Killip, R., Vişan, M., & Zhang, X. (2021). Finite-dimensional approximation
and non-squeezing for the cubic nonlinear Schrödinger equation on ℝ2. American
Journal of Mathematics. Johns Hopkins University Press. https://doi.org/10.1353/ajm.2021.0014
chicago: Killip, Rowan, Monica Vişan, and Xiaoyi Zhang. “Finite-Dimensional Approximation
and Non-Squeezing for the Cubic Nonlinear Schrödinger Equation on ℝ2.” American
Journal of Mathematics. Johns Hopkins University Press, 2021. https://doi.org/10.1353/ajm.2021.0014.
ieee: R. Killip, M. Vişan, and X. Zhang, “Finite-dimensional approximation and non-squeezing
for the cubic nonlinear Schrödinger equation on ℝ2,” American Journal of Mathematics,
vol. 143, no. 2. Johns Hopkins University Press, pp. 613–680, 2021.
ista: Killip R, Vişan M, Zhang X. 2021. Finite-dimensional approximation and non-squeezing
for the cubic nonlinear Schrödinger equation on ℝ2. American Journal of Mathematics.
143(2), 613–680.
mla: Killip, Rowan, et al. “Finite-Dimensional Approximation and Non-Squeezing for
the Cubic Nonlinear Schrödinger Equation on ℝ2.” American Journal of Mathematics,
vol. 143, no. 2, Johns Hopkins University Press, 2021, pp. 613–80, doi:10.1353/ajm.2021.0014.
short: R. Killip, M. Vişan, X. Zhang, American Journal of Mathematics 143 (2021)
613–680.
das_tickbox: '1'
date_created: 2026-06-19T08:46:12Z
date_published: 2021-04-01T00:00:00Z
date_updated: 2026-07-01T12:38:11Z
day: '01'
doi: 10.1353/ajm.2021.0014
extern: '1'
external_id:
arxiv:
- '1606.07738'
intvolume: ' 143'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1606.07738
month: '04'
oa: 1
oa_version: Preprint
page: 613-680
publication: American Journal of Mathematics
publication_identifier:
eissn:
- 1080-6377
publication_status: published
publisher: Johns Hopkins University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Finite-dimensional approximation and non-squeezing for the cubic nonlinear
Schrödinger equation on ℝ2
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 143
year: '2021'
...