---
OA_place: publisher
OA_type: gold
_id: '21949'
abstract:
- lang: eng
text: "Cardiac T1 mapping provides critical quantitative insights into myocardial
tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation,
and edema.\r\nHowever, the inherently dynamic nature of the heart imposes strict
limits on acquisition\r\ntimes, making high-resolution T1 mapping a persistent
challenge. Compressed sensing (CS)\r\napproaches have reduced scan durations by
undersampling k-space and reconstructing images from partial data, and recent
studies show that jointly optimizing the undersampling\r\npatterns with the reconstruction
network can substantially improve performance. Still,\r\nmost current T1 mapping
pipelines rely on static, hand-crafted masks that do not exploit\r\nthe full acceleration
and accuracy potential. Furthermore, most existing methods do not\r\nlevarage
the physical T1 decay model in optimization. In this work, we introduce T1-\r\nPILOT:
an end-to-end method that explicitly incorporates the T1 signal relaxation model\r\ninto
the sampling–reconstruction framework to guide the learning of non-Cartesian trajectories,
cross-frame alignment, and T1 decay estimation. Through extensive experiments\r\non
the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies
(including learned single-mask and fixed radial or golden-angle sampling schemes),\r\nachieving
higher T1 map fidelity at greater acceleration factors. In particular, we observe
consistent gains in PSNR and VIF relative to existing methods, along with marked\r\nimprovements
in delineating finer myocardial structures. Our results highlight that optimizing
sampling trajectories in tandem with the physical relaxation model leads to both\r\nenhanced
quantitative accuracy and reduced acquisition times. Code for reproducing all\r\nexperiments
and results is available at https://github.com/tamirshor7/T1-PILOT"
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Tamir
full_name: Shor, Tamir
last_name: Shor
- first_name: Moti
full_name: Freiman, Moti
last_name: Freiman
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned
optimized trajectories for T1 mapping acceleration. In: Medical Imaging with
Deep Learning. Vol 315. ML Research Press; :1969-1982.'
apa: 'Shor, T., Freiman, M., Baskin, C., & Bronstein, A. M. (n.d.). T1-PILOT:
Physics-informed learned optimized trajectories for T1 mapping acceleration. In
Medical Imaging with Deep Learning (Vol. 315, pp. 1969–1982). Taipei, Taiwan:
ML Research Press.'
chicago: 'Shor, Tamir, Moti Freiman, Chaim Baskin, and Alex M. Bronstein. “T1-PILOT:
Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration.”
In Medical Imaging with Deep Learning, 315:1969–82. ML Research Press,
n.d.'
ieee: 'T. Shor, M. Freiman, C. Baskin, and A. M. Bronstein, “T1-PILOT: Physics-informed
learned optimized trajectories for T1 mapping acceleration,” in Medical Imaging
with Deep Learning, Taipei, Taiwan, vol. 315, pp. 1969–1982.'
ista: 'Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned
optimized trajectories for T1 mapping acceleration. Medical Imaging with Deep
Learning. MIDL: Medical Imaging with Deep Learning, PMLR, vol. 315, 1969–1982.'
mla: 'Shor, Tamir, et al. “T1-PILOT: Physics-Informed Learned Optimized Trajectories
for T1 Mapping Acceleration.” Medical Imaging with Deep Learning, vol.
315, ML Research Press, pp. 1969–82.'
short: T. Shor, M. Freiman, C. Baskin, A.M. Bronstein, in:, Medical Imaging with
Deep Learning, ML Research Press, n.d., pp. 1969–1982.
conference:
end_date: 2026-07-10
location: Taipei, Taiwan
name: 'MIDL: Medical Imaging with Deep Learning'
start_date: 2026-07-08
corr_author: '1'
date_created: 2026-06-07T22:01:36Z
date_published: 2026-03-17T00:00:00Z
date_updated: 2026-06-08T08:05:24Z
day: '17'
ddc:
- '000'
department:
- _id: AlBr
has_accepted_license: '1'
intvolume: ' 315'
keyword:
- Cardiac T1 Mapping
- Trajectory Optimization and Reconstruction
- PhysicsInformed Deep-Learning
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=nZaPtHbd6N#discussion
month: '03'
oa: 1
oa_version: Published Version
page: 1969-1982
publication: Medical Imaging with Deep Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: accepted
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/tamirshor7/T1-PILOT
scopus_import: '1'
status: public
title: 'T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration'
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: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 315
year: '2026'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '21246'
abstract:
- lang: eng
text: Stellar astrophysics relies on diverse observational modalities—primarily
photometric light curves and spectroscopic data—from which fundamental stellar
properties are inferred. While machine learning (ML) has advanced analysis within
individual modalities, the complementary information encoded across modalities
remains largely underexploited. We present the dual embedding for stellar astronomy
(DESA) model, a novel multimodal foundation model that integrates light curves
and spectra to learn a unified, physically meaningful latent space for stars.
DESA first trains separate modality-specific encoders using a hybrid supervised/self-supervised
scheme, and then aligns them through DualFormer, a transformer-based cross-modal
integration module tailored for astrophysical data. DualFormer combines cross-
and self-attention, a novel dual-projection alignment loss, and a projection-space
eigendecomposition that yields physically structured embeddings. We demonstrate
that DESA significantly outperforms leading unimodal and self-supervised baselines
across a range of tasks. In zero- and few-shot settings, DESA’s learned representations
recover stellar color–magnitude and Hertzsprung–Russell diagrams with high fidelity
(R2 = 0.92 for photometric regressions). In full fine-tuning, DESA achieves state-of-the-art
accuracy for binary star detection (AUC = 0.99, AP = 1.00) and stellar age prediction
(RMSE = 0.94 Gyr). As a compelling case, DESA naturally separates synchronized
binaries from young stars—two populations with nearly identical light curves—purely
from their embedded positions in UMAP space, without requiring external kinematic
or luminosity information. DESA thus offers a powerful new framework for multimodal,
data-driven stellar population analysis, enabling both accurate prediction and
novel discovery.
acknowledgement: This research was partially supported by the Israeli Science Foundation
grant 1834/24.
article_number: '110'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Ilay
full_name: Kamai, Ilay
last_name: Kamai
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Hagai B.
full_name: Perets, Hagai B.
last_name: Perets
citation:
ama: Kamai I, Bronstein AM, Perets HB. Machine Learning inference of stellar properties
using integrated photometric and spectroscopic data. The Astrophysical Journal.
2025;994. doi:10.3847/1538-4357/ae0cbc
apa: Kamai, I., Bronstein, A. M., & Perets, H. B. (2025). Machine Learning inference
of stellar properties using integrated photometric and spectroscopic data. The
Astrophysical Journal. IOP Publishing. https://doi.org/10.3847/1538-4357/ae0cbc
chicago: Kamai, Ilay, Alex M. Bronstein, and Hagai B. Perets. “Machine Learning
Inference of Stellar Properties Using Integrated Photometric and Spectroscopic
Data.” The Astrophysical Journal. IOP Publishing, 2025. https://doi.org/10.3847/1538-4357/ae0cbc.
ieee: I. Kamai, A. M. Bronstein, and H. B. Perets, “Machine Learning inference of
stellar properties using integrated photometric and spectroscopic data,” The
Astrophysical Journal, vol. 994. IOP Publishing, 2025.
ista: Kamai I, Bronstein AM, Perets HB. 2025. Machine Learning inference of stellar
properties using integrated photometric and spectroscopic data. The Astrophysical
Journal. 994, 110.
mla: Kamai, Ilay, et al. “Machine Learning Inference of Stellar Properties Using
Integrated Photometric and Spectroscopic Data.” The Astrophysical Journal,
vol. 994, 110, IOP Publishing, 2025, doi:10.3847/1538-4357/ae0cbc.
short: I. Kamai, A.M. Bronstein, H.B. Perets, The Astrophysical Journal 994 (2025).
date_created: 2026-02-16T15:35:29Z
date_published: 2025-11-19T00:00:00Z
date_updated: 2026-02-17T12:33:19Z
day: '19'
ddc:
- '520'
- '000'
department:
- _id: AlBr
doi: 10.3847/1538-4357/ae0cbc
external_id:
arxiv:
- '2507.10666'
file:
- access_level: open_access
checksum: 255ffd6d664e6c2d1cffbaced650bd10
content_type: application/pdf
creator: dernst
date_created: 2026-02-17T12:32:18Z
date_updated: 2026-02-17T12:32:18Z
file_id: '21302'
file_name: 2025_AstrophysicalJournal_Kamai.pdf
file_size: 16415089
relation: main_file
success: 1
file_date_updated: 2026-02-17T12:32:18Z
has_accepted_license: '1'
intvolume: ' 994'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: The Astrophysical Journal
publication_identifier:
eissn:
- 1538-4357
issn:
- 0004-637X
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
status: public
title: Machine Learning inference of stellar properties using integrated photometric
and spectroscopic data
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 994
year: '2025'
...
---
OA_place: publisher
OA_type: gold
_id: '21327'
abstract:
- lang: eng
text: Proteins exist as a dynamic ensemble of multiple conformations, and these
motions are often crucial for their functions. However, current structure prediction
methods predominantly yield a single conformation, overlooking the conformational
heterogeneity revealed by diverse experimental modalities. Here, we present a
framework for building experiment-grounded protein structure generative models
that infer conformational ensembles consistent with measured experimental data.
The key idea is to treat stateof-the-art protein structure predictors (e.g., AlphaFold3)
as sequence-conditioned structural priors, and cast ensemble modeling as posterior
inference of protein structures given experimental measurements. Through extensive
real-data experiments, we demonstrate the generality of our method to incorporate
a variety of experimental measurements. In particular, our framework uncovers
previously unmodeled conformational heterogeneity from crystallographic densities,
and generates high-accuracy NMR ensembles orders of magnitude faster than the
status quo. Notably, we demonstrate that our ensembles outperform AlphaFold3 (Abramson
et al., 2024) and sometimes better fit experimental data than publicly deposited
structures to the Protein Data Bank (PDB, Burley et al. (2017)). We believe that
this approach will unlock building predictive models that fully embrace experimentally
observed conformational diversity.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: 'This work was supported by the Israeli Science Foundation (ISF)
grant number 1834/24. We acknowledge support from the Austrian Science Fund (FWF,
grant numbers I5812-B and I6223) and the financial support of the Helmsley Fellowships
Program for Sustainability and Health. This research uses resources of the Institute
of Science and Technology Austria’s scientific computing cluster. '
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Sai A
full_name: Maddipatla, Sai A
id: e957f5e5-91c9-11f0-a95f-e090f66ecb4d
last_name: Maddipatla
- first_name: Nadav E
full_name: Sellam, Nadav E
id: ef280fe0-91c9-11f0-a95f-8dea3f5bc513
last_name: Sellam
- first_name: Meital I
full_name: Bojan, Meital I
id: 11d88cf5-91ca-11f0-a95f-edf9f08f47b7
last_name: Bojan
- first_name: Sanketh
full_name: Vedula, Sanketh
id: 94f2fe44-70fa-11f0-b76b-92922c09452b
last_name: Vedula
- first_name: Paul
full_name: Schanda, Paul
id: 7B541462-FAF6-11E9-A490-E8DFE5697425
last_name: Schanda
orcid: 0000-0002-9350-7606
- 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: 'Maddipatla SA, Sellam NE, Bojan MI, et al. Inverse problems with experiment-guided
AlphaFold. In: Proceedings of the 42nd International Conference on Machine
Learning. Vol 267. ML Research Press; 2025:42366-42393.'
apa: 'Maddipatla, S. A., Sellam, N. E., Bojan, M. I., Vedula, S., Schanda, P., Marx,
A., & Bronstein, A. M. (2025). Inverse problems with experiment-guided AlphaFold.
In Proceedings of the 42nd International Conference on Machine Learning
(Vol. 267, pp. 42366–42393). Vancouver, Canada: ML Research Press.'
chicago: Maddipatla, Sai A, Nadav E Sellam, Meital I Bojan, Sanketh Vedula, Paul
Schanda, Ailie Marx, and Alex M. Bronstein. “Inverse Problems with Experiment-Guided
AlphaFold.” In Proceedings of the 42nd International Conference on Machine
Learning, 267:42366–93. ML Research Press, 2025.
ieee: S. A. Maddipatla et al., “Inverse problems with experiment-guided AlphaFold,”
in Proceedings of the 42nd International Conference on Machine Learning,
Vancouver, Canada, 2025, vol. 267, pp. 42366–42393.
ista: 'Maddipatla SA, Sellam NE, Bojan MI, Vedula S, Schanda P, Marx A, Bronstein
AM. 2025. Inverse problems with experiment-guided AlphaFold. Proceedings of the
42nd International Conference on Machine Learning. ICML: International Conference
on Machine Learning, PMLR, vol. 267, 42366–42393.'
mla: Maddipatla, Sai A., et al. “Inverse Problems with Experiment-Guided AlphaFold.”
Proceedings of the 42nd International Conference on Machine Learning, vol.
267, ML Research Press, 2025, pp. 42366–93.
short: S.A. Maddipatla, N.E. Sellam, M.I. Bojan, S. Vedula, P. Schanda, A. Marx,
A.M. Bronstein, in:, Proceedings of the 42nd International Conference on Machine
Learning, ML Research Press, 2025, pp. 42366–42393.
conference:
end_date: 2025-07-19
location: Vancouver, Canada
name: 'ICML: International Conference on Machine Learning'
start_date: 2025-07-13
corr_author: '1'
date_created: 2026-02-18T12:11:17Z
date_published: 2025-07-30T00:00:00Z
date_updated: 2026-02-19T08:56:43Z
day: '30'
ddc:
- '000'
- '540'
department:
- _id: PaSc
- _id: AlBr
- _id: GradSch
external_id:
arxiv:
- '2502.09372'
file:
- access_level: open_access
checksum: f33230a6d59b7978d4cd72795e4e9059
content_type: application/pdf
creator: dernst
date_created: 2026-02-19T08:56:10Z
date_updated: 2026-02-19T08:56:10Z
file_id: '21338'
file_name: 2025_ICML_Maddipatla.pdf
file_size: 1924177
relation: main_file
success: 1
file_date_updated: 2026-02-19T08:56:10Z
has_accepted_license: '1'
intvolume: ' 267'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 42366 - 42393
project:
- _id: eb9c82eb-77a9-11ec-83b8-aadd536561cf
grant_number: I05812
name: AlloSpace. The emergence and mechanisms of allostery
- _id: bdb9578d-d553-11ed-ba76-ed5d39fce6f0
grant_number: I06223
name: Structure and mechanism of the mitochondrial MIM insertase
publication: Proceedings of the 42nd International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Inverse problems with experiment-guided AlphaFold
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: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 267
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
_id: '19595'
abstract:
- lang: eng
text: We investigate the locality of magnetic response in polycyclic aromatic molecules
using a novel deep-learning approach. Our method employs graph neural networks
(GNNs) with a graph-of-rings representation to predict nucleus independent chemical
shifts (NICS) in the space around the molecule. We train a series of models, each
time reducing the size of the largest molecules used in training. The accuracy
of prediction remains high (MAE < 0.5 ppm), even when training the model only
on molecules with up to four rings, thus providing strong evidence for the locality
of magnetic response. To overcome the known problem of generalization of GNNs,
we implement a k-hop expansion strategy and succeed in achieving accurate predictions
for molecules with up to 15 rings (almost 4 times the size of the largest training
example). Our findings have implications for understanding the magnetic response
in complex molecules and demonstrate a promising approach to overcoming GNN scalability
limitations. Furthermore, the trained models enable rapid characterization, without
the need for more expensive DFT calculations.
acknowledgement: The authors express their gratitude to Professor Dr. Peter Chen for
his continued support. The authors acknowledge the Branco Weiss Fellowship for supporting
this research as part of a Society in Science grant and the Israel Science Foundation
for financial support (Grant No. 1745/23 to R.G.-P.). R.G.-P. is a Branco Weiss
Fellow, a Horev Fellow, and an Alon Scholarship recipient. A.M.B. was supported
by the ERC StG EARS and the Israeli Science Foundation.
article_number: '144101'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Yair
full_name: Davidson, Yair
last_name: Davidson
- first_name: Aviad
full_name: Philipp, Aviad
last_name: Philipp
- first_name: Sabyasachi
full_name: Chakraborty, Sabyasachi
last_name: Chakraborty
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Renana
full_name: Gershoni-Poranne, Renana
last_name: Gershoni-Poranne
citation:
ama: Davidson Y, Philipp A, Chakraborty S, Bronstein AM, Gershoni-Poranne R. How
local is “local”? Deep learning reveals locality of the induced magnetic field
of polycyclic aromatic hydrocarbons. Journal of Chemical Physics. 2025;162(14).
doi:10.1063/5.0257558
apa: Davidson, Y., Philipp, A., Chakraborty, S., Bronstein, A. M., & Gershoni-Poranne,
R. (2025). How local is “local”? Deep learning reveals locality of the induced
magnetic field of polycyclic aromatic hydrocarbons. Journal of Chemical Physics.
AIP Publishing. https://doi.org/10.1063/5.0257558
chicago: Davidson, Yair, Aviad Philipp, Sabyasachi Chakraborty, Alex M. Bronstein,
and Renana Gershoni-Poranne. “How Local Is ‘Local’? Deep Learning Reveals Locality
of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons.” Journal
of Chemical Physics. AIP Publishing, 2025. https://doi.org/10.1063/5.0257558.
ieee: Y. Davidson, A. Philipp, S. Chakraborty, A. M. Bronstein, and R. Gershoni-Poranne,
“How local is ‘local’? Deep learning reveals locality of the induced magnetic
field of polycyclic aromatic hydrocarbons,” Journal of Chemical Physics,
vol. 162, no. 14. AIP Publishing, 2025.
ista: Davidson Y, Philipp A, Chakraborty S, Bronstein AM, Gershoni-Poranne R. 2025.
How local is “local”? Deep learning reveals locality of the induced magnetic field
of polycyclic aromatic hydrocarbons. Journal of Chemical Physics. 162(14), 144101.
mla: Davidson, Yair, et al. “How Local Is ‘Local’? Deep Learning Reveals Locality
of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons.” Journal
of Chemical Physics, vol. 162, no. 14, 144101, AIP Publishing, 2025, doi:10.1063/5.0257558.
short: Y. Davidson, A. Philipp, S. Chakraborty, A.M. Bronstein, R. Gershoni-Poranne,
Journal of Chemical Physics 162 (2025).
corr_author: '1'
date_created: 2025-04-20T22:01:28Z
date_published: 2025-04-14T00:00:00Z
date_updated: 2025-09-30T12:06:51Z
day: '14'
ddc:
- '000'
department:
- _id: AlBr
doi: 10.1063/5.0257558
external_id:
isi:
- '001466311300030'
pmid:
- '40197568'
file:
- access_level: open_access
checksum: 20a31a4c506b52de863bab7d3ff989ef
content_type: application/pdf
creator: dernst
date_created: 2025-04-22T09:27:43Z
date_updated: 2025-04-22T09:27:43Z
file_id: '19606'
file_name: 2025_JourChemicalPhysics_Davidson.pdf
file_size: 7812182
relation: main_file
success: 1
file_date_updated: 2025-04-22T09:27:43Z
has_accepted_license: '1'
intvolume: ' 162'
isi: 1
issue: '14'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 92f4a086-16d5-11f0-9cad-c929f5c58b0c
grant_number: '863839'
name: Acoustics-based drone navigation and interaction
publication: Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://gitlab.com/porannegroup/magnetic_locality
scopus_import: '1'
status: public
title: How local is “local”? Deep learning reveals locality of the induced magnetic
field of polycyclic aromatic hydrocarbons
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 162
year: '2025'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '20100'
abstract:
- lang: eng
text: A key step in protein structure prediction involves the detection of co-evolving
pairs of residues, a signal for spatial proximity. This information is gleaned
from multiple sequence alignment and underscores Alphafold’s structure prediction
for almost every known protein. A simple means to create proteins beyond those
found in nature, is by unnaturally fusing together two known proteins or protein
parts. Here we demonstrate that structured peptides are predicted with significantly
reduced accuracy when added to the terminal ends of scaffold proteins. Appending
the multiple sequence alignment for the individual peptide tags to that of the
scaffold protein often restores prediction accuracy. This work suggests that this
windowed multiple sequence alignment approach can be a useful tool for predicting
the structure of fused, chimeric proteins.
acknowledgement: AM acknowledges the financial support of the Helmsley Fellowships
Program for Sustainability and Health. AMB is supported by the Schmidt Chair in
Artificial Intelligence.
article_processing_charge: Yes
article_type: original
author:
- first_name: Sanketh
full_name: Vedula, Sanketh
id: 94f2fe44-70fa-11f0-b76b-92922c09452b
last_name: Vedula
- 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: Vedula S, Bronstein AM, Marx A. Improving prediction accuracy in chimeric proteins
with windowed multiple sequence alignment. Computational and Structural Biotechnology
Journal. 2025;27:3292-3298. doi:10.1016/j.csbj.2025.07.039
apa: Vedula, S., Bronstein, A. M., & Marx, A. (2025). Improving prediction accuracy
in chimeric proteins with windowed multiple sequence alignment. Computational
and Structural Biotechnology Journal. Elsevier. https://doi.org/10.1016/j.csbj.2025.07.039
chicago: Vedula, Sanketh, Alex M. Bronstein, and Ailie Marx. “Improving Prediction
Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” Computational
and Structural Biotechnology Journal. Elsevier, 2025. https://doi.org/10.1016/j.csbj.2025.07.039.
ieee: S. Vedula, A. M. Bronstein, and A. Marx, “Improving prediction accuracy in
chimeric proteins with windowed multiple sequence alignment,” Computational
and Structural Biotechnology Journal, vol. 27. Elsevier, pp. 3292–3298, 2025.
ista: Vedula S, Bronstein AM, Marx A. 2025. Improving prediction accuracy in chimeric
proteins with windowed multiple sequence alignment. Computational and Structural
Biotechnology Journal. 27, 3292–3298.
mla: Vedula, Sanketh, et al. “Improving Prediction Accuracy in Chimeric Proteins
with Windowed Multiple Sequence Alignment.” Computational and Structural Biotechnology
Journal, vol. 27, Elsevier, 2025, pp. 3292–98, doi:10.1016/j.csbj.2025.07.039.
short: S. Vedula, A.M. Bronstein, A. Marx, Computational and Structural Biotechnology
Journal 27 (2025) 3292–3298.
date_created: 2025-08-03T22:01:31Z
date_published: 2025-06-27T00:00:00Z
date_updated: 2025-11-27T14:09:59Z
day: '27'
ddc:
- '000'
- '570'
department:
- _id: AlBr
doi: 10.1016/j.csbj.2025.07.039
external_id:
isi:
- '001583543100001'
file:
- access_level: open_access
checksum: 78d01f30fc1dc11dd2bd1d7bb7ac8a62
content_type: application/pdf
creator: dernst
date_created: 2025-08-04T06:25:23Z
date_updated: 2025-08-04T06:25:23Z
file_id: '20104'
file_name: 2025_CompStrucBiotechJour_Vedula.pdf
file_size: 6609770
relation: main_file
success: 1
file_date_updated: 2025-08-04T06:25:23Z
has_accepted_license: '1'
intvolume: ' 27'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 3292-3298
publication: Computational and Structural Biotechnology Journal
publication_identifier:
eissn:
- 2001-0370
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/sankethvedula/AFChimera
record:
- id: '20103'
relation: software
status: public
scopus_import: '1'
status: public
title: Improving prediction accuracy in chimeric proteins with windowed multiple sequence
alignment
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 27
year: '2025'
...
---
OA_place: repository
_id: '20103'
abstract:
- lang: eng
text: Official implementation, windowed MSAs, and the predictions as reported in
the manuscript titled "Improving Prediction Accuracy in Chimeric Proteins with
Windowed Multiple Sequence Alignment". (2025-06-27)
article_processing_charge: No
author:
- first_name: Sanketh
full_name: Vedula, Sanketh
id: 94f2fe44-70fa-11f0-b76b-92922c09452b
last_name: Vedula
- 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: 'Vedula S, Bronstein AM, Marx A. Replication Data for: “Improving Prediction
Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” 2025.
doi:10.7910/DVN/DYEBVM'
apa: 'Vedula, S., Bronstein, A. M., & Marx, A. (2025). Replication Data for:
“Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence
Alignment.” Harvard Dataverse. https://doi.org/10.7910/DVN/DYEBVM'
chicago: 'Vedula, Sanketh, Alex M. Bronstein, and Ailie Marx. “Replication Data
for: ‘Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple
Sequence Alignment.’” Harvard Dataverse, 2025. https://doi.org/10.7910/DVN/DYEBVM.'
ieee: 'S. Vedula, A. M. Bronstein, and A. Marx, “Replication Data for: ‘Improving
Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.’”
Harvard Dataverse, 2025.'
ista: 'Vedula S, Bronstein AM, Marx A. 2025. Replication Data for: ‘Improving Prediction
Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment’, Harvard
Dataverse, 10.7910/DVN/DYEBVM.'
mla: 'Vedula, Sanketh, et al. Replication Data for: “Improving Prediction Accuracy
in Chimeric Proteins with Windowed Multiple Sequence Alignment.” Harvard Dataverse,
2025, doi:10.7910/DVN/DYEBVM.'
short: S. Vedula, A.M. Bronstein, A. Marx, (2025).
date_created: 2025-08-04T06:18:55Z
date_published: 2025-06-27T00:00:00Z
date_updated: 2025-11-27T14:09:58Z
day: '27'
ddc:
- '000'
department:
- _id: AlBr
doi: 10.7910/DVN/DYEBVM
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.7910/DVN/DYEBVM
month: '06'
oa: 1
oa_version: Published Version
publisher: Harvard Dataverse
related_material:
record:
- id: '20100'
relation: used_for_analysis_in
status: public
status: public
title: 'Replication Data for: "Improving Prediction Accuracy in Chimeric Proteins
with Windowed Multiple Sequence Alignment"'
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
_id: '20707'
abstract:
- lang: eng
text: 'Understanding physiological responses during running is critical for performance
optimization, tailored training prescriptions, and athlete health management.
We introduce a comprehensive framework—what we believe to be the first capable
of predicting instantaneous oxygen consumption (VO2) trajectories exclusively
from consumer-grade wearable data. Our approach employs two complementary physiological
models: (1) accurate modeling of heart rate (HR) dynamics via a physiologically
constrained ordinary differential equation (ODE) and neural Kalman filter, trained
on over 3 million HR observations, achieving 1-second interval predictions with
mean absolute errors as low as 2.81 bpm (correlation 0.87); and (2) leveraging
the principles of precise HR modeling, a novel VO2 prediction architecture requiring
only the initial second of VO2 data for calibration, enabling robust, sequence-to-sequence
metabolic demand estimation. Despite relying solely on smartwatch and chest-strap
data, our method achieves mean absolute percentage errors of approximately 13%,
effectively capturing rapid physiological transitions and steady-state conditions
across diverse running intensities. Our synchronized dataset, complemented by
blood lactate measurements, further lays the foundation for future noninvasive
metabolic zone identification. By embedding physiological constraints within modern
machine learning, this framework democratizes advanced metabolic monitoring, bridging
laboratory-grade accuracy and everyday accessibility, thus empowering both elite
athletes and recreational fitness enthusiasts.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Barak
full_name: Gahtan, Barak
last_name: Gahtan
- first_name: Sanketh
full_name: Vedula, Sanketh
last_name: Vedula
- first_name: Gil
full_name: Samuelly Leichtag, Gil
last_name: Samuelly Leichtag
- first_name: Einat
full_name: Kodesh, Einat
last_name: Kodesh
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Gahtan B, Vedula S, Samuelly Leichtag G, Kodesh E, Bronstein AM. From lab
to wrist: Bridging metabolic monitoring and consumer wearables for heart rate
and oxygen consumption modeling. In: Proceedings of the 27th International
Conference on Multimodal Interaction. Association for Computing Machinery;
2025:60-77. doi:10.1145/3716553.3750815'
apa: 'Gahtan, B., Vedula, S., Samuelly Leichtag, G., Kodesh, E., & Bronstein,
A. M. (2025). From lab to wrist: Bridging metabolic monitoring and consumer wearables
for heart rate and oxygen consumption modeling. In Proceedings of the 27th
International Conference on Multimodal Interaction (pp. 60–77). Canberra,
Australia: Association for Computing Machinery. https://doi.org/10.1145/3716553.3750815'
chicago: 'Gahtan, Barak, Sanketh Vedula, Gil Samuelly Leichtag, Einat Kodesh, and
Alex M. Bronstein. “From Lab to Wrist: Bridging Metabolic Monitoring and Consumer
Wearables for Heart Rate and Oxygen Consumption Modeling.” In Proceedings of
the 27th International Conference on Multimodal Interaction, 60–77. Association
for Computing Machinery, 2025. https://doi.org/10.1145/3716553.3750815.'
ieee: 'B. Gahtan, S. Vedula, G. Samuelly Leichtag, E. Kodesh, and A. M. Bronstein,
“From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart
rate and oxygen consumption modeling,” in Proceedings of the 27th International
Conference on Multimodal Interaction, Canberra, Australia, 2025, pp. 60–77.'
ista: 'Gahtan B, Vedula S, Samuelly Leichtag G, Kodesh E, Bronstein AM. 2025. From
lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate
and oxygen consumption modeling. Proceedings of the 27th International Conference
on Multimodal Interaction. ICMI: International Conference on Multimodal Interaction,
60–77.'
mla: 'Gahtan, Barak, et al. “From Lab to Wrist: Bridging Metabolic Monitoring and
Consumer Wearables for Heart Rate and Oxygen Consumption Modeling.” Proceedings
of the 27th International Conference on Multimodal Interaction, Association
for Computing Machinery, 2025, pp. 60–77, doi:10.1145/3716553.3750815.'
short: B. Gahtan, S. Vedula, G. Samuelly Leichtag, E. Kodesh, A.M. Bronstein, in:,
Proceedings of the 27th International Conference on Multimodal Interaction, Association
for Computing Machinery, 2025, pp. 60–77.
conference:
end_date: 2025-10-17
location: Canberra, Australia
name: 'ICMI: International Conference on Multimodal Interaction'
start_date: 2025-10-13
corr_author: '1'
date_created: 2025-11-30T23:02:08Z
date_published: 2025-10-12T00:00:00Z
date_updated: 2025-12-01T07:22:09Z
day: '12'
ddc:
- '000'
department:
- _id: AlBr
doi: 10.1145/3716553.3750815
external_id:
arxiv:
- '2505.00101'
file:
- access_level: open_access
checksum: f793472a71d27012244567b499a4967f
content_type: application/pdf
creator: dernst
date_created: 2025-12-01T07:19:06Z
date_updated: 2025-12-01T07:19:06Z
file_id: '20713'
file_name: 2025_ICMI_Gahtan.pdf
file_size: 3045062
relation: main_file
success: 1
file_date_updated: 2025-12-01T07:19:06Z
has_accepted_license: '1'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 60-77
publication: Proceedings of the 27th International Conference on Multimodal Interaction
publication_identifier:
isbn:
- '9798400714993'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'From lab to wrist: Bridging metabolic monitoring and consumer wearables for
heart rate and oxygen consumption modeling'
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: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2025'
...
---
_id: '18203'
abstract:
- lang: eng
text: Protein Data Bank (PDB) files list the relative spatial location of atoms
in a protein structure as the final output of the process of fitting and refining
to experimentally determined electron density measurements. Where experimental
evidence exists for multiple conformations, atoms are modelled in alternate locations.
Programs reading PDB files commonly ignore these alternate conformations by default
leaving users oblivious to the presence of alternate conformations in the structures
they analyze. This has led to underappreciation of their prevalence, under characterisation
of their features and limited the accessibility to this high-resolution data representing
structural ensembles. We have trawled PDB files to extract structural features
of residues with alternately located atoms. The output includes the distance between
alternate conformations and identifies the location of these segments within the
protein chain and in proximity of all other atoms within a defined radius. This
dataset should be of use in efforts to predict multiple structures from a single
sequence and support studies investigating protein flexibility and the association
with protein function.
article_number: '783'
article_processing_charge: No
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. A dataset of alternately located segments
in protein crystal structures. Scientific Data. 2024;11. doi:10.1038/s41597-024-03595-4
apa: Rosenberg, A. A., Marx, A., & Bronstein, A. M. (2024). A dataset of alternately
located segments in protein crystal structures. Scientific Data. Springer
Nature. https://doi.org/10.1038/s41597-024-03595-4
chicago: Rosenberg, Aviv A., Ailie Marx, and Alex M. Bronstein. “A Dataset of Alternately
Located Segments in Protein Crystal Structures.” Scientific Data. Springer
Nature, 2024. https://doi.org/10.1038/s41597-024-03595-4.
ieee: A. A. Rosenberg, A. Marx, and A. M. Bronstein, “A dataset of alternately located
segments in protein crystal structures,” Scientific Data, vol. 11. Springer
Nature, 2024.
ista: Rosenberg AA, Marx A, Bronstein AM. 2024. A dataset of alternately located
segments in protein crystal structures. Scientific Data. 11, 783.
mla: Rosenberg, Aviv A., et al. “A Dataset of Alternately Located Segments in Protein
Crystal Structures.” Scientific Data, vol. 11, 783, Springer Nature, 2024,
doi:10.1038/s41597-024-03595-4.
short: A.A. Rosenberg, A. Marx, A.M. Bronstein, Scientific Data 11 (2024).
date_created: 2024-10-08T11:50:30Z
date_published: 2024-07-17T00:00:00Z
date_updated: 2024-10-09T10:08:08Z
day: '17'
doi: 10.1038/s41597-024-03595-4
extern: '1'
external_id:
pmid:
- '39019896'
has_accepted_license: '1'
intvolume: ' 11'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41597-024-03595-4
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
publication: Scientific Data
publication_identifier:
issn:
- 2052-4463
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: A dataset of alternately located segments in protein crystal structures
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2024'
...
---
_id: '18204'
abstract:
- lang: eng
text: 'Non-linear dynamical systems describe numerous real-world phenomena, ranging
from the weather, to financial markets and disease progression. Individual systems
may share substantial common information, for example patients’ anatomy. Lately,
deep-learning has emerged as a leading method for data-driven modeling of non-linear
dynamical systems. Yet, despite recent breakthroughs, prior works largely ignored
the existence of shared information between different systems. However, such cases
are quite common, for example, in medicine: we may wish to have a patient-specific
model for some disease, but the data collected from a single patient is usually
too small to train a deep-learning model. Hence, we must properly utilize data
gathered from other patients. Here, we explicitly consider such cases by jointly
modeling multiple systems. We show that the current single-system models consistently
fail when trying to learn simultaneously from multiple systems. We suggest a framework
for jointly approximating the Koopman operators of multiple systems, while intrinsically
exploiting common information. We demonstrate how we can adapt to a new system
using order-of-magnitude less new data and show the superiority of our model over
competing methods, in terms of both forecasting ability and statistical fidelity,
across chaotic, cardiac, and climate systems.'
article_number: '141'
article_processing_charge: Yes
article_type: original
author:
- first_name: Yonatan
full_name: Elul, Yonatan
last_name: Elul
- first_name: Eyal
full_name: Rozenberg, Eyal
last_name: Rozenberg
- first_name: Amit
full_name: Boyarski, Amit
last_name: Boyarski
- first_name: Yael
full_name: Yaniv, Yael
last_name: Yaniv
- 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
citation:
ama: Elul Y, Rozenberg E, Boyarski A, Yaniv Y, Schuster A, Bronstein AM. Data-driven
modeling of interrelated dynamical systems. Communications Physics. 2024;7.
doi:10.1038/s42005-024-01626-5
apa: Elul, Y., Rozenberg, E., Boyarski, A., Yaniv, Y., Schuster, A., & Bronstein,
A. M. (2024). Data-driven modeling of interrelated dynamical systems. Communications
Physics. Springer Nature. https://doi.org/10.1038/s42005-024-01626-5
chicago: Elul, Yonatan, Eyal Rozenberg, Amit Boyarski, Yael Yaniv, Assaf Schuster,
and Alex M. Bronstein. “Data-Driven Modeling of Interrelated Dynamical Systems.”
Communications Physics. Springer Nature, 2024. https://doi.org/10.1038/s42005-024-01626-5.
ieee: Y. Elul, E. Rozenberg, A. Boyarski, Y. Yaniv, A. Schuster, and A. M. Bronstein,
“Data-driven modeling of interrelated dynamical systems,” Communications Physics,
vol. 7. Springer Nature, 2024.
ista: Elul Y, Rozenberg E, Boyarski A, Yaniv Y, Schuster A, Bronstein AM. 2024.
Data-driven modeling of interrelated dynamical systems. Communications Physics.
7, 141.
mla: Elul, Yonatan, et al. “Data-Driven Modeling of Interrelated Dynamical Systems.”
Communications Physics, vol. 7, 141, Springer Nature, 2024, doi:10.1038/s42005-024-01626-5.
short: Y. Elul, E. Rozenberg, A. Boyarski, Y. Yaniv, A. Schuster, A.M. Bronstein,
Communications Physics 7 (2024).
date_created: 2024-10-08T12:45:35Z
date_published: 2024-05-01T00:00:00Z
date_updated: 2024-10-09T10:12:11Z
day: '01'
doi: 10.1038/s42005-024-01626-5
extern: '1'
intvolume: ' 7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s42005-024-01626-5
month: '05'
oa: 1
oa_version: Published Version
publication: Communications Physics
publication_identifier:
issn:
- 2399-3650
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Data-driven modeling of interrelated dynamical systems
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7
year: '2024'
...
---
_id: '18205'
abstract:
- lang: eng
text: We explore numerically an unsupervised, physics-informed, deep learning-based
reconstruction technique for time-resolved imaging by multiplexed ptychography.
In our method, the untrained deep learning model replaces the iterative algorithm’s
update step, yielding superior reconstructions of multiple dynamic object frames
compared to conventional methodologies. More precisely, we demonstrate improvements
in image quality and resolution, while reducing sensitivity to the number of recorded
frames, the mutual orthogonality of different probe modes, overlap between neighboring
probe beams and the cutoff frequency of the ptychographic microscope – properties
that are generally of paramount importance for ptychographic reconstruction algorithms.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Omri
full_name: Wengrowicz, Omri
last_name: Wengrowicz
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Oren
full_name: Cohen, Oren
last_name: Cohen
citation:
ama: Wengrowicz O, Bronstein AM, Cohen O. Unsupervised physics-informed deep learning-based
reconstruction for time-resolved imaging by multiplexed ptychography. Optics
Express. 2024;32(6):8791-8803. doi:10.1364/oe.515445
apa: Wengrowicz, O., Bronstein, A. M., & Cohen, O. (2024). Unsupervised physics-informed
deep learning-based reconstruction for time-resolved imaging by multiplexed ptychography.
Optics Express. Optica Publishing Group. https://doi.org/10.1364/oe.515445
chicago: Wengrowicz, Omri, Alex M. Bronstein, and Oren Cohen. “Unsupervised Physics-Informed
Deep Learning-Based Reconstruction for Time-Resolved Imaging by Multiplexed Ptychography.”
Optics Express. Optica Publishing Group, 2024. https://doi.org/10.1364/oe.515445.
ieee: O. Wengrowicz, A. M. Bronstein, and O. Cohen, “Unsupervised physics-informed
deep learning-based reconstruction for time-resolved imaging by multiplexed ptychography,”
Optics Express, vol. 32, no. 6. Optica Publishing Group, pp. 8791–8803,
2024.
ista: Wengrowicz O, Bronstein AM, Cohen O. 2024. Unsupervised physics-informed deep
learning-based reconstruction for time-resolved imaging by multiplexed ptychography.
Optics Express. 32(6), 8791–8803.
mla: Wengrowicz, Omri, et al. “Unsupervised Physics-Informed Deep Learning-Based
Reconstruction for Time-Resolved Imaging by Multiplexed Ptychography.” Optics
Express, vol. 32, no. 6, Optica Publishing Group, 2024, pp. 8791–803, doi:10.1364/oe.515445.
short: O. Wengrowicz, A.M. Bronstein, O. Cohen, Optics Express 32 (2024) 8791–8803.
date_created: 2024-10-08T12:46:01Z
date_published: 2024-03-11T00:00:00Z
date_updated: 2024-10-09T10:26:44Z
day: '11'
doi: 10.1364/oe.515445
extern: '1'
external_id:
pmid:
- '38571128'
intvolume: ' 32'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1364/OE.515445
month: '03'
oa: 1
oa_version: Published Version
page: 8791-8803
pmid: 1
publication: Optics Express
publication_identifier:
issn:
- 1094-4087
publication_status: published
publisher: Optica Publishing Group
quality_controlled: '1'
scopus_import: '1'
status: public
title: Unsupervised physics-informed deep learning-based reconstruction for time-resolved
imaging by multiplexed ptychography
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2024'
...
---
_id: '18206'
abstract:
- lang: eng
text: In the context of in vitro fertilization (IVF), selecting embryos for transfer
is critical in determining pregnancy outcomes, with implantation as the essential
first milestone for a successful pregnancy. This study introduces the Bonna algorithm,
an advanced deep-learning framework engineered to predict embryo implantation
probabilities. The algorithm employs a sophisticated integration of machine-learning
techniques, utilizing MobileNetV2 for pixel and context embedding, a custom Pix2Pix
model for precise segmentation, and a Vision Transformer for additional depth
in embedding. MobileNetV2 was chosen for its robust feature extraction capabilities,
focusing on textures and edges. The custom Pix2Pix model is adapted for precise
segmentation of significant biological features such as the zona pellucida and
blastocyst cavity. The Vision Transformer adds a global perspective, capturing
complex patterns not apparent in local image segments. Tested on a dataset of
images of human blastocysts collected from Ukraine, Israel, and Spain, the Bonna
algorithm was rigorously validated through 10-fold cross-validation to ensure
its robustness and reliability. It demonstrates superior performance with a mean
area under the receiver operating characteristic curve (AUC) of 0.754, significantly
outperforming existing models. The study not only advances predictive accuracy
in embryo selection but also highlights the algorithm’s clinical applicability
due to reliable confidence reporting.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Gilad
full_name: Rave, Gilad
last_name: Rave
- first_name: Daniel E.
full_name: Fordham, Daniel E.
last_name: Fordham
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: David H.
full_name: Silver, David H.
last_name: Silver
citation:
ama: 'Rave G, Fordham DE, Bronstein AM, Silver DH. Enhancing predictive accuracy
in embryo implantation: The Bonna algorithm and its clinical implications. In:
First International Conference on Artificial Intelligence in Healthcare.
Vol 14976. Springer Nature; 2024:160-171. doi:10.1007/978-3-031-67285-9_12'
apa: 'Rave, G., Fordham, D. E., Bronstein, A. M., & Silver, D. H. (2024). Enhancing
predictive accuracy in embryo implantation: The Bonna algorithm and its clinical
implications. In First International Conference on Artificial Intelligence
in Healthcare (Vol. 14976, pp. 160–171). Swansea, United Kingdom: Springer
Nature. https://doi.org/10.1007/978-3-031-67285-9_12'
chicago: 'Rave, Gilad, Daniel E. Fordham, Alex M. Bronstein, and David H. Silver.
“Enhancing Predictive Accuracy in Embryo Implantation: The Bonna Algorithm and Its
Clinical Implications.” In First International Conference on Artificial Intelligence
in Healthcare, 14976:160–71. Springer Nature, 2024. https://doi.org/10.1007/978-3-031-67285-9_12.'
ieee: 'G. Rave, D. E. Fordham, A. M. Bronstein, and D. H. Silver, “Enhancing predictive
accuracy in embryo implantation: The Bonna algorithm and its clinical implications,”
in First International Conference on Artificial Intelligence in Healthcare,
Swansea, United Kingdom, 2024, vol. 14976, pp. 160–171.'
ista: 'Rave G, Fordham DE, Bronstein AM, Silver DH. 2024. Enhancing predictive accuracy
in embryo implantation: The Bonna algorithm and its clinical implications. First
International Conference on Artificial Intelligence in Healthcare. AIiH: Artificial
Intelligence in Healthcare, LNCS, vol. 14976, 160–171.'
mla: 'Rave, Gilad, et al. “Enhancing Predictive Accuracy in Embryo Implantation:
The Bonna Algorithm and Its Clinical Implications.” First International Conference
on Artificial Intelligence in Healthcare, vol. 14976, Springer Nature, 2024,
pp. 160–71, doi:10.1007/978-3-031-67285-9_12.'
short: G. Rave, D.E. Fordham, A.M. Bronstein, D.H. Silver, in:, First International
Conference on Artificial Intelligence in Healthcare, Springer Nature, 2024, pp.
160–171.
conference:
end_date: 2024-09-06
location: Swansea, United Kingdom
name: 'AIiH: Artificial Intelligence in Healthcare'
start_date: 2024-09-04
date_created: 2024-10-08T12:46:23Z
date_published: 2024-08-15T00:00:00Z
date_updated: 2024-10-09T10:33:39Z
day: '15'
doi: 10.1007/978-3-031-67285-9_12
extern: '1'
intvolume: ' 14976'
language:
- iso: eng
month: '08'
oa_version: None
page: 160-171
publication: First International Conference on Artificial Intelligence in Healthcare
publication_identifier:
eisbn:
- '9783031672859'
eissn:
- 1611-3349
isbn:
- '9783031672842'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Enhancing predictive accuracy in embryo implantation: The Bonna algorithm
and its clinical implications'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14976
year: '2024'
...
---
_id: '18207'
abstract:
- lang: eng
text: Comparison of myoglobin structures reveals that protein isolated from horse
heart consistently adopts an alternate turn conformation in comparison to its
homologues. Analysis of hundreds of high-resolution structures discounts crystallization
conditions or the surrounding amino acid protein environment as explaining this
difference, that is also not captured by the AlphaFold prediction. Rather, a water
molecule is identified as stabilizing the conformation in the horse heart structure,
which immediately reverts to the whale conformation in molecular dynamics simulations
excluding that structural water.
article_number: '6094'
article_processing_charge: No
article_type: original
author:
- 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: Bronstein AM, Marx A. Water stabilizes an alternate turn conformation in horse
heart myoglobin. Scientific Reports. 2023;13. doi:10.1038/s41598-023-32821-z
apa: Bronstein, A. M., & Marx, A. (2023). Water stabilizes an alternate turn
conformation in horse heart myoglobin. Scientific Reports. Springer Nature.
https://doi.org/10.1038/s41598-023-32821-z
chicago: Bronstein, Alex M., and Ailie Marx. “Water Stabilizes an Alternate Turn
Conformation in Horse Heart Myoglobin.” Scientific Reports. Springer Nature,
2023. https://doi.org/10.1038/s41598-023-32821-z.
ieee: A. M. Bronstein and A. Marx, “Water stabilizes an alternate turn conformation
in horse heart myoglobin,” Scientific Reports, vol. 13. Springer Nature,
2023.
ista: Bronstein AM, Marx A. 2023. Water stabilizes an alternate turn conformation
in horse heart myoglobin. Scientific Reports. 13, 6094.
mla: Bronstein, Alex M., and Ailie Marx. “Water Stabilizes an Alternate Turn Conformation
in Horse Heart Myoglobin.” Scientific Reports, vol. 13, 6094, Springer
Nature, 2023, doi:10.1038/s41598-023-32821-z.
short: A.M. Bronstein, A. Marx, Scientific Reports 13 (2023).
date_created: 2024-10-08T12:46:41Z
date_published: 2023-04-13T00:00:00Z
date_updated: 2024-10-09T10:39:26Z
day: '13'
doi: 10.1038/s41598-023-32821-z
extern: '1'
external_id:
pmid:
- '37055458'
has_accepted_license: '1'
intvolume: ' 13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41598-023-32821-z
month: '04'
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: Water stabilizes an alternate turn conformation in horse heart myoglobin
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2023'
...
---
_id: '18208'
abstract:
- lang: eng
text: The holy grail of materials science is de novo molecular design, meaning engineering
molecules with desired characteristics. The introduction of generative deep learning
has greatly advanced efforts in this direction, yet molecular discovery remains
challenging and often inefficient. Herein we introduce GaUDI, a guided diffusion
model for inverse molecular design that combines an equivariant graph neural net
for property prediction and a generative diffusion model. We demonstrate GaUDI’s
effectiveness in designing molecules for organic electronic applications by using
single- and multiple-objective tasks applied to a generated dataset of 475,000
polycyclic aromatic systems. GaUDI shows improved conditional design, generating
molecules with optimal properties and even going beyond the original distribution
to suggest better molecules than those in the dataset. In addition to point-wise
targets, GaUDI can also be guided toward open-ended targets (for example, a minimum
or maximum) and in all cases achieves close to 100% validity of generated molecules.
article_processing_charge: No
article_type: original
author:
- first_name: Tomer
full_name: Weiss, Tomer
last_name: Weiss
- first_name: Eduardo
full_name: Mayo Yanes, Eduardo
last_name: Mayo Yanes
- first_name: Sabyasachi
full_name: Chakraborty, Sabyasachi
last_name: Chakraborty
- first_name: Luca
full_name: Cosmo, Luca
last_name: Cosmo
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Renana
full_name: Gershoni-Poranne, Renana
last_name: Gershoni-Poranne
citation:
ama: Weiss T, Mayo Yanes E, Chakraborty S, Cosmo L, Bronstein AM, Gershoni-Poranne
R. Guided diffusion for inverse molecular design. Nature Computational Science.
2023;3(10):873-882. doi:10.1038/s43588-023-00532-0
apa: Weiss, T., Mayo Yanes, E., Chakraborty, S., Cosmo, L., Bronstein, A. M., &
Gershoni-Poranne, R. (2023). Guided diffusion for inverse molecular design. Nature
Computational Science. Springer Nature. https://doi.org/10.1038/s43588-023-00532-0
chicago: Weiss, Tomer, Eduardo Mayo Yanes, Sabyasachi Chakraborty, Luca Cosmo, Alex
M. Bronstein, and Renana Gershoni-Poranne. “Guided Diffusion for Inverse Molecular
Design.” Nature Computational Science. Springer Nature, 2023. https://doi.org/10.1038/s43588-023-00532-0.
ieee: T. Weiss, E. Mayo Yanes, S. Chakraborty, L. Cosmo, A. M. Bronstein, and R.
Gershoni-Poranne, “Guided diffusion for inverse molecular design,” Nature Computational
Science, vol. 3, no. 10. Springer Nature, pp. 873–882, 2023.
ista: Weiss T, Mayo Yanes E, Chakraborty S, Cosmo L, Bronstein AM, Gershoni-Poranne
R. 2023. Guided diffusion for inverse molecular design. Nature Computational Science.
3(10), 873–882.
mla: Weiss, Tomer, et al. “Guided Diffusion for Inverse Molecular Design.” Nature
Computational Science, vol. 3, no. 10, Springer Nature, 2023, pp. 873–82,
doi:10.1038/s43588-023-00532-0.
short: T. Weiss, E. Mayo Yanes, S. Chakraborty, L. Cosmo, A.M. Bronstein, R. Gershoni-Poranne,
Nature Computational Science 3 (2023) 873–882.
date_created: 2024-10-08T12:46:58Z
date_published: 2023-10-05T00:00:00Z
date_updated: 2024-10-09T10:44:41Z
day: '05'
doi: 10.1038/s43588-023-00532-0
extern: '1'
external_id:
pmid:
- '38177755'
intvolume: ' 3'
issue: '10'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.26434/chemrxiv-2023-z8ltp
month: '10'
oa: 1
oa_version: Preprint
page: 873-882
pmid: 1
publication: Nature Computational Science
publication_identifier:
issn:
- 2662-8457
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Guided diffusion for inverse molecular design
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2023'
...
---
_id: '18209'
abstract:
- lang: eng
text: In this work, interpretable deep learning was used to identify structure–property
relationships governing the HOMO–LUMO gap and the relative stability of polybenzenoid
hydrocarbons (PBHs) using a ring-based graph representation. This representation
was combined with a subunit-based perception of PBHs, allowing chemical insights
to be presented in terms of intuitive and simple structural motifs. The resulting
insights agree with conventional organic chemistry knowledge and electronic structure-based
analyses and also reveal new behaviors and identify influential structural motifs.
In particular, we evaluated and compared the effects of linear, angular, and branching
motifs on these two molecular properties and explored the role of dispersion in
mitigating the torsional strain inherent in nonplanar PBHs. Hence, the observed
regularities and the proposed analysis contribute to a deeper understanding of
the behavior of PBHs and form the foundation for design strategies for new functional
PBHs.
article_processing_charge: No
article_type: original
author:
- first_name: Tomer
full_name: Weiss, Tomer
last_name: Weiss
- first_name: Alexandra
full_name: Wahab, Alexandra
last_name: Wahab
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Renana
full_name: Gershoni-Poranne, Renana
last_name: Gershoni-Poranne
citation:
ama: Weiss T, Wahab A, Bronstein AM, Gershoni-Poranne R. Interpretable deep-learning
unveils structure–property relationships in polybenzenoid hydrocarbons. The
Journal of Organic Chemistry. 2023;88(14):9645-9656. doi:10.1021/acs.joc.2c02381
apa: Weiss, T., Wahab, A., Bronstein, A. M., & Gershoni-Poranne, R. (2023).
Interpretable deep-learning unveils structure–property relationships in polybenzenoid
hydrocarbons. The Journal of Organic Chemistry. American Chemical Society.
https://doi.org/10.1021/acs.joc.2c02381
chicago: Weiss, Tomer, Alexandra Wahab, Alex M. Bronstein, and Renana Gershoni-Poranne.
“Interpretable Deep-Learning Unveils Structure–Property Relationships in Polybenzenoid
Hydrocarbons.” The Journal of Organic Chemistry. American Chemical Society,
2023. https://doi.org/10.1021/acs.joc.2c02381.
ieee: T. Weiss, A. Wahab, A. M. Bronstein, and R. Gershoni-Poranne, “Interpretable
deep-learning unveils structure–property relationships in polybenzenoid hydrocarbons,”
The Journal of Organic Chemistry, vol. 88, no. 14. American Chemical Society,
pp. 9645–9656, 2023.
ista: Weiss T, Wahab A, Bronstein AM, Gershoni-Poranne R. 2023. Interpretable deep-learning
unveils structure–property relationships in polybenzenoid hydrocarbons. The Journal
of Organic Chemistry. 88(14), 9645–9656.
mla: Weiss, Tomer, et al. “Interpretable Deep-Learning Unveils Structure–Property
Relationships in Polybenzenoid Hydrocarbons.” The Journal of Organic Chemistry,
vol. 88, no. 14, American Chemical Society, 2023, pp. 9645–56, doi:10.1021/acs.joc.2c02381.
short: T. Weiss, A. Wahab, A.M. Bronstein, R. Gershoni-Poranne, The Journal of Organic
Chemistry 88 (2023) 9645–9656.
date_created: 2024-10-08T12:47:17Z
date_published: 2023-01-25T00:00:00Z
date_updated: 2024-10-09T10:49:42Z
day: '25'
doi: 10.1021/acs.joc.2c02381
extern: '1'
external_id:
pmid:
- '36696660'
intvolume: ' 88'
issue: '14'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 10.26434/chemrxiv-2022-krng1
month: '01'
oa: 1
oa_version: Preprint
page: 9645-9656
pmid: 1
publication: The Journal of Organic Chemistry
publication_identifier:
eissn:
- 1520-6904
issn:
- 0022-3263
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Interpretable deep-learning unveils structure–property relationships in polybenzenoid
hydrocarbons
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 88
year: '2023'
...
---
_id: '18212'
abstract:
- lang: eng
text: The high memory bandwidth demand of sparse embedding layers continues to be
a critical challenge in scaling the performance of recommendation models. While
prior works have exploited heterogeneous memory system designs and partial embedding
sum memoization techniques, they offer limited benefits. This is because prior
designs either target a very small subset of embeddings to simplify their analysis
or incur a high processing cost to account for all embeddings, which does not
scale with the large sizes of modern embedding tables. This paper proposes GRACE-a
lightweight and scalable graph-based algorithm-system co-design framework to significantly
improve the embedding layer performance of recommendation models. GRACE proposes
a novel Item Co-occurrence Graph (ICG) that scalably records item co-occurrences.
GRACE then presents a new system-aware ICG clustering algorithm to find frequently
accessed item combinations of arbitrary lengths to compute and memoize their partial
sums. High-frequency partial sums are stored in a software-managed cache space
to reduce memory traffic and improve the throughput of computing sparse features.
We further present a cache data layout and low-cost address computation logic
to efficiently lookup item embeddings and their partial sums. Our evaluation shows
that GRACE significantly outperforms the state-of-the-art techniques SPACE and
MERCI by 1.5x and 1.4x, respectively.
article_processing_charge: No
author:
- first_name: Haojie
full_name: Ye, Haojie
last_name: Ye
- first_name: Sanketh
full_name: Vedula, Sanketh
last_name: Vedula
- first_name: Yuhan
full_name: Chen, Yuhan
last_name: Chen
- first_name: Yichen
full_name: Yang, Yichen
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: Ronald
full_name: Dreslinski, Ronald
last_name: Dreslinski
- first_name: Trevor
full_name: Mudge, Trevor
last_name: Mudge
- first_name: Nishil
full_name: Talati, Nishil
last_name: Talati
citation:
ama: 'Ye H, Vedula S, Chen Y, et al. GRACE: A scalable graph-based approach to accelerating
recommendation model inference. In: Proceedings of the 28th ACM International
Conference on Architectural Support for Programming Languages and Operating Systems.
Vol 11. Association for Computing Machinery; 2023:282-301. doi:10.1145/3582016.3582029'
apa: 'Ye, H., Vedula, S., Chen, Y., Yang, Y., Bronstein, A. M., Dreslinski, R.,
… Talati, N. (2023). GRACE: A scalable graph-based approach to accelerating recommendation
model inference. In Proceedings of the 28th ACM International Conference on
Architectural Support for Programming Languages and Operating Systems (Vol.
11, pp. 282–301). Association for Computing Machinery. https://doi.org/10.1145/3582016.3582029'
chicago: 'Ye, Haojie, Sanketh Vedula, Yuhan Chen, Yichen Yang, Alex M. Bronstein,
Ronald Dreslinski, Trevor Mudge, and Nishil Talati. “GRACE: A Scalable Graph-Based
Approach to Accelerating Recommendation Model Inference.” In Proceedings of
the 28th ACM International Conference on Architectural Support for Programming
Languages and Operating Systems, 11:282–301. Association for Computing Machinery,
2023. https://doi.org/10.1145/3582016.3582029.'
ieee: 'H. Ye et al., “GRACE: A scalable graph-based approach to accelerating
recommendation model inference,” in Proceedings of the 28th ACM International
Conference on Architectural Support for Programming Languages and Operating Systems,
2023, vol. 11, no. 3, pp. 282–301.'
ista: 'Ye H, Vedula S, Chen Y, Yang Y, Bronstein AM, Dreslinski R, Mudge T, Talati
N. 2023. GRACE: A scalable graph-based approach to accelerating recommendation
model inference. Proceedings of the 28th ACM International Conference on Architectural
Support for Programming Languages and Operating Systems. vol. 11, 282–301.'
mla: 'Ye, Haojie, et al. “GRACE: A Scalable Graph-Based Approach to Accelerating
Recommendation Model Inference.” Proceedings of the 28th ACM International
Conference on Architectural Support for Programming Languages and Operating Systems,
vol. 11, no. 3, Association for Computing Machinery, 2023, pp. 282–301, doi:10.1145/3582016.3582029.'
short: H. Ye, S. Vedula, Y. Chen, Y. Yang, A.M. Bronstein, R. Dreslinski, T. Mudge,
N. Talati, in:, Proceedings of the 28th ACM International Conference on Architectural
Support for Programming Languages and Operating Systems, Association for Computing
Machinery, 2023, pp. 282–301.
date_created: 2024-10-08T12:48:11Z
date_published: 2023-03-01T00:00:00Z
date_updated: 2024-10-09T11:21:19Z
day: '01'
doi: 10.1145/3582016.3582029
extern: '1'
intvolume: ' 11'
issue: '3'
language:
- iso: eng
month: '03'
oa_version: None
page: 282-301
publication: Proceedings of the 28th ACM International Conference on Architectural
Support for Programming Languages and Operating Systems
publication_identifier:
isbn:
- '9781450399180'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://doi.org/10.5281/zenodo.7699872
scopus_import: '1'
status: public
title: 'GRACE: A scalable graph-based approach to accelerating recommendation model
inference'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2023'
...
---
_id: '18213'
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. Some solutions assume that auxiliary information on
known matches or node or edge attributes is available, or utilize arbitrary graph
features. Such methods fare poorly in the pure form of the problem, in which only
graph structures are given. Other proposals translate the problem to one of aligning
node embeddings, yet, by doing so, provide only a single-scale view of the graph.\r\nIn
this article, 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 first establishes a correspondence between functions derived
from Laplacian matrix eigenvectors, which capture multiscale structural characteristics,
and then exploits this correspondence to align nodes. We enhance the basic form
of GRASP by altering two of its components, namely the embedding method and the
assignment procedure it employs, leveraging its modular, hence adaptable design.
Our experimental study, featuring noise levels higher than anything used in previous
studies, shows that the enhanced form of GRASP outperforms scalable state-of-the-art
methods for graph alignment across noise levels and graph types, and performs
competitively with respect to the best non-scalable ones. We include in our study
another modular graph alignment algorithm, CONE, which is also adaptable thanks
to its modular nature, and show it can manage graphs with skewed power-law degree
distributions."
article_number: '50'
article_processing_charge: No
article_type: original
author:
- first_name: Judith
full_name: Hermanns, Judith
last_name: Hermanns
- first_name: Konstantinos
full_name: Skitsas, Konstantinos
last_name: Skitsas
- 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: Kyster, Alexander
last_name: Kyster
- first_name: Simon
full_name: Nielsen, Simon
last_name: Nielsen
- 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, Skitsas K, Tsitsulin A, et al. GRASP: Scalable graph alignment
by spectral corresponding functions. ACM Transactions on Knowledge Discovery
from Data. 2023;17(4). doi:10.1145/3561058'
apa: 'Hermanns, J., Skitsas, K., Tsitsulin, A., Munkhoeva, M., Kyster, A., Nielsen,
S., … Karras, P. (2023). GRASP: Scalable graph alignment by spectral corresponding
functions. ACM Transactions on Knowledge Discovery from Data. Association
for Computing Machinery. https://doi.org/10.1145/3561058'
chicago: 'Hermanns, Judith, Konstantinos Skitsas, Anton Tsitsulin, Marina Munkhoeva,
Alexander Kyster, Simon Nielsen, Alex M. Bronstein, Davide Mottin, and Panagiotis
Karras. “GRASP: Scalable Graph Alignment by Spectral Corresponding Functions.”
ACM Transactions on Knowledge Discovery from Data. Association for Computing
Machinery, 2023. https://doi.org/10.1145/3561058.'
ieee: 'J. Hermanns et al., “GRASP: Scalable graph alignment by spectral corresponding
functions,” ACM Transactions on Knowledge Discovery from Data, vol. 17,
no. 4. Association for Computing Machinery, 2023.'
ista: 'Hermanns J, Skitsas K, Tsitsulin A, Munkhoeva M, Kyster A, Nielsen S, Bronstein
AM, Mottin D, Karras P. 2023. GRASP: Scalable graph alignment by spectral corresponding
functions. ACM Transactions on Knowledge Discovery from Data. 17(4), 50.'
mla: 'Hermanns, Judith, et al. “GRASP: Scalable Graph Alignment by Spectral Corresponding
Functions.” ACM Transactions on Knowledge Discovery from Data, vol. 17,
no. 4, 50, Association for Computing Machinery, 2023, doi:10.1145/3561058.'
short: J. Hermanns, K. Skitsas, A. Tsitsulin, M. Munkhoeva, A. Kyster, S. Nielsen,
A.M. Bronstein, D. Mottin, P. Karras, ACM Transactions on Knowledge Discovery
from Data 17 (2023).
date_created: 2024-10-08T12:48:38Z
date_published: 2023-02-24T00:00:00Z
date_updated: 2024-10-09T11:24:50Z
day: '24'
doi: 10.1145/3561058
extern: '1'
intvolume: ' 17'
issue: '4'
language:
- iso: eng
month: '02'
oa_version: None
publication: ACM Transactions on Knowledge Discovery from Data
publication_identifier:
eissn:
- 1556-472X
issn:
- 1556-4681
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'GRASP: Scalable graph alignment by spectral corresponding functions'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2023'
...
---
_id: '18214'
abstract:
- lang: eng
text: "Graph sparsification is a technique that approximates a given graph by a
sparse graph with a subset of vertices and/or edges. The goal of an effective
sparsification algorithm is to maintain specific graph properties relevant to
the downstream task while minimizing the graph's size. Graph algorithms often
suffer from long execution time due to the irregularity and the large real-world
graph size. Graph sparsification can be applied to greatly reduce the run time
of graph algorithms by substituting the full graph with a much smaller sparsified
graph, without significantly degrading the output quality. However, the interaction
between numerous sparsifiers and graph properties is not widely explored, and
the potential of graph sparsification is not fully understood.\r\n In
this work, we cover 16 widely-used graph metrics, 12 representative graph sparsification
algorithms, and 14 real-world input graphs spanning various categories, exhibiting
diverse characteristics, sizes, and densities. We developed a framework to extensively
assess the performance of these sparsification algorithms against graph metrics,
and provide insights to the results. Our study shows that there is no one sparsifier
that performs the best in preserving all graph properties, e.g. sparsifiers that
preserve distance-related graph properties (eccentricity) struggle to perform
well on Graph Neural Networks (GNN). This paper presents a comprehensive experimental
study evaluating the performance of sparsification algorithms in preserving essential
graph metrics. The insights inform future research in incorporating matching graph
sparsification to graph algorithms to maximize benefits while minimizing quality
degradation. Furthermore, we provide a framework to facilitate the future evaluation
of evolving sparsification algorithms, graph metrics, and ever-growing graph data."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yuhan
full_name: Chen, Yuhan
last_name: Chen
- first_name: Haojie
full_name: Ye, Haojie
last_name: Ye
- first_name: Sanketh
full_name: Vedula, Sanketh
last_name: Vedula
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Ronald
full_name: Dreslinski, Ronald
last_name: Dreslinski
- first_name: Trevor
full_name: Mudge, Trevor
last_name: Mudge
- first_name: Nishil
full_name: Talati, Nishil
last_name: Talati
citation:
ama: Chen Y, Ye H, Vedula S, et al. Demystifying graph sparsification algorithms
in graph properties preservation. Proceedings of the VLDB Endowment. 2023;17(3):427-440.
doi:10.14778/3632093.3632106
apa: Chen, Y., Ye, H., Vedula, S., Bronstein, A. M., Dreslinski, R., Mudge, T.,
& Talati, N. (2023). Demystifying graph sparsification algorithms in graph
properties preservation. Proceedings of the VLDB Endowment. Association
for Computing Machinery. https://doi.org/10.14778/3632093.3632106
chicago: Chen, Yuhan, Haojie Ye, Sanketh Vedula, Alex M. Bronstein, Ronald Dreslinski,
Trevor Mudge, and Nishil Talati. “Demystifying Graph Sparsification Algorithms
in Graph Properties Preservation.” Proceedings of the VLDB Endowment. Association
for Computing Machinery, 2023. https://doi.org/10.14778/3632093.3632106.
ieee: Y. Chen et al., “Demystifying graph sparsification algorithms in graph
properties preservation,” Proceedings of the VLDB Endowment, vol. 17, no.
3. Association for Computing Machinery, pp. 427–440, 2023.
ista: Chen Y, Ye H, Vedula S, Bronstein AM, Dreslinski R, Mudge T, Talati N. 2023.
Demystifying graph sparsification algorithms in graph properties preservation.
Proceedings of the VLDB Endowment. 17(3), 427–440.
mla: Chen, Yuhan, et al. “Demystifying Graph Sparsification Algorithms in Graph
Properties Preservation.” Proceedings of the VLDB Endowment, vol. 17, no.
3, Association for Computing Machinery, 2023, pp. 427–40, doi:10.14778/3632093.3632106.
short: Y. Chen, H. Ye, S. Vedula, A.M. Bronstein, R. Dreslinski, T. Mudge, N. Talati,
Proceedings of the VLDB Endowment 17 (2023) 427–440.
date_created: 2024-10-08T12:48:57Z
date_published: 2023-11-01T00:00:00Z
date_updated: 2024-10-09T11:28:33Z
day: '01'
doi: 10.14778/3632093.3632106
extern: '1'
external_id:
arxiv:
- '2311.12314'
intvolume: ' 17'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2311.12314
month: '11'
oa: 1
oa_version: Preprint
page: 427-440
publication: Proceedings of the VLDB Endowment
publication_identifier:
issn:
- 2150-8097
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Demystifying graph sparsification algorithms in graph properties preservation
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2023'
...
---
_id: '18215'
abstract:
- lang: eng
text: 'We study the problem of real-time scheduling in a multi-hop millimeter-wave
(mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called
Adaptive Activator RL (AARL), which determines the subset of mmWave links that
should be activated during each time slot and the power level for each link. The
most important property of AARL is its ability to make scheduling decisions within
the strict time frame constraints of typical 5G mmWave networks. AARL can handle
a variety of network topologies, network loads, and interference models, it can
also adapt to different workloads. We demonstrate the operation of AARL on several
topologies: a small topology with 10 links, a moderately-sized mesh with 48 links,
and a large topology with 96 links. We show that for each topology, we compare
the throughput obtained by AARL to that of a benchmark algorithm called RPMA (Residual
Profit Maximizer Algorithm). The most important advantage of AARL compared to
RPMA is that it is much faster and can make the necessary scheduling decisions
very rapidly during every time slot, while RPMA cannot. In addition, the quality
of the scheduling decisions made by AARL outperforms those made by RPMA.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Barak
full_name: Gahtan, Barak
last_name: Gahtan
- first_name: Reuven
full_name: Cohen, Reuven
last_name: Cohen
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Gil
full_name: Kedar, Gil
last_name: Kedar
citation:
ama: 'Gahtan B, Cohen R, Bronstein AM, Kedar G. Using deep reinforcement learning
for mmWave real-time scheduling. In: 14th International Conference on Network
of the Future. IEEE; 2023:71-79. doi:10.1109/nof58724.2023.10302794'
apa: 'Gahtan, B., Cohen, R., Bronstein, A. M., & Kedar, G. (2023). Using deep
reinforcement learning for mmWave real-time scheduling. In 14th International
Conference on Network of the Future (pp. 71–79). Izmir, Turkiye: IEEE. https://doi.org/10.1109/nof58724.2023.10302794'
chicago: Gahtan, Barak, Reuven Cohen, Alex M. Bronstein, and Gil Kedar. “Using Deep
Reinforcement Learning for MmWave Real-Time Scheduling.” In 14th International
Conference on Network of the Future, 71–79. IEEE, 2023. https://doi.org/10.1109/nof58724.2023.10302794.
ieee: B. Gahtan, R. Cohen, A. M. Bronstein, and G. Kedar, “Using deep reinforcement
learning for mmWave real-time scheduling,” in 14th International Conference
on Network of the Future, Izmir, Turkiye, 2023, pp. 71–79.
ista: 'Gahtan B, Cohen R, Bronstein AM, Kedar G. 2023. Using deep reinforcement
learning for mmWave real-time scheduling. 14th International Conference on Network
of the Future. NoF: Conference on Network of the Future, 71–79.'
mla: Gahtan, Barak, et al. “Using Deep Reinforcement Learning for MmWave Real-Time
Scheduling.” 14th International Conference on Network of the Future, IEEE,
2023, pp. 71–79, doi:10.1109/nof58724.2023.10302794.
short: B. Gahtan, R. Cohen, A.M. Bronstein, G. Kedar, in:, 14th International Conference
on Network of the Future, IEEE, 2023, pp. 71–79.
conference:
end_date: 2023-10-06
location: Izmir, Turkiye
name: 'NoF: Conference on Network of the Future'
start_date: 2023-10-04
date_created: 2024-10-08T12:50:18Z
date_published: 2023-11-01T00:00:00Z
date_updated: 2024-10-09T11:40:45Z
day: '01'
doi: 10.1109/nof58724.2023.10302794
extern: '1'
external_id:
arxiv:
- '2210.01423'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2210.01423'
month: '11'
oa: 1
oa_version: Preprint
page: 71-79
publication: 14th International Conference on Network of the Future
publication_identifier:
eissn:
- 2833-0072
isbn:
- '9798350338089'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Using deep reinforcement learning for mmWave real-time scheduling
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '18216'
abstract:
- lang: eng
text: Protein structure, both at the global and local level, dictates function.
Proteins fold from chains of amino acids, forming secondary structures, α-helices
and β-strands, that, at least for globular proteins, subsequently fold into a
three-dimensional structure. Here, we show that a Ramachandran-type plot focusing
on the two dihedral angles separated by the peptide bond, and entirely contained
within an amino acid pair, defines a local structural unit. We further demonstrate
the usefulness of this cross-peptide-bond Ramachandran plot by showing that it
captures β-turn conformations in coil regions, that traditional Ramachandran plot
outliers fall into occupied regions of our plot, and that thermophilic proteins
prefer specific amino acid pair conformations. Further, we demonstrate experimentally
that the effect of a point mutation on backbone conformation and protein stability
depends on the amino acid pair context, i.e., the identity of the adjacent amino
acid, in a manner predictable by our method.
article_number: e2301064120
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Aviv A.
full_name: Rosenberg, Aviv A.
last_name: Rosenberg
- first_name: Nitsan
full_name: Yehishalom, Nitsan
last_name: Yehishalom
- 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, Yehishalom N, Marx A, Bronstein AM. An amino-domino model described
by a cross-peptide-bond Ramachandran plot defines amino acid pairs as local structural
units. Proceedings of the National Academy of Sciences. 2023;120(44). doi:10.1073/pnas.2301064120
apa: Rosenberg, A. A., Yehishalom, N., Marx, A., & Bronstein, A. M. (2023).
An amino-domino model described by a cross-peptide-bond Ramachandran plot defines
amino acid pairs as local structural units. Proceedings of the National Academy
of Sciences. National Academy of Sciences. https://doi.org/10.1073/pnas.2301064120
chicago: Rosenberg, Aviv A., Nitsan Yehishalom, Ailie Marx, and Alex M. Bronstein.
“An Amino-Domino Model Described by a Cross-Peptide-Bond Ramachandran Plot Defines
Amino Acid Pairs as Local Structural Units.” Proceedings of the National Academy
of Sciences. National Academy of Sciences, 2023. https://doi.org/10.1073/pnas.2301064120.
ieee: A. A. Rosenberg, N. Yehishalom, A. Marx, and A. M. Bronstein, “An amino-domino
model described by a cross-peptide-bond Ramachandran plot defines amino acid pairs
as local structural units,” Proceedings of the National Academy of Sciences,
vol. 120, no. 44. National Academy of Sciences, 2023.
ista: Rosenberg AA, Yehishalom N, Marx A, Bronstein AM. 2023. An amino-domino model
described by a cross-peptide-bond Ramachandran plot defines amino acid pairs as
local structural units. Proceedings of the National Academy of Sciences. 120(44),
e2301064120.
mla: Rosenberg, Aviv A., et al. “An Amino-Domino Model Described by a Cross-Peptide-Bond
Ramachandran Plot Defines Amino Acid Pairs as Local Structural Units.” Proceedings
of the National Academy of Sciences, vol. 120, no. 44, e2301064120, National
Academy of Sciences, 2023, doi:10.1073/pnas.2301064120.
short: A.A. Rosenberg, N. Yehishalom, A. Marx, A.M. Bronstein, Proceedings of the
National Academy of Sciences 120 (2023).
date_created: 2024-10-08T12:50:36Z
date_published: 2023-10-25T00:00:00Z
date_updated: 2024-10-09T11:55:12Z
day: '25'
doi: 10.1073/pnas.2301064120
extern: '1'
external_id:
pmid:
- '37878722'
intvolume: ' 120'
issue: '44'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1073/pnas.2301064120
month: '10'
oa: 1
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: An amino-domino model described by a cross-peptide-bond Ramachandran plot defines
amino acid pairs as local structural units
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 120
year: '2023'
...
---
_id: '18217'
abstract:
- lang: eng
text: A central challenge in building robotic prostheses is the creation of a sensor-based
system able to read physiological signals from the lower limb and instruct a robotic
hand to perform various tasks. Existing systems typically perform discrete gestures
such as pointing or grasping, by employing electromyography (EMG) or ultrasound
(US) technologies to analyze muscle states. While estimating finger gestures has
been done in the past by detecting prominent gestures, we are interested in detection,
or inference, done in the context of fine motions that evolve over time. Examples
include motions occurring when performing fine and dexterous tasks such as keyboard
typing or piano playing. We consider this task as an important step towards higher
adoption rates of robotic prostheses among arm amputees, as it has the potential
to dramatically increase functionality in performing daily tasks. To this end,
we present an end-to-end robotic system, which can successfully infer fine finger
motions. This is achieved by modeling the hand as a robotic manipulator and using
it as an intermediate representation to encode muscles' dynamics from a sequence
of US images. We evaluated our method by collecting data from a group of subjects
and demonstrating how it can be used to replay music played or text typed. To
the best of our knowledge, this is the first study demonstrating these downstream
tasks within an end-to-end system.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dean
full_name: Zadok, Dean
last_name: Zadok
- first_name: Oren
full_name: Salzman, Oren
last_name: Salzman
- first_name: Alon
full_name: Wolf, Alon
last_name: Wolf
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Zadok D, Salzman O, Wolf A, Bronstein AM. Towards predicting fine finger motions
from ultrasound images via kinematic representation. In: 2023 IEEE International
Conference on Robotics and Automation. Vol 27. IEEE; 2023. doi:10.1109/icra48891.2023.10160601'
apa: 'Zadok, D., Salzman, O., Wolf, A., & Bronstein, A. M. (2023). Towards predicting
fine finger motions from ultrasound images via kinematic representation. In 2023
IEEE International Conference on Robotics and Automation (Vol. 27). London,
United Kingdom: IEEE. https://doi.org/10.1109/icra48891.2023.10160601'
chicago: Zadok, Dean, Oren Salzman, Alon Wolf, and Alex M. Bronstein. “Towards Predicting
Fine Finger Motions from Ultrasound Images via Kinematic Representation.” In 2023
IEEE International Conference on Robotics and Automation, Vol. 27. IEEE, 2023.
https://doi.org/10.1109/icra48891.2023.10160601.
ieee: D. Zadok, O. Salzman, A. Wolf, and A. M. Bronstein, “Towards predicting fine
finger motions from ultrasound images via kinematic representation,” in 2023
IEEE International Conference on Robotics and Automation, London, United Kingdom,
2023, vol. 27.
ista: 'Zadok D, Salzman O, Wolf A, Bronstein AM. 2023. Towards predicting fine finger
motions from ultrasound images via kinematic representation. 2023 IEEE International
Conference on Robotics and Automation. ICRA: Conference on Robotics and Automation
vol. 27.'
mla: Zadok, Dean, et al. “Towards Predicting Fine Finger Motions from Ultrasound
Images via Kinematic Representation.” 2023 IEEE International Conference on
Robotics and Automation, vol. 27, IEEE, 2023, doi:10.1109/icra48891.2023.10160601.
short: D. Zadok, O. Salzman, A. Wolf, A.M. Bronstein, in:, 2023 IEEE International
Conference on Robotics and Automation, IEEE, 2023.
conference:
end_date: 2023-06-02
location: London, United Kingdom
name: 'ICRA: Conference on Robotics and Automation'
start_date: 2023-05-29
date_created: 2024-10-08T12:50:55Z
date_published: 2023-07-04T00:00:00Z
date_updated: 2024-10-09T12:00:32Z
day: '04'
doi: 10.1109/icra48891.2023.10160601
extern: '1'
external_id:
arxiv:
- '2202.05204'
intvolume: ' 27'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2202.05204
month: '07'
oa: 1
oa_version: Preprint
publication: 2023 IEEE International Conference on Robotics and Automation
publication_identifier:
eisbn:
- '9798350323658'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Towards predicting fine finger motions from ultrasound images via kinematic
representation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 27
year: '2023'
...