---
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'
fulldoi: https://doi.org/10.3390/math9172144
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'
fulldoi: https://doi.org/10.1016/j.patrec.2021.05.010
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'
fulldoi: https://doi.org/10.1073/pnas.2020620118
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'
fulldoi: https://doi.org/10.1145/3444943
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'
fulldoi: https://doi.org/10.3390/su13020717
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'
fulldoi: https://doi.org/10.1109/iccv48922.2021.00182
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'
fulldoi: https://doi.org/10.1109/cvpr46437.2021.01176
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'
fulldoi: https://doi.org/10.1109/mlsp52302.2021.9596168
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'
fulldoi: https://doi.org/10.1007/978-3-030-73018-5_2
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'
fulldoi: https://doi.org/10.1007/978-3-030-85896-4_4
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'
fulldoi: https://doi.org/10.1109/3dv50981.2020.00099
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'
fulldoi: https://doi.org/10.1007/s00605-021-01529-5
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'
fulldoi: https://doi.org/10.1137/20m1381824
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'
fulldoi: https://doi.org/10.1353/ajm.2021.0014
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'
...
---
_id: '10327'
abstract:
- lang: eng
text: Composite materials offer numerous advantages in a wide range of applications,
including thermoelectrics. Here, semiconductor–metal composites are produced by
just blending nanoparticles of a sulfide semiconductor obtained in aqueous solution
and at room temperature with a metallic Cu powder. The obtained blend is annealed
in a reducing atmosphere and afterward consolidated into dense polycrystalline
pellets through spark plasma sintering (SPS). We observe that, during the annealing
process, the presence of metallic copper activates a partial reduction of the
PbS, resulting in the formation of PbS–Pb–CuxS composites. The presence of metallic
lead during the SPS process habilitates the liquid-phase sintering of the composite.
Besides, by comparing the transport properties of PbS, the PbS–Pb–CuxS composites,
and PbS–CuxS composites obtained by blending PbS and CuxS nanoparticles, we demonstrate
that the presence of metallic lead decisively contributes to a strong increase
of the charge carrier concentration through spillover of charge carriers enabled
by the low work function of lead. The increase in charge carrier concentration
translates into much higher electrical conductivities and moderately lower Seebeck
coefficients. These properties translate into power factors up to 2.1 mW m–1 K–2
at ambient temperature, well above those of PbS and PbS + CuxS. Additionally,
the presence of multiple phases in the final composite results in a notable decrease
in the lattice thermal conductivity. Overall, the introduction of metallic copper
in the initial blend results in a significant improvement of the thermoelectric
performance of PbS, reaching a dimensionless thermoelectric figure of merit ZT
= 1.1 at 750 K, which represents about a 400% increase over bare PbS. Besides,
an average ZTave = 0.72 in the temperature range 320–773 K is demonstrated.
acknowledgement: This work was supported by the European Regional Development Funds.
M.L., Y.Z., X.H., and K.X. thank the China Scholarship Council for scholarship support.
M. I. has been financially supported by IST Austria and the Werner Siemens Foundation.
Y.L. acknowledges funding from the European Union’s Horizon 2020 research and innovation
program under the Marie Sklodowska-Curie grant agreement No. 754411. J.L. is a Serra
Húnter fellow and is grateful to ICREA Academia program and projects MICINN/FEDER
RTI2018-093996-B-C31 and GC 2017 SGR 128. ICN2 acknowledges funding from Generalitat
de Catalunya 2017 SGR 327 and the Spanish MINECO project NANOGEN (PID2020-116093RB-C43).
ICN2 was supported by the Severo Ochoa program from Spanish MINECO (grant no. SEV-2017-0706)
and was funded by the CERCA Programme/Generalitat de Catalunya. X.H. thanks China
Scholarship Council for scholarship support (201804910551). Part of the present
work was performed in the framework of Universitat Autònoma de Barcelona Materials
Science Ph.D. program.
article_processing_charge: No
article_type: original
author:
- first_name: Mengyao
full_name: Li, Mengyao
last_name: Li
- first_name: Yu
full_name: Liu, Yu
id: 2A70014E-F248-11E8-B48F-1D18A9856A87
last_name: Liu
orcid: 0000-0001-7313-6740
- first_name: Yu
full_name: Zhang, Yu
last_name: Zhang
- first_name: Xu
full_name: Han, Xu
last_name: Han
- first_name: Ke
full_name: Xiao, Ke
last_name: Xiao
- first_name: Mehran
full_name: Nabahat, Mehran
last_name: Nabahat
- first_name: Jordi
full_name: Arbiol, Jordi
last_name: Arbiol
- first_name: Jordi
full_name: Llorca, Jordi
last_name: Llorca
- first_name: Maria
full_name: Ibáñez, Maria
id: 43C61214-F248-11E8-B48F-1D18A9856A87
last_name: Ibáñez
orcid: 0000-0001-5013-2843
- first_name: Andreu
full_name: Cabot, Andreu
last_name: Cabot
citation:
ama: Li M, Liu Y, Zhang Y, et al. PbS–Pb–CuxS composites for thermoelectric application.
ACS Applied Materials and Interfaces. 2021;13(43):51373–51382. doi:10.1021/acsami.1c15609
apa: Li, M., Liu, Y., Zhang, Y., Han, X., Xiao, K., Nabahat, M., … Cabot, A. (2021).
PbS–Pb–CuxS composites for thermoelectric application. ACS Applied Materials
and Interfaces. American Chemical Society. https://doi.org/10.1021/acsami.1c15609
chicago: Li, Mengyao, Yu Liu, Yu Zhang, Xu Han, Ke Xiao, Mehran Nabahat, Jordi Arbiol,
Jordi Llorca, Maria Ibáñez, and Andreu Cabot. “PbS–Pb–CuxS Composites for Thermoelectric
Application.” ACS Applied Materials and Interfaces. American Chemical Society,
2021. https://doi.org/10.1021/acsami.1c15609.
ieee: M. Li et al., “PbS–Pb–CuxS composites for thermoelectric application,”
ACS Applied Materials and Interfaces, vol. 13, no. 43. American Chemical
Society, pp. 51373–51382, 2021.
ista: Li M, Liu Y, Zhang Y, Han X, Xiao K, Nabahat M, Arbiol J, Llorca J, Ibáñez
M, Cabot A. 2021. PbS–Pb–CuxS composites for thermoelectric application. ACS Applied
Materials and Interfaces. 13(43), 51373–51382.
mla: Li, Mengyao, et al. “PbS–Pb–CuxS Composites for Thermoelectric Application.”
ACS Applied Materials and Interfaces, vol. 13, no. 43, American Chemical
Society, 2021, pp. 51373–51382, doi:10.1021/acsami.1c15609.
short: M. Li, Y. Liu, Y. Zhang, X. Han, K. Xiao, M. Nabahat, J. Arbiol, J. Llorca,
M. Ibáñez, A. Cabot, ACS Applied Materials and Interfaces 13 (2021) 51373–51382.
corr_author: '1'
das_tickbox: '1'
date_created: 2021-11-21T23:01:30Z
date_published: 2021-10-19T00:00:00Z
date_updated: 2026-07-06T12:15:05Z
day: '19'
department:
- _id: MaIb
doi: 10.1021/acsami.1c15609
ec_funded: 1
external_id:
isi:
- '000715852100070'
pmid:
- '34665616'
fulldoi: https://doi.org/10.1021/acsami.1c15609
intvolume: ' 13'
isi: 1
issue: '43'
keyword:
- CuxS
- PbS
- energy conversion
- nanocomposite
- nanoparticle
- solution synthesis
- thermoelectric
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://upcommons.upc.edu/bitstream/2117/363528/1/Pb%20mengyao.pdf
month: '10'
oa: 1
oa_version: Submitted Version
page: 51373–51382
pmid: 1
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 9B8F7476-BA93-11EA-9121-9846C619BF3A
name: 'HighTE: The Werner Siemens Laboratory for the High Throughput Discovery of
Semiconductors for Waste Heat Recovery'
publication: ACS Applied Materials and Interfaces
publication_identifier:
eissn:
- 1944-8252
issn:
- 1944-8244
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: PbS–Pb–CuxS composites for thermoelectric application
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2021'
...
---
_id: '9235'
abstract:
- lang: eng
text: Cu2–xS has become one of the most promising thermoelectric materials for application
in the middle-high temperature range. Its advantages include the abundance, low
cost, and safety of its elements and a high performance at relatively elevated
temperatures. However, stability issues limit its operation current and temperature,
thus calling for the optimization of the material performance in the middle temperature
range. Here, we present a synthetic protocol for large scale production of covellite
CuS nanoparticles at ambient temperature and atmosphere, and using water as a
solvent. The crystal phase and stoichiometry of the particles are afterward tuned
through an annealing process at a moderate temperature under inert or reducing
atmosphere. While annealing under argon results in Cu1.8S nanopowder with a rhombohedral
crystal phase, annealing in an atmosphere containing hydrogen leads to tetragonal
Cu1.96S. High temperature X-ray diffraction analysis shows the material annealed
in argon to transform to the cubic phase at ca. 400 K, while the material annealed
in the presence of hydrogen undergoes two phase transitions, first to hexagonal
and then to the cubic structure. The annealing atmosphere, temperature, and time
allow adjustment of the density of copper vacancies and thus tuning of the charge
carrier concentration and material transport properties. In this direction, the
material annealed under Ar is characterized by higher electrical conductivities
but lower Seebeck coefficients than the material annealed in the presence of hydrogen.
By optimizing the charge carrier concentration through the annealing time, Cu2–xS
with record figures of merit in the middle temperature range, up to 1.41 at 710
K, is obtained. We finally demonstrate that this strategy, based on a low-cost
and scalable solution synthesis process, is also suitable for the production of
high performance Cu2–xS layers using high throughput and cost-effective printing
technologies.
acknowledgement: This work was supported by the European Regional Development Funds.
M.Y.L., X.H., T.Z., and K.X. thank the China Scholarship Council for scholarship
support. M.I. acknowledges financial support from IST Austria. J.L. acknowledges
support from the National Natural Science Foundation of China (No. 22008091), the
funding for scientific research startup of Jiangsu University (No. 19JDG044), and
Jiangsu Provincial Program for High-Level Innovative and Entrepreneurial Talents
Introduction. J.L. is a Serra Húnter fellow and is grateful to the ICREA Academia
program and projects MICINN/FEDER RTI2018-093996-B-C31 and GC 2017 SGR 128. ICN2
acknowledges funding from Generalitat de Catalunya 2017 SGR 327 and the Spanish
MINECO ENE2017-85087-C3. ICN2 is supported by the Severo Ochoa program from Spanish
MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat
de Catalunya. Part of the present work has been performed in the framework of Universitat
Autònoma de Barcelona Materials Science PhD program. T.Z. has received funding from
the CSC-UAB PhD scholarship program.
article_processing_charge: No
article_type: original
author:
- first_name: Mengyao
full_name: Li, Mengyao
last_name: Li
- first_name: Yu
full_name: Liu, Yu
id: 2A70014E-F248-11E8-B48F-1D18A9856A87
last_name: Liu
orcid: 0000-0001-7313-6740
- first_name: Yu
full_name: Zhang, Yu
last_name: Zhang
- first_name: Xu
full_name: Han, Xu
last_name: Han
- first_name: Ting
full_name: Zhang, Ting
last_name: Zhang
- first_name: Yong
full_name: Zuo, Yong
last_name: Zuo
- first_name: Chenyang
full_name: Xie, Chenyang
last_name: Xie
- first_name: Ke
full_name: Xiao, Ke
last_name: Xiao
- first_name: Jordi
full_name: Arbiol, Jordi
last_name: Arbiol
- first_name: Jordi
full_name: Llorca, Jordi
last_name: Llorca
- first_name: Maria
full_name: Ibáñez, Maria
id: 43C61214-F248-11E8-B48F-1D18A9856A87
last_name: Ibáñez
orcid: 0000-0001-5013-2843
- first_name: Junfeng
full_name: Liu, Junfeng
last_name: Liu
- first_name: Andreu
full_name: Cabot, Andreu
last_name: Cabot
citation:
ama: Li M, Liu Y, Zhang Y, et al. Effect of the annealing atmosphere on crystal
phase and thermoelectric properties of copper sulfide. ACS Nano. 2021;15(3):4967–4978.
doi:10.1021/acsnano.0c09866
apa: Li, M., Liu, Y., Zhang, Y., Han, X., Zhang, T., Zuo, Y., … Cabot, A. (2021).
Effect of the annealing atmosphere on crystal phase and thermoelectric properties
of copper sulfide. ACS Nano. American Chemical Society. https://doi.org/10.1021/acsnano.0c09866
chicago: Li, Mengyao, Yu Liu, Yu Zhang, Xu Han, Ting Zhang, Yong Zuo, Chenyang Xie,
et al. “Effect of the Annealing Atmosphere on Crystal Phase and Thermoelectric
Properties of Copper Sulfide.” ACS Nano. American Chemical Society, 2021.
https://doi.org/10.1021/acsnano.0c09866.
ieee: M. Li et al., “Effect of the annealing atmosphere on crystal phase
and thermoelectric properties of copper sulfide,” ACS Nano, vol. 15, no.
3. American Chemical Society, pp. 4967–4978, 2021.
ista: Li M, Liu Y, Zhang Y, Han X, Zhang T, Zuo Y, Xie C, Xiao K, Arbiol J, Llorca
J, Ibáñez M, Liu J, Cabot A. 2021. Effect of the annealing atmosphere on crystal
phase and thermoelectric properties of copper sulfide. ACS Nano. 15(3), 4967–4978.
mla: Li, Mengyao, et al. “Effect of the Annealing Atmosphere on Crystal Phase and
Thermoelectric Properties of Copper Sulfide.” ACS Nano, vol. 15, no. 3,
American Chemical Society, 2021, pp. 4967–4978, doi:10.1021/acsnano.0c09866.
short: M. Li, Y. Liu, Y. Zhang, X. Han, T. Zhang, Y. Zuo, C. Xie, K. Xiao, J. Arbiol,
J. Llorca, M. Ibáñez, J. Liu, A. Cabot, ACS Nano 15 (2021) 4967–4978.
corr_author: '1'
das_tickbox: '1'
date_created: 2021-03-10T20:12:45Z
date_published: 2021-03-01T00:00:00Z
date_updated: 2026-07-06T12:15:39Z
day: '01'
department:
- _id: MaIb
doi: 10.1021/acsnano.0c09866
external_id:
isi:
- '000634569100106'
pmid:
- '33645986'
fulldoi: https://doi.org/10.1021/acsnano.0c09866
intvolume: ' 15'
isi: 1
issue: '3'
keyword:
- General Engineering
- General Physics and Astronomy
- General Materials Science
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://upcommons.upc.edu/bitstream/handle/2117/363528/Pb%20mengyao.pdf?sequence=1&isAllowed=y
month: '03'
oa: 1
oa_version: Submitted Version
page: 4967–4978
pmid: 1
publication: ACS Nano
publication_identifier:
eissn:
- 1936-086X
issn:
- 1936-0851
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Effect of the annealing atmosphere on crystal phase and thermoelectric properties
of copper sulfide
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 15
year: '2021'
...
---
_id: '10134'
abstract:
- lang: eng
text: We investigate the effect of coupling between translational and internal degrees
of freedom of composite quantum particles on their localization in a random potential.
We show that entanglement between the two degrees of freedom weakens localization
due to the upper bound imposed on the inverse participation ratio by purity of
a quantum state. We perform numerical calculations for a two-particle system bound
by a harmonic force in a 1D disordered lattice and a rigid rotor in a 2D disordered
lattice. We illustrate that the coupling has a dramatic effect on localization
properties, even with a small number of internal states participating in quantum
dynamics.
acknowledgement: "We acknowledge helpful discussions with W. G. Unruh and A. Rodriguez.
F. S. is supported by European Union’s\r\nHorizon 2020 research and innovation programme
under the Marie Skłodowska-Curie Grant No. 754411. M. L. acknowledges support by
the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). W. H. Z.
is\r\nsupported by Department of Energy under the Los\r\nAlamos National Laboratory
LDRD Program as well as by the U.S. Department of Energy, Office of Science, Basic\r\nEnergy
Sciences, Materials Sciences and Engineering Division, Condensed Matter Theory Program.
R. V. K. is supported by NSERC of Canada.\r\n"
article_number: '160602'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Fumika
full_name: Suzuki, Fumika
id: 650C99FC-1079-11EA-A3C0-73AE3DDC885E
last_name: Suzuki
orcid: 0000-0003-4982-5970
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
- first_name: Wojciech H.
full_name: Zurek, Wojciech H.
last_name: Zurek
- first_name: Roman V.
full_name: Krems, Roman V.
last_name: Krems
citation:
ama: Suzuki F, Lemeshko M, Zurek WH, Krems RV. Anderson localization of composite
particles. Physical Review Letters. 2021;127(16). doi:10.1103/physrevlett.127.160602
apa: Suzuki, F., Lemeshko, M., Zurek, W. H., & Krems, R. V. (2021). Anderson
localization of composite particles. Physical Review Letters. American
Physical Society. https://doi.org/10.1103/physrevlett.127.160602
chicago: Suzuki, Fumika, Mikhail Lemeshko, Wojciech H. Zurek, and Roman V. Krems.
“Anderson Localization of Composite Particles.” Physical Review Letters.
American Physical Society, 2021. https://doi.org/10.1103/physrevlett.127.160602.
ieee: F. Suzuki, M. Lemeshko, W. H. Zurek, and R. V. Krems, “Anderson localization
of composite particles,” Physical Review Letters, vol. 127, no. 16. American
Physical Society, 2021.
ista: Suzuki F, Lemeshko M, Zurek WH, Krems RV. 2021. Anderson localization of composite
particles. Physical Review Letters. 127(16), 160602.
mla: Suzuki, Fumika, et al. “Anderson Localization of Composite Particles.” Physical
Review Letters, vol. 127, no. 16, 160602, American Physical Society, 2021,
doi:10.1103/physrevlett.127.160602.
short: F. Suzuki, M. Lemeshko, W.H. Zurek, R.V. Krems, Physical Review Letters 127
(2021).
corr_author: '1'
das_tickbox: '1'
date_created: 2021-10-13T09:21:33Z
date_published: 2021-10-12T00:00:00Z
date_updated: 2026-07-06T12:22:26Z
day: '12'
department:
- _id: MiLe
doi: 10.1103/physrevlett.127.160602
ec_funded: 1
external_id:
arxiv:
- '2011.06279'
isi:
- '000707495700001'
fulldoi: https://doi.org/10.1103/physrevlett.127.160602
intvolume: ' 127'
isi: 1
issue: '16'
keyword:
- General Physics and Astronomy
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2011.06279
month: '10'
oa: 1
oa_version: Preprint
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 2688CF98-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '801770'
name: 'Angulon: physics and applications of a new quasiparticle'
publication: Physical Review Letters
publication_identifier:
eissn:
- 1079-7114
issn:
- 0031-9007
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Anderson localization of composite particles
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 127
year: '2021'
...
---
_id: '8997'
abstract:
- lang: eng
text: Phenomenological relations such as Ohm’s or Fourier’s law have a venerable
history in physics but are still scarce in biology. This situation restrains predictive
theory. Here, we build on bacterial “growth laws,” which capture physiological
feedback between translation and cell growth, to construct a minimal biophysical
model for the combined action of ribosome-targeting antibiotics. Our model predicts
drug interactions like antagonism or synergy solely from responses to individual
drugs. We provide analytical results for limiting cases, which agree well with
numerical results. We systematically refine the model by including direct physical
interactions of different antibiotics on the ribosome. In a limiting case, our
model provides a mechanistic underpinning for recent predictions of higher-order
interactions that were derived using entropy maximization. We further refine the
model to include the effects of antibiotics that mimic starvation and the presence
of resistance genes. We describe the impact of a starvation-mimicking antibiotic
on drug interactions analytically and verify it experimentally. Our extended model
suggests a change in the type of drug interaction that depends on the strength
of resistance, which challenges established rescaling paradigms. We experimentally
show that the presence of unregulated resistance genes can lead to altered drug
interaction, which agrees with the prediction of the model. While minimal, the
model is readily adaptable and opens the door to predicting interactions of second
and higher-order in a broad range of biological systems.
acknowledgement: 'This work was supported in part by Tum stipend of Knafelj foundation
(to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and
P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation
(DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG)
Collaborative Research Centre (SFB) 1310 (to T.B.). '
article_number: e1008529
article_processing_charge: Yes
article_type: original
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic
action. PLOS Computational Biology. 2021;17. doi:10.1371/journal.pcbi.1008529
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2021). Minimal biophysical
model of combined antibiotic action. PLOS Computational Biology. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1008529
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical
Model of Combined Antibiotic Action.” PLOS Computational Biology. Public
Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008529.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of
combined antibiotic action,” PLOS Computational Biology, vol. 17. Public
Library of Science, 2021.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined
antibiotic action. PLOS Computational Biology. 17, e1008529.
mla: Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.”
PLOS Computational Biology, vol. 17, e1008529, Public Library of Science,
2021, doi:10.1371/journal.pcbi.1008529.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021).
date_created: 2021-01-08T07:16:18Z
date_published: 2021-01-07T00:00:00Z
date_updated: 2026-07-06T12:44:35Z
day: '07'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1008529
external_id:
isi:
- '000608045000010'
pmid:
- '33411759'
file:
- access_level: open_access
checksum: e29f2b42651bef8e034781de8781ffac
content_type: application/pdf
creator: dernst
date_created: 2021-02-04T12:30:48Z
date_updated: 2021-02-04T12:30:48Z
file_id: '9092'
file_name: 2021_PlosComBio_Kavcic.pdf
file_size: 3690053
relation: main_file
success: 1
file_date_updated: 2021-02-04T12:30:48Z
fulldoi: https://doi.org/10.1371/journal.pcbi.1008529
has_accepted_license: '1'
intvolume: ' 17'
isi: 1
keyword:
- Modelling and Simulation
- Genetics
- Molecular Biology
- Antibiotics
- Drug interactions
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: PLOS Computational Biology
publication_identifier:
issn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
record:
- id: '8930'
relation: research_data
status: public
- id: '7673'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Minimal biophysical model of combined antibiotic action
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: 17
year: '2021'
...
---
_id: '9082'
abstract:
- lang: eng
text: Acquired mutations are sufficiently frequent such that the genome of a single
cell offers a record of its history of cell divisions. Among more common somatic
genomic alterations are loss of heterozygosity (LOH). Large LOH events are potentially
detectable in single cell RNA sequencing (scRNA-seq) datasets as tracts of monoallelic
expression for constitutionally heterozygous single nucleotide variants (SNVs)
located among contiguous genes. We identified runs of monoallelic expression,
consistent with LOH, uniquely distributed throughout the genome in single cell
brain cortex transcriptomes of F1 hybrids involving different inbred mouse strains.
We then phylogenetically reconstructed single cell lineages and simultaneously
identified cell types by corresponding gene expression patterns. Our results are
consistent with progenitor cells giving rise to multiple cortical cell types through
stereotyped expansion and distinct waves of neurogenesis. Compared to engineered
recording systems, LOH events accumulate throughout the genome and across the
lifetime of an organism, affording tremendous capacity for encoding lineage information
and increasing resolution for later cell divisions. This approach can conceivably
be computationally incorporated into scRNA-seq analysis and may be useful for
organisms where genetic engineering is prohibitive, such as humans.
acknowledgement: "We thank Bill Bolosky, Microsoft Research, for earlier work showing
proof of concept in TCGA\r\nbulk RNA-seq data. Supported by the Paul G. Allen Frontiers
Group (University of Washington);\r\nNIH R00HG010152 (Dartmouth); and NÖ Forschung
und Bildung n[f+b] life science call grant\r\n(C13-002) to SH, and the European
Research Council (ERC) under the European Union’s\r\nHorizon 2020 research and innovation
program 725780 LinPro to SH."
article_processing_charge: No
author:
- first_name: Donovan J.
full_name: Anderson, Donovan J.
last_name: Anderson
- first_name: Florian
full_name: Pauler, Florian
id: 48EA0138-F248-11E8-B48F-1D18A9856A87
last_name: Pauler
orcid: 0000-0002-7462-0048
- first_name: Aaron
full_name: McKenna, Aaron
last_name: McKenna
- first_name: Jay
full_name: Shendure, Jay
last_name: Shendure
- first_name: Simon
full_name: Hippenmeyer, Simon
id: 37B36620-F248-11E8-B48F-1D18A9856A87
last_name: Hippenmeyer
orcid: 0000-0003-2279-1061
- first_name: Marshall S.
full_name: Horwitz, Marshall S.
last_name: Horwitz
citation:
ama: Anderson DJ, Pauler F, McKenna A, Shendure J, Hippenmeyer S, Horwitz MS. Simultaneous
identification of brain cell type and lineage via single cell RNA sequencing.
bioRxiv. doi:10.1101/2020.12.31.425016
apa: Anderson, D. J., Pauler, F., McKenna, A., Shendure, J., Hippenmeyer, S., &
Horwitz, M. S. (n.d.). Simultaneous identification of brain cell type and lineage
via single cell RNA sequencing. bioRxiv. https://doi.org/10.1101/2020.12.31.425016
chicago: Anderson, Donovan J., Florian Pauler, Aaron McKenna, Jay Shendure, Simon
Hippenmeyer, and Marshall S. Horwitz. “Simultaneous Identification of Brain Cell
Type and Lineage via Single Cell RNA Sequencing.” BioRxiv, n.d. https://doi.org/10.1101/2020.12.31.425016.
ieee: D. J. Anderson, F. Pauler, A. McKenna, J. Shendure, S. Hippenmeyer, and M.
S. Horwitz, “Simultaneous identification of brain cell type and lineage via single
cell RNA sequencing,” bioRxiv. .
ista: Anderson DJ, Pauler F, McKenna A, Shendure J, Hippenmeyer S, Horwitz MS. Simultaneous
identification of brain cell type and lineage via single cell RNA sequencing.
bioRxiv, 10.1101/2020.12.31.425016.
mla: Anderson, Donovan J., et al. “Simultaneous Identification of Brain Cell Type
and Lineage via Single Cell RNA Sequencing.” BioRxiv, doi:10.1101/2020.12.31.425016.
short: D.J. Anderson, F. Pauler, A. McKenna, J. Shendure, S. Hippenmeyer, M.S. Horwitz,
BioRxiv (n.d.).
das_tickbox: '1'
date_created: 2021-02-04T07:23:23Z
date_published: 2021-01-01T00:00:00Z
date_updated: 2026-07-06T12:46:11Z
day: '01'
department:
- _id: SiHi
doi: 10.1101/2020.12.31.425016
ec_funded: 1
fulldoi: https://doi.org/10.1101/2020.12.31.425016
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2020.12.31.425016
month: '01'
oa: 1
oa_version: Preprint
project:
- _id: 260018B0-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '725780'
name: Principles of Neural Stem Cell Lineage Progression in Cerebral Cortex Development
publication: bioRxiv
publication_status: submitted
status: public
title: Simultaneous identification of brain cell type and lineage via single cell
RNA sequencing
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10290'
abstract:
- lang: eng
text: A precise quantitative description of the ultrastructural characteristics
underlying biological mechanisms is often key to their understanding. This is
particularly true for dynamic extra- and intracellular filamentous assemblies,
playing a role in cell motility, cell integrity, cytokinesis, tissue formation
and maintenance. For example, genetic manipulation or modulation of actin regulatory
proteins frequently manifests in changes of the morphology, dynamics, and ultrastructural
architecture of actin filament-rich cell peripheral structures, such as lamellipodia
or filopodia. However, the observed ultrastructural effects often remain subtle
and require sufficiently large datasets for appropriate quantitative analysis.
The acquisition of such large datasets has been enabled by recent advances in
high-throughput cryo-electron tomography (cryo-ET) methods. This also necessitates
the development of complementary approaches to maximize the extraction of relevant
biological information. We have developed a computational toolbox for the semi-automatic
quantification of segmented and vectorized filamentous networks from pre-processed
cryo-electron tomograms, facilitating the analysis and cross-comparison of multiple
experimental conditions. GUI-based components simplify the processing of data
and allow users to obtain a large number of ultrastructural parameters describing
filamentous assemblies. We demonstrate the feasibility of this workflow by analyzing
cryo-ET data of untreated and chemically perturbed branched actin filament networks
and that of parallel actin filament arrays. In principle, the computational toolbox
presented here is applicable for data analysis comprising any type of filaments
in regular (i.e. parallel) or random arrangement. We show that it can ease the
identification of key differences between experimental groups and facilitate the
in-depth analysis of ultrastructural data in a time-efficient manner.
acknowledged_ssus:
- _id: ScienComp
- _id: LifeSc
- _id: Bio
- _id: EM-Fac
acknowledgement: 'This research was supported by the Scientific Service Units (SSUs)
of IST Austria through resources provided by Scientific Computing (SciComp), the
Life Science Facility (LSF), the BioImaging Facility (BIF), and the Electron Microscopy
Facility (EMF). We also thank Victor-Valentin Hodirnau for help with cryo-ET data
acquisition. The authors acknowledge support from IST Austria and from the Austrian
Science Fund (FWF): M02495 to G.D. and Austrian Science Fund (FWF): P33367 to F.K.M.S.'
article_number: '107808'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Georgi A
full_name: Dimchev, Georgi A
id: 38C393BE-F248-11E8-B48F-1D18A9856A87
last_name: Dimchev
orcid: 0000-0001-8370-6161
- first_name: Behnam
full_name: Amiri, Behnam
last_name: Amiri
- first_name: Florian
full_name: Fäßler, Florian
id: 404F5528-F248-11E8-B48F-1D18A9856A87
last_name: Fäßler
orcid: 0000-0001-7149-769X
- first_name: Martin
full_name: Falcke, Martin
last_name: Falcke
- first_name: Florian KM
full_name: Schur, Florian KM
id: 48AD8942-F248-11E8-B48F-1D18A9856A87
last_name: Schur
orcid: 0000-0003-4790-8078
citation:
ama: Dimchev GA, Amiri B, Fäßler F, Falcke M, Schur FK. Computational toolbox for
ultrastructural quantitative analysis of filament networks in cryo-ET data. Journal
of Structural Biology. 2021;213(4). doi:10.1016/j.jsb.2021.107808
apa: Dimchev, G. A., Amiri, B., Fäßler, F., Falcke, M., & Schur, F. K. (2021).
Computational toolbox for ultrastructural quantitative analysis of filament networks
in cryo-ET data. Journal of Structural Biology. Elsevier. https://doi.org/10.1016/j.jsb.2021.107808
chicago: Dimchev, Georgi A, Behnam Amiri, Florian Fäßler, Martin Falcke, and Florian
KM Schur. “Computational Toolbox for Ultrastructural Quantitative Analysis of
Filament Networks in Cryo-ET Data.” Journal of Structural Biology. Elsevier,
2021. https://doi.org/10.1016/j.jsb.2021.107808.
ieee: G. A. Dimchev, B. Amiri, F. Fäßler, M. Falcke, and F. K. Schur, “Computational
toolbox for ultrastructural quantitative analysis of filament networks in cryo-ET
data,” Journal of Structural Biology, vol. 213, no. 4. Elsevier, 2021.
ista: Dimchev GA, Amiri B, Fäßler F, Falcke M, Schur FK. 2021. Computational toolbox
for ultrastructural quantitative analysis of filament networks in cryo-ET data.
Journal of Structural Biology. 213(4), 107808.
mla: Dimchev, Georgi A., et al. “Computational Toolbox for Ultrastructural Quantitative
Analysis of Filament Networks in Cryo-ET Data.” Journal of Structural Biology,
vol. 213, no. 4, 107808, Elsevier, 2021, doi:10.1016/j.jsb.2021.107808.
short: G.A. Dimchev, B. Amiri, F. Fäßler, M. Falcke, F.K. Schur, Journal of Structural
Biology 213 (2021).
corr_author: '1'
das_tickbox: '1'
date_created: 2021-11-15T12:21:42Z
date_published: 2021-11-03T00:00:00Z
date_updated: 2026-07-06T12:57:43Z
day: '03'
ddc:
- '572'
department:
- _id: FlSc
doi: 10.1016/j.jsb.2021.107808
external_id:
isi:
- '000720259500002'
file:
- access_level: open_access
checksum: 6b209e4d44775d4e02b50f78982c15fa
content_type: application/pdf
creator: cchlebak
date_created: 2021-11-15T13:11:27Z
date_updated: 2021-11-15T13:11:27Z
file_id: '10291'
file_name: 2021_JournalStructBiol_Dimchev.pdf
file_size: 16818304
relation: main_file
success: 1
file_date_updated: 2021-11-15T13:11:27Z
fulldoi: https://doi.org/10.1016/j.jsb.2021.107808
has_accepted_license: '1'
intvolume: ' 213'
isi: 1
issue: '4'
keyword:
- Structural Biology
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 9B954C5C-BA93-11EA-9121-9846C619BF3A
grant_number: P33367
name: Structure and isoform diversity of the Arp2/3 complex
- _id: 2674F658-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: M02495
name: Protein structure and function in filopodia across scales
publication: Journal of Structural Biology
publication_identifier:
issn:
- 1047-8477
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
record:
- id: '14502'
relation: software
status: public
scopus_import: '1'
status: public
title: Computational toolbox for ultrastructural quantitative analysis of filament
networks in cryo-ET 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: 213
year: '2021'
...