---
OA_type: closed access
_id: '18245'
abstract:
- lang: eng
text: Intel® RealSense™ SR300 is a depth camera capable of providing a VGA-size
depth map at 60 fps and 0.125mm depth resolution. In addition, it outputs an infrared
VGA-resolution image and a 1080p color texture image at 30 fps. SR300 form-factor
enables it to be integrated into small consumer products and as a front facing
camera in laptops and Ultrabooks™. The SR300 depth camera is based on a coded-light
technology where triangulation between projected patterns and images captured
by a dedicated sensor is used to produce the depth map. Each projected line is
coded by a special temporal optical code, that enables a dense depth map reconstruction
from its reflection. The solid mechanical assembly of the camera allows it to
stay calibrated throughout temperature and pressure changes, drops, and hits.
In addition, active dynamic control maintains a calibrated depth output. An extended
API LibRS released with the camera allows developers to integrate the camera in
various applications. Algorithms for 3D scanning, facial analysis, hand gesture
recognition, and tracking are within reach for applications using the SR300. In
this paper, we describe the underlying technology, hardware, and algorithms of
the SR300, as well as its calibration procedure, and outline some use cases. We
believe that this paper will provide a full case study of a mass-produced depth
sensing product and technology.
article_processing_charge: No
article_type: original
author:
- first_name: Aviad
full_name: Zabatani, Aviad
last_name: Zabatani
- first_name: Vitaly
full_name: Surazhsky, Vitaly
last_name: Surazhsky
- first_name: Erez
full_name: Sperling, Erez
last_name: Sperling
- first_name: Sagi Ben
full_name: Moshe, Sagi Ben
last_name: Moshe
- first_name: Ohad
full_name: Menashe, Ohad
last_name: Menashe
- first_name: David H.
full_name: Silver, David H.
last_name: Silver
- first_name: Zachi
full_name: Karni, Zachi
last_name: Karni
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Michael K
full_name: Bronstein, Michael K
last_name: Bronstein
- first_name: Ron
full_name: Kimmel, Ron
last_name: Kimmel
citation:
ama: Zabatani A, Surazhsky V, Sperling E, et al. Intel® RealSenseTM SR300
coded light depth camera. IEEE Transactions on Pattern Analysis and Machine
Intelligence. 2020;42(10):2333-2345. doi:10.1109/tpami.2019.2915841
apa: Zabatani, A., Surazhsky, V., Sperling, E., Moshe, S. B., Menashe, O., Silver,
D. H., … Kimmel, R. (2020). Intel® RealSenseTM SR300 coded light depth
camera. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tpami.2019.2915841
chicago: Zabatani, Aviad, Vitaly Surazhsky, Erez Sperling, Sagi Ben Moshe, Ohad
Menashe, David H. Silver, Zachi Karni, Alex M. Bronstein, Michael K Bronstein,
and Ron Kimmel. “Intel® RealSenseTM SR300 Coded Light Depth Camera.”
IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute
of Electrical and Electronics Engineers, 2020. https://doi.org/10.1109/tpami.2019.2915841.
ieee: A. Zabatani et al., “Intel® RealSenseTM SR300 coded light
depth camera,” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 42, no. 10. Institute of Electrical and Electronics Engineers, pp. 2333–2345,
2020.
ista: Zabatani A, Surazhsky V, Sperling E, Moshe SB, Menashe O, Silver DH, Karni
Z, Bronstein AM, Bronstein MK, Kimmel R. 2020. Intel® RealSenseTM SR300
coded light depth camera. IEEE Transactions on Pattern Analysis and Machine Intelligence.
42(10), 2333–2345.
mla: Zabatani, Aviad, et al. “Intel® RealSenseTM SR300 Coded Light Depth
Camera.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 42, no. 10, Institute of Electrical and Electronics Engineers, 2020, pp.
2333–45, doi:10.1109/tpami.2019.2915841.
short: A. Zabatani, V. Surazhsky, E. Sperling, S.B. Moshe, O. Menashe, D.H. Silver,
Z. Karni, A.M. Bronstein, M.K. Bronstein, R. Kimmel, IEEE Transactions on Pattern
Analysis and Machine Intelligence 42 (2020) 2333–2345.
date_created: 2024-10-08T13:04:18Z
date_published: 2020-10-01T00:00:00Z
date_updated: 2024-10-15T09:40:01Z
day: '01'
doi: 10.1109/tpami.2019.2915841
extern: '1'
external_id:
pmid:
- '31094683'
intvolume: ' 42'
issue: '10'
language:
- iso: eng
month: '10'
oa_version: None
page: 2333-2345
pmid: 1
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
eissn:
- 1939-3539
issn:
- 0162-8828
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Intel® RealSense™ SR300 coded light depth camera
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 42
year: '2020'
...
---
OA_type: closed access
_id: '18246'
abstract:
- lang: eng
text: Adversarial attacks have demonstrated remarkable efficacy in altering the
output of a learning model by applying a minimal perturbation to the input data.
While increasing attention has been placed on the image domain, however, the study
of adversarial perturbations for geometric data has been notably lagging behind.
In this paper, we show that effective adversarial attacks can be concocted for
surfaces embedded in 3D, under weak smoothness assumptions on the perceptibility
of the attack. We address the case of deformable 3D shapes in particular, and
introduce a general model that is not tailored to any specific surface representation,
nor does it assume access to a parametric description of the 3D object. In this
context, we consider targeted and untargeted variants of the attack, demonstrating
compelling results in either case. We further show how discovering adversarial
examples, and then using them for adversarial training, leads to an increase in
both robustness and accuracy. Our findings are confirmed empirically over multiple
datasets spanning different semantic classes and deformations.
article_processing_charge: No
article_type: original
author:
- first_name: G.
full_name: Mariani, G.
last_name: Mariani
- first_name: L.
full_name: Cosmo, L.
last_name: Cosmo
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: E.
full_name: Rodolà, E.
last_name: Rodolà
citation:
ama: Mariani G, Cosmo L, Bronstein AM, Rodolà E. Generating adversarial surfaces
via band‐limited perturbations. Computer Graphics Forum. 2020;39(5):253-264.
doi:10.1111/cgf.14083
apa: Mariani, G., Cosmo, L., Bronstein, A. M., & Rodolà, E. (2020). Generating
adversarial surfaces via band‐limited perturbations. Computer Graphics Forum.
Wiley. https://doi.org/10.1111/cgf.14083
chicago: Mariani, G., L. Cosmo, Alex M. Bronstein, and E. Rodolà. “Generating Adversarial
Surfaces via Band‐limited Perturbations.” Computer Graphics Forum. Wiley,
2020. https://doi.org/10.1111/cgf.14083.
ieee: G. Mariani, L. Cosmo, A. M. Bronstein, and E. Rodolà, “Generating adversarial
surfaces via band‐limited perturbations,” Computer Graphics Forum, vol.
39, no. 5. Wiley, pp. 253–264, 2020.
ista: Mariani G, Cosmo L, Bronstein AM, Rodolà E. 2020. Generating adversarial surfaces
via band‐limited perturbations. Computer Graphics Forum. 39(5), 253–264.
mla: Mariani, G., et al. “Generating Adversarial Surfaces via Band‐limited Perturbations.”
Computer Graphics Forum, vol. 39, no. 5, Wiley, 2020, pp. 253–64, doi:10.1111/cgf.14083.
short: G. Mariani, L. Cosmo, A.M. Bronstein, E. Rodolà, Computer Graphics Forum
39 (2020) 253–264.
date_created: 2024-10-08T13:04:35Z
date_published: 2020-08-01T00:00:00Z
date_updated: 2024-10-15T09:36:46Z
day: '01'
doi: 10.1111/cgf.14083
extern: '1'
intvolume: ' 39'
issue: '5'
language:
- iso: eng
month: '08'
oa_version: None
page: 253-264
publication: Computer Graphics Forum
publication_identifier:
eissn:
- 1467-8659
issn:
- 0167-7055
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Generating adversarial surfaces via band‐limited perturbations
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2020'
...
---
_id: '18247'
abstract:
- lang: eng
text: Convolutional neural networks (CNNs) achieve state-of-the-art accuracy in
a variety of tasks in computer vision and beyond. One of the major obstacles hindering
the ubiquitous use of CNNs for inference on low-power edge devices is their high
computational complexity and memory bandwidth requirements. The latter often dominates
the energy footprint on modern hardware. In this paper, we introduce a lossy transform
coding approach, inspired by image and video compression, designed to reduce the
memory bandwidth due to the storage of intermediate activation calculation results.
Our method does not require fine-tuning the network weights and halves the data
transfer volumes to the main memory by compressing feature maps, which are highly
correlated, with variable length coding. Our method outperform previous approach
in term of the number of bits per value with minor accuracy degradation on ResNet-34
and MobileNetV2. We analyze the performance of our approach on a variety of CNN
architectures and demonstrate that FPGA implementation of ResNet-18 with our approach
results in a reduction of around 40% in the memory energy footprint, compared
to quantized network, with negligible impact on accuracy. When allowing accuracy
degradation of up to 2%, the reduction of 60% is achieved. A reference implementation
accompanies the paper.
article_number: '9206968'
article_processing_charge: No
arxiv: 1
author:
- first_name: Brian
full_name: Chmiel, Brian
last_name: Chmiel
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Evgenii
full_name: Zheltonozhskii, Evgenii
last_name: Zheltonozhskii
- first_name: Ron
full_name: Banner, Ron
last_name: Banner
- first_name: Yevgeny
full_name: Yermolin, Yevgeny
last_name: Yermolin
- first_name: Alex
full_name: Karbachevsky, Alex
last_name: Karbachevsky
- 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: 'Chmiel B, Baskin C, Zheltonozhskii E, et al. Feature map transform coding
for energy-efficient CNN inference. In: 2020 International Joint Conference
on Neural Networks (IJCNN). IEEE; 2020. doi:10.1109/ijcnn48605.2020.9206968'
apa: 'Chmiel, B., Baskin, C., Zheltonozhskii, E., Banner, R., Yermolin, Y., Karbachevsky,
A., … Mendelson, A. (2020). Feature map transform coding for energy-efficient
CNN inference. In 2020 International Joint Conference on Neural Networks (IJCNN).
Glasgow, United Kingdom: IEEE. https://doi.org/10.1109/ijcnn48605.2020.9206968'
chicago: Chmiel, Brian, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Yevgeny
Yermolin, Alex Karbachevsky, Alex M. Bronstein, and Avi Mendelson. “Feature Map
Transform Coding for Energy-Efficient CNN Inference.” In 2020 International
Joint Conference on Neural Networks (IJCNN). IEEE, 2020. https://doi.org/10.1109/ijcnn48605.2020.9206968.
ieee: B. Chmiel et al., “Feature map transform coding for energy-efficient
CNN inference,” in 2020 International Joint Conference on Neural Networks (IJCNN),
Glasgow, United Kingdom, 2020.
ista: Chmiel B, Baskin C, Zheltonozhskii E, Banner R, Yermolin Y, Karbachevsky A,
Bronstein AM, Mendelson A. 2020. Feature map transform coding for energy-efficient
CNN inference. 2020 International Joint Conference on Neural Networks (IJCNN).
International Joint Conference on Neural Networks, 9206968.
mla: Chmiel, Brian, et al. “Feature Map Transform Coding for Energy-Efficient CNN
Inference.” 2020 International Joint Conference on Neural Networks (IJCNN),
9206968, IEEE, 2020, doi:10.1109/ijcnn48605.2020.9206968.
short: B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, Y. Yermolin, A. Karbachevsky,
A.M. Bronstein, A. Mendelson, in:, 2020 International Joint Conference on Neural
Networks (IJCNN), IEEE, 2020.
conference:
end_date: 2020-07-24
location: Glasgow, United Kingdom
name: International Joint Conference on Neural Networks
start_date: 2020-07-19
date_created: 2024-10-08T13:04:52Z
date_published: 2020-09-28T00:00:00Z
date_updated: 2024-12-12T10:04:54Z
day: '28'
doi: 10.1109/ijcnn48605.2020.9206968
extern: '1'
external_id:
arxiv:
- '1905.10830'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1905.10830
month: '09'
oa: 1
oa_version: Preprint
publication: 2020 International Joint Conference on Neural Networks (IJCNN)
publication_identifier:
eissn:
- 2161-4407
isbn:
- '9781728169279'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Feature map transform coding for energy-efficient CNN inference
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18248'
abstract:
- lang: eng
text: Learning an object detection or retrieval system requires a large data set
with manual annotations. Such data are expensive and time-consuming to create
and therefore difficult to obtain on a large scale. In this work, we propose using
the natural correlation in narrations and the visual presence of objects in video
to learn an object detector and retriever without any manual labeling involved.
We pose the problem as weakly supervised learning with noisy labels, and propose
a novel object detection and retrieval paradigm under these constraints. We handle
the background rejection by using contrastive samples and confront the high level
of label noise with a new clustering score. Our evaluation is based on a set of
ten objects with manual ground truth annotation in almost 5000 frames extracted
from instructional videos from the web. We demonstrate superior results compared
to state-of-the-art weakly- supervised approaches and report a strongly-labeled
upper bound as well. While the focus of the paper is object detection and retrieval,
the proposed methodology can be applied to a broader range of noisy weakly-supervised
problems.
article_number: '9150938'
article_processing_charge: No
author:
- first_name: Elad
full_name: Amrani, Elad
last_name: Amrani
- first_name: Rami
full_name: Ben-Ari, Rami
last_name: Ben-Ari
- first_name: Inbar
full_name: Shapira, Inbar
last_name: Shapira
- first_name: Tal
full_name: Hakim, Tal
last_name: Hakim
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Amrani E, Ben-Ari R, Shapira I, Hakim T, Bronstein AM. Self-supervised object
detection and retrieval using unlabeled videos. In: 2020 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE; 2020.
doi:10.1109/cvprw50498.2020.00485'
apa: 'Amrani, E., Ben-Ari, R., Shapira, I., Hakim, T., & Bronstein, A. M. (2020).
Self-supervised object detection and retrieval using unlabeled videos. In 2020
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
Seattle, WA, United States: IEEE. https://doi.org/10.1109/cvprw50498.2020.00485'
chicago: Amrani, Elad, Rami Ben-Ari, Inbar Shapira, Tal Hakim, and Alex M. Bronstein.
“Self-Supervised Object Detection and Retrieval Using Unlabeled Videos.” In 2020
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
IEEE, 2020. https://doi.org/10.1109/cvprw50498.2020.00485.
ieee: E. Amrani, R. Ben-Ari, I. Shapira, T. Hakim, and A. M. Bronstein, “Self-supervised
object detection and retrieval using unlabeled videos,” in 2020 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA,
United States, 2020.
ista: Amrani E, Ben-Ari R, Shapira I, Hakim T, Bronstein AM. 2020. Self-supervised
object detection and retrieval using unlabeled videos. 2020 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops, 9150938.
mla: Amrani, Elad, et al. “Self-Supervised Object Detection and Retrieval Using
Unlabeled Videos.” 2020 IEEE/CVF Conference on Computer Vision and Pattern
Recognition Workshops (CVPRW), 9150938, IEEE, 2020, doi:10.1109/cvprw50498.2020.00485.
short: E. Amrani, R. Ben-Ari, I. Shapira, T. Hakim, A.M. Bronstein, in:, 2020 IEEE/CVF
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE,
2020.
conference:
end_date: 2020-06-19
location: Seattle, WA, United States
name: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
start_date: 2020-06-14
date_created: 2024-10-08T13:05:08Z
date_published: 2020-07-28T00:00:00Z
date_updated: 2024-12-12T09:59:41Z
day: '28'
doi: 10.1109/cvprw50498.2020.00485
extern: '1'
language:
- iso: eng
month: '07'
oa_version: None
publication: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
(CVPRW)
publication_identifier:
eissn:
- 2160-7516
isbn:
- '9781728193618'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Self-supervised object detection and retrieval using unlabeled videos
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18249'
abstract:
- lang: eng
text: Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality
for soft tissues. The long acquisition times, however, make it more prone to motion
artifacts as well as contribute to the relative high costs of this examination.
Over the years, multiple studies concentrated on designing reduced measurement
schemes and image reconstruction schemes for MRI, 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 with a constrained time budget. Inspired by these
successes, in this work, we propose to learn accelerated MR acquisition schemes
(in the form of Cartesian trajectories) jointly with the image reconstruction
operator. 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 the learned Cartesian trajectories at different speed up rates. Code available
at https://github.com/tomer196/fastMRI-Cartesian.
article_number: '9054542'
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: Michael
full_name: Zibulevsky, Michael
last_name: Zibulevsky
- 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, Zibulevsky M, Bronstein AM. Joint
learning of cartesian undersampling and reconstruction for accelerated MRI. In:
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP). IEEE; 2020. doi:10.1109/icassp40776.2020.9054542'
apa: 'Weiss, T., Vedula, S., Senouf, O., Michailovich, O., Zibulevsky, M., &
Bronstein, A. M. (2020). Joint learning of cartesian undersampling and reconstruction
for accelerated MRI. In ICASSP 2020 - 2020 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE.
https://doi.org/10.1109/icassp40776.2020.9054542'
chicago: Weiss, Tomer, Sanketh Vedula, Ortal Senouf, Oleg Michailovich, Michael
Zibulevsky, and Alex M. Bronstein. “Joint Learning of Cartesian Undersampling
and Reconstruction for Accelerated MRI.” In ICASSP 2020 - 2020 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.
https://doi.org/10.1109/icassp40776.2020.9054542.
ieee: T. Weiss, S. Vedula, O. Senouf, O. Michailovich, M. Zibulevsky, and A. M.
Bronstein, “Joint learning of cartesian undersampling and reconstruction for accelerated
MRI,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), Barcelona, Spain, 2020.
ista: Weiss T, Vedula S, Senouf O, Michailovich O, Zibulevsky M, Bronstein AM. 2020.
Joint learning of cartesian undersampling and reconstruction for accelerated MRI.
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP). IEEE International Conference on Acoustics, Speech, and Signal
Processing, 9054542.
mla: Weiss, Tomer, et al. “Joint Learning of Cartesian Undersampling and Reconstruction
for Accelerated MRI.” ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 9054542, IEEE, 2020, doi:10.1109/icassp40776.2020.9054542.
short: T. Weiss, S. Vedula, O. Senouf, O. Michailovich, M. Zibulevsky, A.M. Bronstein,
in:, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), IEEE, 2020.
conference:
end_date: 2020-05-08
location: Barcelona, Spain
name: IEEE International Conference on Acoustics, Speech, and Signal Processing
start_date: 2020-05-04
date_created: 2024-10-08T13:05:24Z
date_published: 2020-04-09T00:00:00Z
date_updated: 2024-12-11T16:06:20Z
day: '09'
doi: 10.1109/icassp40776.2020.9054542
extern: '1'
external_id:
arxiv:
- '1905.09324'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1905.09324
month: '04'
oa: 1
oa_version: Preprint
publication: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)
publication_identifier:
eissn:
- 2379-190X
isbn:
- '9781509066322'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Joint learning of cartesian undersampling and reconstruction for accelerated
MRI
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
OA_place: repository
OA_type: green
_id: '18250'
abstract:
- lang: eng
text: Many shape analysis methods treat the geometry of an object as a metric space
that can be captured by the Laplace-Beltrami operator. In this paper, we propose
to adapt the classical Hamiltonian operator from quantum mechanics to the field
of shape analysis. To this end, we study the addition of a potential function
to the Laplacian as a generator for dual spaces in which shape processing is performed.
We present general optimization approaches for solving variational problems involving
the basis defined by the Hamiltonian using perturbation theory for its eigenvectors.
The suggested operator is shown to produce better functional spaces to operate
with, as demonstrated on different shape analysis tasks.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yoni
full_name: Choukroun, Yoni
last_name: Choukroun
- first_name: Alon
full_name: Shtern, Alon
last_name: Shtern
- 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: Choukroun Y, Shtern A, Bronstein AM, Kimmel R. Hamiltonian operator for spectral
shape analysis. IEEE Transactions on Visualization and Computer Graphics.
2020;26(2):1320-1331. doi:10.1109/tvcg.2018.2867513
apa: Choukroun, Y., Shtern, A., Bronstein, A. M., & Kimmel, R. (2020). Hamiltonian
operator for spectral shape analysis. IEEE Transactions on Visualization and
Computer Graphics. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tvcg.2018.2867513
chicago: Choukroun, Yoni, Alon Shtern, Alex M. Bronstein, and Ron Kimmel. “Hamiltonian
Operator for Spectral Shape Analysis.” IEEE Transactions on Visualization and
Computer Graphics. Institute of Electrical and Electronics Engineers, 2020.
https://doi.org/10.1109/tvcg.2018.2867513.
ieee: Y. Choukroun, A. Shtern, A. M. Bronstein, and R. Kimmel, “Hamiltonian operator
for spectral shape analysis,” IEEE Transactions on Visualization and Computer
Graphics, vol. 26, no. 2. Institute of Electrical and Electronics Engineers,
pp. 1320–1331, 2020.
ista: Choukroun Y, Shtern A, Bronstein AM, Kimmel R. 2020. Hamiltonian operator
for spectral shape analysis. IEEE Transactions on Visualization and Computer Graphics.
26(2), 1320–1331.
mla: Choukroun, Yoni, et al. “Hamiltonian Operator for Spectral Shape Analysis.”
IEEE Transactions on Visualization and Computer Graphics, vol. 26, no.
2, Institute of Electrical and Electronics Engineers, 2020, pp. 1320–31, doi:10.1109/tvcg.2018.2867513.
short: Y. Choukroun, A. Shtern, A.M. Bronstein, R. Kimmel, IEEE Transactions on
Visualization and Computer Graphics 26 (2020) 1320–1331.
date_created: 2024-10-08T13:05:41Z
date_published: 2020-02-01T00:00:00Z
date_updated: 2024-10-15T09:43:31Z
day: '01'
doi: 10.1109/tvcg.2018.2867513
extern: '1'
external_id:
arxiv:
- '1611.01990'
pmid:
- '30176599'
intvolume: ' 26'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.48550/arXiv.1611.01990 '
month: '02'
oa: 1
oa_version: Preprint
page: 1320-1331
pmid: 1
publication: IEEE Transactions on Visualization and Computer Graphics
publication_identifier:
eissn:
- 2160-9306
issn:
- 1077-2626
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Hamiltonian operator for spectral shape analysis
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 26
year: '2020'
...
---
_id: '18251'
abstract:
- lang: eng
text: Magnetic Resonance Imaging (MRI) has long been considered to be among the
gold standards of today’s diagnostic imaging. The most significant drawback of
MRI is long acquisition times, prohibiting its use in standard practice for some
applications. Compressed sensing (CS) proposes to subsample the k-space (the Fourier
domain dual to the physical space of spatial coordinates) leading to significantly
accelerated acquisition. However, the benefit of compressed sensing has not been
fully exploited; most of the sampling densities obtained through CS do not produce
a trajectory that obeys the stringent constraints of the MRI machine imposed in
practice. Inspired by recent success of deep learning-based approaches for image
reconstruction and ideas from computational imaging on learning-based design of
imaging systems, we introduce 3D FLAT, a novel protocol for data-driven design
of 3D non-Cartesian accelerated trajectories in MRI. Our proposal leverages the
entire 3D k-space to simultaneously learn a physically feasible acquisition trajectory
with a reconstruction method. Experimental results, performed as a proof-of-concept,
suggest that 3D FLAT achieves higher image quality for a given readout time compared
to standard trajectories such as radial, stack-of-stars, or 2D learned trajectories
(trajectories that evolve only in the 2D plane while fully sampling along the
third dimension). Furthermore, we demonstrate evidence supporting the significant
benefit of performing MRI acquisitions using non-Cartesian 3D trajectories over
2D non-Cartesian trajectories acquired slice-wise.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Jonathan
full_name: Alush-Aben, Jonathan
last_name: Alush-Aben
- first_name: Linor
full_name: Ackerman-Schraier, Linor
last_name: Ackerman-Schraier
- 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: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Alush-Aben J, Ackerman-Schraier L, Weiss T, Vedula S, Senouf O, Bronstein
AM. 3D FLAT: Feasible learned acquisition trajectories for accelerated MRI. In:
International Workshop on Machine Learning for Medical Image Reconstruction.
Vol 12450. Springer Nature; 2020:3-16. doi:10.1007/978-3-030-61598-7_1'
apa: 'Alush-Aben, J., Ackerman-Schraier, L., Weiss, T., Vedula, S., Senouf, O.,
& Bronstein, A. M. (2020). 3D FLAT: Feasible learned acquisition trajectories
for accelerated MRI. In International Workshop on Machine Learning for Medical
Image Reconstruction (Vol. 12450, pp. 3–16). Lima, Peru: Springer Nature.
https://doi.org/10.1007/978-3-030-61598-7_1'
chicago: 'Alush-Aben, Jonathan, Linor Ackerman-Schraier, Tomer Weiss, Sanketh Vedula,
Ortal Senouf, and Alex M. Bronstein. “3D FLAT: Feasible Learned Acquisition Trajectories
for Accelerated MRI.” In International Workshop on Machine Learning for Medical
Image Reconstruction, 12450:3–16. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-61598-7_1.'
ieee: 'J. Alush-Aben, L. Ackerman-Schraier, T. Weiss, S. Vedula, O. Senouf, and
A. M. Bronstein, “3D FLAT: Feasible learned acquisition trajectories for accelerated
MRI,” in International Workshop on Machine Learning for Medical Image Reconstruction,
Lima, Peru, 2020, vol. 12450, pp. 3–16.'
ista: 'Alush-Aben J, Ackerman-Schraier L, Weiss T, Vedula S, Senouf O, Bronstein
AM. 2020. 3D FLAT: Feasible learned acquisition trajectories for accelerated MRI.
International Workshop on Machine Learning for Medical Image Reconstruction. MLMIR:
Workshop on Machine Learning for Medical Image Reconstruction, LNCS, vol. 12450,
3–16.'
mla: 'Alush-Aben, Jonathan, et al. “3D FLAT: Feasible Learned Acquisition Trajectories
for Accelerated MRI.” International Workshop on Machine Learning for Medical
Image Reconstruction, vol. 12450, Springer Nature, 2020, pp. 3–16, doi:10.1007/978-3-030-61598-7_1.'
short: J. Alush-Aben, L. Ackerman-Schraier, T. Weiss, S. Vedula, O. Senouf, A.M.
Bronstein, in:, International Workshop on Machine Learning for Medical Image Reconstruction,
Springer Nature, 2020, pp. 3–16.
conference:
end_date: 2020-10-08
location: Lima, Peru
name: 'MLMIR: Workshop on Machine Learning for Medical Image Reconstruction'
start_date: 2020-10-08
date_created: 2024-10-08T13:06:03Z
date_published: 2020-10-20T00:00:00Z
date_updated: 2025-01-23T15:13:44Z
day: '20'
doi: 10.1007/978-3-030-61598-7_1
extern: '1'
intvolume: ' 12450'
language:
- iso: eng
month: '10'
oa_version: None
page: 3 - 16
publication: International Workshop on Machine Learning for Medical Image Reconstruction
publication_identifier:
eisbn:
- '9783030615987'
eissn:
- 1611-3349
isbn:
- '9783030615970'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: '3D FLAT: Feasible learned acquisition trajectories for accelerated MRI'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 12450
year: '2020'
...
---
OA_type: closed access
_id: '18252'
abstract:
- lang: eng
text: 'Vector fields arise in a variety of quantity measure and visualization techniques,
such as fluid flow imaging, motion estimation, deformation measures, and color
imaging, leading to a better understanding of physical phenomena. Recent progress
in vector field imaging technologies has emphasized the need for efficient noise
removal and reconstruction algorithms. A key ingredient in the successful extraction
of signals from noisy measurements is prior information, which can often be represented
as a parameterized model. In this work, we extend the overparameterization variational
framework in order to perform model-based reconstruction of vector fields. The
overparameterization methodology combines local modeling of the data with global
model parameter regularization. By considering the vector field as a linear combination
of basis vector fields and appropriate scale and rotation coefficients, we can
reduce the denoising problem to a simpler form of coefficient recovery. We introduce
two versions of the overparameterization framework: a total variation-based method
and a sparsity-based method, which relies on the cosparse analysis model. We demonstrate
the efficiency of the proposed frameworks for two- and three-dimensional vector
fields with linear and quadratic overparameterization models.'
article_processing_charge: No
article_type: original
author:
- first_name: Keren
full_name: Rotker, Keren
last_name: Rotker
- first_name: Dafna Ben
full_name: Bashat, Dafna Ben
last_name: Bashat
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: Rotker K, Bashat DB, Bronstein AM. Overparameterized models for vector fields.
SIAM Journal on Imaging Sciences. 2020;13(3):1386-1414. doi:10.1137/19m1280697
apa: Rotker, K., Bashat, D. B., & Bronstein, A. M. (2020). Overparameterized
models for vector fields. SIAM Journal on Imaging Sciences. Society for
Industrial & Applied Mathematics. https://doi.org/10.1137/19m1280697
chicago: Rotker, Keren, Dafna Ben Bashat, and Alex M. Bronstein. “Overparameterized
Models for Vector Fields.” SIAM Journal on Imaging Sciences. Society for
Industrial & Applied Mathematics, 2020. https://doi.org/10.1137/19m1280697.
ieee: K. Rotker, D. B. Bashat, and A. M. Bronstein, “Overparameterized models for
vector fields,” SIAM Journal on Imaging Sciences, vol. 13, no. 3. Society
for Industrial & Applied Mathematics, pp. 1386–1414, 2020.
ista: Rotker K, Bashat DB, Bronstein AM. 2020. Overparameterized models for vector
fields. SIAM Journal on Imaging Sciences. 13(3), 1386–1414.
mla: Rotker, Keren, et al. “Overparameterized Models for Vector Fields.” SIAM
Journal on Imaging Sciences, vol. 13, no. 3, Society for Industrial &
Applied Mathematics, 2020, pp. 1386–414, doi:10.1137/19m1280697.
short: K. Rotker, D.B. Bashat, A.M. Bronstein, SIAM Journal on Imaging Sciences
13 (2020) 1386–1414.
date_created: 2024-10-08T13:06:25Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2024-10-15T10:43:38Z
day: '01'
doi: 10.1137/19m1280697
extern: '1'
intvolume: ' 13'
issue: '3'
language:
- iso: eng
month: '01'
oa_version: None
page: 1386-1414
publication: SIAM Journal on Imaging Sciences
publication_identifier:
eissn:
- 1936-4954
publication_status: published
publisher: Society for Industrial & Applied Mathematics
quality_controlled: '1'
scopus_import: '1'
status: public
title: Overparameterized models for vector fields
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2020'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '18253'
abstract:
- lang: eng
text: PCNA, the ring that encircles DNA maintaining the processivity of DNA polymerases,
is modified by ubiquitin and SUMO. Whereas ubiquitin is required for bypassing
lesions through the DNA damage tolerance (DDT) pathways, we show here that SUMOylation
represses another pathway, salvage recombination. The Srs2 helicase is recruited
to SUMOylated PCNA and prevents the salvage pathway from acting. The pathway can
be induced by overexpressing the PCNA unloader Elg1, or the homologous recombination
protein Rad52. Our results underscore the role of PCNA modifications in controlling
the various bypass and DNA repair mechanisms.
article_number: 00705-20
article_processing_charge: Yes
article_type: original
author:
- first_name: Matan
full_name: Arbel, Matan
last_name: Arbel
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Soumitra
full_name: Sau, Soumitra
last_name: Sau
- first_name: Batia
full_name: Liefshitz, Batia
last_name: Liefshitz
- first_name: Martin
full_name: Kupiec, Martin
last_name: Kupiec
citation:
ama: Arbel M, Bronstein AM, Sau S, Liefshitz B, Kupiec M. Access to PCNA by Srs2
and Elg1 controls the choice between alternative repair pathways in Saccharomyces
cerevisiae. mBio. 2020;11(3). doi:10.1128/mbio.00705-20
apa: Arbel, M., Bronstein, A. M., Sau, S., Liefshitz, B., & Kupiec, M. (2020).
Access to PCNA by Srs2 and Elg1 controls the choice between alternative repair
pathways in Saccharomyces cerevisiae. MBio. American Society for Microbiology.
https://doi.org/10.1128/mbio.00705-20
chicago: Arbel, Matan, Alex M. Bronstein, Soumitra Sau, Batia Liefshitz, and Martin
Kupiec. “Access to PCNA by Srs2 and Elg1 Controls the Choice between Alternative
Repair Pathways in Saccharomyces Cerevisiae.” MBio. American Society for
Microbiology, 2020. https://doi.org/10.1128/mbio.00705-20.
ieee: M. Arbel, A. M. Bronstein, S. Sau, B. Liefshitz, and M. Kupiec, “Access to
PCNA by Srs2 and Elg1 controls the choice between alternative repair pathways
in Saccharomyces cerevisiae,” mBio, vol. 11, no. 3. American Society for
Microbiology, 2020.
ista: Arbel M, Bronstein AM, Sau S, Liefshitz B, Kupiec M. 2020. Access to PCNA
by Srs2 and Elg1 controls the choice between alternative repair pathways in Saccharomyces
cerevisiae. mBio. 11(3), 00705-20.
mla: Arbel, Matan, et al. “Access to PCNA by Srs2 and Elg1 Controls the Choice between
Alternative Repair Pathways in Saccharomyces Cerevisiae.” MBio, vol. 11,
no. 3, 00705-20, American Society for Microbiology, 2020, doi:10.1128/mbio.00705-20.
short: M. Arbel, A.M. Bronstein, S. Sau, B. Liefshitz, M. Kupiec, MBio 11 (2020).
date_created: 2024-10-08T13:06:43Z
date_published: 2020-06-01T00:00:00Z
date_updated: 2024-10-15T10:50:42Z
day: '01'
doi: 10.1128/mbio.00705-20
extern: '1'
external_id:
pmid:
- '32371600'
intvolume: ' 11'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1128/mbio.00705-20
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
publication: mBio
publication_identifier:
eissn:
- 2150-7511
issn:
- 2161-2129
publication_status: published
publisher: American Society for Microbiology
quality_controlled: '1'
scopus_import: '1'
status: public
title: Access to PCNA by Srs2 and Elg1 controls the choice between alternative repair
pathways in Saccharomyces cerevisiae
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2020'
...
---
_id: '18255'
abstract:
- lang: eng
text: Learning an object detection or retrieval system requires a large data set
with manual annotations. Such data sets are expensive and time consuming to create
and therefore difficult to obtain on a large scale. In this work, we propose to
exploit the natural correlation in narrations and the visual presence of objects
in video, to learn an object detector and retrieval without any manual labeling
involved. We pose the problem as weakly supervised learning with noisy labels,
and propose a novel object detection paradigm under these constraints. We handle
the background rejection by using contrastive samples and confront the high level
of label noise with a new clustering score. Our evaluation is based on a set of
11 manually annotated objects in over 5000 frames. We show comparison to a weakly-supervised
approach as baseline and provide a strongly labeled upper bound.
article_number: '9022341'
article_processing_charge: No
arxiv: 1
author:
- first_name: Elad
full_name: Amrani, Elad
last_name: Amrani
- first_name: Rami
full_name: Ben-Ari, Rami
last_name: Ben-Ari
- first_name: Tal
full_name: Hakim, Tal
last_name: Hakim
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Amrani E, Ben-Ari R, Hakim T, Bronstein AM. Learning to detect and retrieve
objects from unlabeled videos. In: 2019 IEEE/CVF International Conference on
Computer Vision Workshop (ICCVW). IEEE; 2020. doi:10.1109/iccvw.2019.00567'
apa: 'Amrani, E., Ben-Ari, R., Hakim, T., & Bronstein, A. M. (2020). Learning
to detect and retrieve objects from unlabeled videos. In 2019 IEEE/CVF International
Conference on Computer Vision Workshop (ICCVW). Seoul, Korea (South): IEEE.
https://doi.org/10.1109/iccvw.2019.00567'
chicago: Amrani, Elad, Rami Ben-Ari, Tal Hakim, and Alex M. Bronstein. “Learning
to Detect and Retrieve Objects from Unlabeled Videos.” In 2019 IEEE/CVF International
Conference on Computer Vision Workshop (ICCVW). IEEE, 2020. https://doi.org/10.1109/iccvw.2019.00567.
ieee: E. Amrani, R. Ben-Ari, T. Hakim, and A. M. Bronstein, “Learning to detect
and retrieve objects from unlabeled videos,” in 2019 IEEE/CVF International
Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2020.
ista: Amrani E, Ben-Ari R, Hakim T, Bronstein AM. 2020. Learning to detect and retrieve
objects from unlabeled videos. 2019 IEEE/CVF International Conference on Computer
Vision Workshop (ICCVW). 17th IEEE/CVF International Conference on Computer Vision
Workshop, 9022341.
mla: Amrani, Elad, et al. “Learning to Detect and Retrieve Objects from Unlabeled
Videos.” 2019 IEEE/CVF International Conference on Computer Vision Workshop
(ICCVW), 9022341, IEEE, 2020, doi:10.1109/iccvw.2019.00567.
short: E. Amrani, R. Ben-Ari, T. Hakim, A.M. Bronstein, in:, 2019 IEEE/CVF International
Conference on Computer Vision Workshop (ICCVW), IEEE, 2020.
conference:
end_date: 2019-10-28
location: Seoul, Korea (South)
name: 17th IEEE/CVF International Conference on Computer Vision Workshop
start_date: 2019-10-27
date_created: 2024-10-08T13:07:16Z
date_published: 2020-03-05T00:00:00Z
date_updated: 2024-12-05T16:04:03Z
day: '05'
doi: 10.1109/iccvw.2019.00567
extern: '1'
external_id:
arxiv:
- '1905.11137'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1905.11137
month: '03'
oa: 1
oa_version: Preprint
publication: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
publication_identifier:
eissn:
- 2473-9944
isbn:
- '9781728150246'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning to detect and retrieve objects from unlabeled videos
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18258'
abstract:
- lang: eng
text: Distance metric learning (DML) has been successfully applied to object classification,
both in the standard regime of rich training data and in the few-shot scenario,
where each category is represented by only a few examples. In this work, we propose
a new method for DML that simultaneously learns the backbone network parameters,
the embedding space, and the multi-modal distribution of each of the training
categories in that space, in a single end-to-end training process. Our approach
outperforms state-of-the-art methods for DML-based object classification on a
variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness
of our approach on the problem of few-shot object detection, by incorporating
the proposed DML architecture as a classification head into a standard object
detection model. We achieve the best results on the ImageNet-LOC dataset compared
to strong baselines, when only a few training examples are available. We also
offer the community a new episodic benchmark based on the ImageNet dataset for
the few-shot object detection task.
article_number: '8953439'
article_processing_charge: No
author:
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
- first_name: Joseph
full_name: Shtok, Joseph
last_name: Shtok
- first_name: Sivan
full_name: Harary, Sivan
last_name: Harary
- first_name: Eli
full_name: Schwartz, Eli
last_name: Schwartz
- first_name: Amit
full_name: Aides, Amit
last_name: Aides
- first_name: Rogerio
full_name: Feris, Rogerio
last_name: Feris
- first_name: Raja
full_name: Giryes, Raja
last_name: Giryes
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Karlinsky L, Shtok J, Harary S, et al. Repmet: Representative-based metric
learning for classification and few-shot object detection. In: 2019 IEEE/CVF
Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2020.
doi:10.1109/cvpr.2019.00534'
apa: 'Karlinsky, L., Shtok, J., Harary, S., Schwartz, E., Aides, A., Feris, R.,
… Bronstein, A. M. (2020). Repmet: Representative-based metric learning for classification
and few-shot object detection. In 2019 IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR). Long Beach, CA, United States: IEEE. https://doi.org/10.1109/cvpr.2019.00534'
chicago: 'Karlinsky, Leonid, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides,
Rogerio Feris, Raja Giryes, and Alex M. Bronstein. “Repmet: Representative-Based
Metric Learning for Classification and Few-Shot Object Detection.” In 2019
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE,
2020. https://doi.org/10.1109/cvpr.2019.00534.'
ieee: 'L. Karlinsky et al., “Repmet: Representative-based metric learning
for classification and few-shot object detection,” in 2019 IEEE/CVF Conference
on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, United
States, 2020.'
ista: 'Karlinsky L, Shtok J, Harary S, Schwartz E, Aides A, Feris R, Giryes R, Bronstein
AM. 2020. Repmet: Representative-based metric learning for classification and
few-shot object detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR). 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition,
8953439.'
mla: 'Karlinsky, Leonid, et al. “Repmet: Representative-Based Metric Learning for
Classification and Few-Shot Object Detection.” 2019 IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR), 8953439, IEEE, 2020, doi:10.1109/cvpr.2019.00534.'
short: L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes,
A.M. Bronstein, in:, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, 2020.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition
start_date: 2019-06-15
date_created: 2024-10-08T13:08:09Z
date_published: 2020-01-09T00:00:00Z
date_updated: 2024-12-05T15:38:16Z
day: '09'
doi: 10.1109/cvpr.2019.00534
extern: '1'
language:
- iso: eng
month: '01'
oa_version: None
publication: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781728132945'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Repmet: Representative-based metric learning for classification and few-shot
object detection'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18259'
abstract:
- lang: eng
text: Example synthesis is one of the leading methods to tackle the problem of few-shot
learning, where only a small number of samples per class are available. However,
current synthesis approaches only address the scenario of a single category label
per image. In this work, we propose a novel technique for synthesizing samples
with multiple labels for the (yet unhandled) multi-label few-shot classification
scenario. We propose to combine pairs of given examples in feature space, so that
the resulting synthesized feature vectors will correspond to examples whose label
sets are obtained through certain set operations on the label sets of the corresponding
input pairs. Thus, our method is capable of producing a sample containing the
intersection, union or set-difference of labels present in two input samples.
As we show, these set operations generalize to labels unseen during training.
This enables performing augmentation on examples of novel categories, thus, facilitating
multi-label few-shot classifier learning. We conduct numerous experiments showing
promising results for the label-set manipulation capabilities of the proposed
approach, both directly (using the classification and retrieval metrics), and
in the context of performing data augmentation for multi-label few-shot learning.
We propose a benchmark for this new and challenging task and show that our method
compares favorably to all the common baselines.
article_number: '8954088'
article_processing_charge: No
arxiv: 1
author:
- first_name: Amit
full_name: Alfassy, Amit
last_name: Alfassy
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
- first_name: Amit
full_name: Aides, Amit
last_name: Aides
- first_name: Joseph
full_name: Shtok, Joseph
last_name: Shtok
- first_name: Sivan
full_name: Harary, Sivan
last_name: Harary
- first_name: Rogerio
full_name: Feris, Rogerio
last_name: Feris
- first_name: Raja
full_name: Giryes, Raja
last_name: Giryes
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Alfassy A, Karlinsky L, Aides A, et al. Laso: Label-set operations networks
for multi-label few-shot learning. In: 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR). IEEE; 2020. doi:10.1109/cvpr.2019.00671'
apa: 'Alfassy, A., Karlinsky, L., Aides, A., Shtok, J., Harary, S., Feris, R., …
Bronstein, A. M. (2020). Laso: Label-set operations networks for multi-label few-shot
learning. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR). Long Beach, CA, United States: IEEE. https://doi.org/10.1109/cvpr.2019.00671'
chicago: 'Alfassy, Amit, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary,
Rogerio Feris, Raja Giryes, and Alex M. Bronstein. “Laso: Label-Set Operations
Networks for Multi-Label Few-Shot Learning.” In 2019 IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. https://doi.org/10.1109/cvpr.2019.00671.'
ieee: 'A. Alfassy et al., “Laso: Label-set operations networks for multi-label
few-shot learning,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), Long Beach, CA, United States, 2020.'
ista: 'Alfassy A, Karlinsky L, Aides A, Shtok J, Harary S, Feris R, Giryes R, Bronstein
AM. 2020. Laso: Label-set operations networks for multi-label few-shot learning.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 32nd
IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8954088.'
mla: 'Alfassy, Amit, et al. “Laso: Label-Set Operations Networks for Multi-Label
Few-Shot Learning.” 2019 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), 8954088, IEEE, 2020, doi:10.1109/cvpr.2019.00671.'
short: A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes,
A.M. Bronstein, in:, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, 2020.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition
start_date: 2019-06-15
date_created: 2024-10-08T13:08:26Z
date_published: 2020-01-09T00:00:00Z
date_updated: 2024-12-05T15:33:21Z
day: '09'
doi: 10.1109/cvpr.2019.00671
extern: '1'
external_id:
arxiv:
- '1902.09811'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1902.09811
month: '01'
oa: 1
oa_version: Preprint
publication: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781728132945'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Laso: Label-set operations networks for multi-label few-shot learning'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '18260'
abstract:
- lang: eng
text: We introduce the first completely unsupervised correspondence learning approach
for deformable 3D shapes. Key to our model is the understanding that natural deformations
(such as changes in pose) approximately preserve the metric structure of the surface,
yielding a natural criterion to drive the learning process toward distortion-minimizing
predictions. On this basis, we overcome the need for annotated data and replace
it by a purely geometric criterion. The resulting learning model is class-agnostic,
and is able to leverage any type of deformable geometric data for the training
phase. In contrast to existing supervised approaches which specialize on the class
seen at training time, we demonstrate stronger generalization as well as applicability
to a variety of challenging settings. We showcase our method on a wide selection
of correspondence benchmarks, where we outperform other methods in terms of accuracy,
generalization, and efficiency.
article_number: '8953366'
article_processing_charge: No
author:
- first_name: Oshri
full_name: Halimi, Oshri
last_name: Halimi
- first_name: Or
full_name: Litany, Or
last_name: Litany
- first_name: Emanuele Rodola
full_name: Rodola, Emanuele Rodola
last_name: Rodola
- 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: 'Halimi O, Litany O, Rodola ER, Bronstein AM, Kimmel R. Unsupervised learning
of dense shape correspondence. In: 2019 IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR). IEEE; 2020. doi:10.1109/cvpr.2019.00450'
apa: 'Halimi, O., Litany, O., Rodola, E. R., Bronstein, A. M., & Kimmel, R.
(2020). Unsupervised learning of dense shape correspondence. In 2019 IEEE/CVF
Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach,
CA, United States: IEEE. https://doi.org/10.1109/cvpr.2019.00450'
chicago: Halimi, Oshri, Or Litany, Emanuele Rodola Rodola, Alex M. Bronstein, and
Ron Kimmel. “Unsupervised Learning of Dense Shape Correspondence.” In 2019
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE,
2020. https://doi.org/10.1109/cvpr.2019.00450.
ieee: O. Halimi, O. Litany, E. R. Rodola, A. M. Bronstein, and R. Kimmel, “Unsupervised
learning of dense shape correspondence,” in 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR), Long Beach, CA, United States, 2020.
ista: Halimi O, Litany O, Rodola ER, Bronstein AM, Kimmel R. 2020. Unsupervised
learning of dense shape correspondence. 2019 IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR). 32nd IEEE/CVF Conference on Computer Vision and
Pattern Recognition, 8953366.
mla: Halimi, Oshri, et al. “Unsupervised Learning of Dense Shape Correspondence.”
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
8953366, IEEE, 2020, doi:10.1109/cvpr.2019.00450.
short: O. Halimi, O. Litany, E.R. Rodola, A.M. Bronstein, R. Kimmel, in:, 2019 IEEE/CVF
Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition
start_date: 2019-06-15
date_created: 2024-10-08T13:08:43Z
date_published: 2020-01-09T00:00:00Z
date_updated: 2024-12-05T15:19:01Z
day: '09'
doi: 10.1109/cvpr.2019.00450
extern: '1'
language:
- iso: eng
month: '01'
oa_version: None
publication: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781728132945'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Unsupervised learning of dense shape correspondence
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '12310'
abstract:
- lang: eng
text: Let be a sequence of points on an elliptic curve defined over a number field
K. In this paper, we study the denominators of the x-coordinates of this sequence.
We prove that, if Q is a torsion point of prime order, then for n large enough
there always exists a primitive divisor. Later on, we show the link between the
study of the primitive divisors and a Lang-Trotter conjecture. Indeed, given two
points P and Q on the elliptic curve, we prove a lower bound for the number of
primes p such that P is in the orbit of Q modulo p.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Matteo
full_name: Verzobio, Matteo
id: 7aa8f170-131e-11ed-88e1-a9efd01027cb
last_name: Verzobio
orcid: 0000-0002-0854-0306
citation:
ama: Verzobio M. Primitive divisors of sequences associated to elliptic curves.
Journal of Number Theory. 2020;209(4):378-390. doi:10.1016/j.jnt.2019.09.003
apa: Verzobio, M. (2020). Primitive divisors of sequences associated to elliptic
curves. Journal of Number Theory. Elsevier. https://doi.org/10.1016/j.jnt.2019.09.003
chicago: Verzobio, Matteo. “Primitive Divisors of Sequences Associated to Elliptic
Curves.” Journal of Number Theory. Elsevier, 2020. https://doi.org/10.1016/j.jnt.2019.09.003.
ieee: M. Verzobio, “Primitive divisors of sequences associated to elliptic curves,”
Journal of Number Theory, vol. 209, no. 4. Elsevier, pp. 378–390, 2020.
ista: Verzobio M. 2020. Primitive divisors of sequences associated to elliptic curves.
Journal of Number Theory. 209(4), 378–390.
mla: Verzobio, Matteo. “Primitive Divisors of Sequences Associated to Elliptic Curves.”
Journal of Number Theory, vol. 209, no. 4, Elsevier, 2020, pp. 378–90,
doi:10.1016/j.jnt.2019.09.003.
short: M. Verzobio, Journal of Number Theory 209 (2020) 378–390.
corr_author: '1'
date_created: 2023-01-16T11:45:07Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2024-10-09T21:05:15Z
day: '01'
doi: 10.1016/j.jnt.2019.09.003
extern: '1'
external_id:
arxiv:
- '1906.00632'
intvolume: ' 209'
issue: '4'
keyword:
- Algebra and Number Theory
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1906.00632
month: '04'
oa: 1
oa_version: Preprint
page: 378-390
publication: Journal of Number Theory
publication_identifier:
issn:
- 0022-314X
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Primitive divisors of sequences associated to elliptic curves
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 209
year: '2020'
...
---
_id: '12593'
abstract:
- lang: eng
text: 'Rock debris can accumulate on glacier surfaces and dramatically reduce glacier
melt. The structure of a debris cover is unique to each glacier and sensitive
to climate. Despite this, debris cover has been omitted from global glacier models
and forecasts of their response to a changing climate. Fundamental to resolving
these omissions is a global map of debris cover and an estimate of its future
spatial evolution. Here we use Landsat imagery and a detailed correction to the
Randolph Glacier Inventory to show that 7.3% of mountain glacier area is debris
covered and over half of Earth’s debris is concentrated in three regions: Alaska
(38.6% of total debris-covered area), Southwest Asia (12.6%) and Greenland (12.0%).
We use a set of new metrics, which include stage, the current position of a glacier
on its trajectory towards reaching its spatial carrying capacity of debris cover,
to quantify the state of glaciers. Debris cover is present on 44% of Earth’s glaciers
and prominent (>1.0 km2) on 15%. Of Earth’s glaciers, 20% have a substantial percentage
of debris cover for which the net stage is 36% and the bulk of individual glaciers
have evolved beyond an optimal moraine configuration favourable for debris-cover
expansion. Use of this dataset in global-scale models will enable improved estimates
of melt over 10.6% of the global glacier domain.'
article_processing_charge: No
article_type: original
author:
- first_name: Sam
full_name: Herreid, Sam
last_name: Herreid
- first_name: Francesca
full_name: Pellicciotti, Francesca
id: b28f055a-81ea-11ed-b70c-a9fe7f7b0e70
last_name: Pellicciotti
citation:
ama: Herreid S, Pellicciotti F. The state of rock debris covering Earth’s glaciers.
Nature Geoscience. 2020;13(9):621-627. doi:10.1038/s41561-020-0615-0
apa: Herreid, S., & Pellicciotti, F. (2020). The state of rock debris covering
Earth’s glaciers. Nature Geoscience. Springer Nature. https://doi.org/10.1038/s41561-020-0615-0
chicago: Herreid, Sam, and Francesca Pellicciotti. “The State of Rock Debris Covering
Earth’s Glaciers.” Nature Geoscience. Springer Nature, 2020. https://doi.org/10.1038/s41561-020-0615-0.
ieee: S. Herreid and F. Pellicciotti, “The state of rock debris covering Earth’s
glaciers,” Nature Geoscience, vol. 13, no. 9. Springer Nature, pp. 621–627,
2020.
ista: Herreid S, Pellicciotti F. 2020. The state of rock debris covering Earth’s
glaciers. Nature Geoscience. 13(9), 621–627.
mla: Herreid, Sam, and Francesca Pellicciotti. “The State of Rock Debris Covering
Earth’s Glaciers.” Nature Geoscience, vol. 13, no. 9, Springer Nature,
2020, pp. 621–27, doi:10.1038/s41561-020-0615-0.
short: S. Herreid, F. Pellicciotti, Nature Geoscience 13 (2020) 621–627.
date_created: 2023-02-20T08:12:17Z
date_published: 2020-09-02T00:00:00Z
date_updated: 2023-02-28T12:45:37Z
day: '02'
doi: 10.1038/s41561-020-0615-0
extern: '1'
intvolume: ' 13'
issue: '9'
keyword:
- General Earth and Planetary Sciences
language:
- iso: eng
month: '09'
oa_version: None
page: 621-627
publication: Nature Geoscience
publication_identifier:
eissn:
- 1752-0908
issn:
- 1752-0894
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1038/s41561-020-0630-1
scopus_import: '1'
status: public
title: The state of rock debris covering Earth’s glaciers
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2020'
...
---
_id: '12594'
abstract:
- lang: eng
text: Information about end-of-winter spatial distribution of snow depth is important
for seasonal forecasts of spring/summer streamflow in high-mountain regions. Nevertheless,
such information typically relies upon extrapolation from a sparse network of
observations at low elevations. Here, we test the potential of high-resolution
snow depth data derived from optical stereophotogrammetry of Pléiades satellites
for improving the representation of snow depth initial conditions (SDICs) in a
glacio-hydrological model and assess potential improvements in the skill of snowmelt
and streamflow simulations in a high-elevation Andean catchment. We calibrate
model parameters controlling glacier mass balance and snow cover evolution using
ground-based and satellite observations, and consider the relative importance
of accurate estimates of SDICs compared to model parameters and forcings. We find
that Pléiades SDICs improve the simulation of snow-covered area, glacier mass
balance, and monthly streamflow compared to alternative SDICs based upon extrapolation
of meteorological variables or statistical methods to estimate SDICs based upon
topography. Model simulations are found to be sensitive to SDICs in the early
spring (up to 48% variability in modeled streamflow compared to the best estimate
model), and to temperature gradients in all months that control albedo and melt
rates over a large elevation range (>2,400 m). As such, appropriately characterizing
the distribution of total snow volume with elevation is important for reproducing
total streamflow and the proportions of snowmelt. Therefore, optical stereo-photogrammetry
offers an advantage for obtaining SDICs that aid both the timing and magnitude
of streamflow simulations, process representation (e.g., snow cover evolution)
and has the potential for large spatial domains.
article_number: e2020WR027188
article_processing_charge: No
article_type: original
author:
- first_name: Thomas E.
full_name: Shaw, Thomas E.
last_name: Shaw
- first_name: Alexis
full_name: Caro, Alexis
last_name: Caro
- first_name: Pablo
full_name: Mendoza, Pablo
last_name: Mendoza
- first_name: Álvaro
full_name: Ayala, Álvaro
last_name: Ayala
- first_name: Francesca
full_name: Pellicciotti, Francesca
id: b28f055a-81ea-11ed-b70c-a9fe7f7b0e70
last_name: Pellicciotti
- first_name: Simon
full_name: Gascoin, Simon
last_name: Gascoin
- first_name: James
full_name: McPhee, James
last_name: McPhee
citation:
ama: Shaw TE, Caro A, Mendoza P, et al. The utility of optical satellite winter
snow depths for initializing a glacio‐hydrological model of a High‐Elevation,
Andean catchment. Water Resources Research. 2020;56(8). doi:10.1029/2020wr027188
apa: Shaw, T. E., Caro, A., Mendoza, P., Ayala, Á., Pellicciotti, F., Gascoin, S.,
& McPhee, J. (2020). The utility of optical satellite winter snow depths for
initializing a glacio‐hydrological model of a High‐Elevation, Andean catchment.
Water Resources Research. American Geophysical Union. https://doi.org/10.1029/2020wr027188
chicago: Shaw, Thomas E., Alexis Caro, Pablo Mendoza, Álvaro Ayala, Francesca Pellicciotti,
Simon Gascoin, and James McPhee. “The Utility of Optical Satellite Winter Snow
Depths for Initializing a Glacio‐hydrological Model of a High‐Elevation, Andean
Catchment.” Water Resources Research. American Geophysical Union, 2020.
https://doi.org/10.1029/2020wr027188.
ieee: T. E. Shaw et al., “The utility of optical satellite winter snow depths
for initializing a glacio‐hydrological model of a High‐Elevation, Andean catchment,”
Water Resources Research, vol. 56, no. 8. American Geophysical Union, 2020.
ista: Shaw TE, Caro A, Mendoza P, Ayala Á, Pellicciotti F, Gascoin S, McPhee J.
2020. The utility of optical satellite winter snow depths for initializing a glacio‐hydrological
model of a High‐Elevation, Andean catchment. Water Resources Research. 56(8),
e2020WR027188.
mla: Shaw, Thomas E., et al. “The Utility of Optical Satellite Winter Snow Depths
for Initializing a Glacio‐hydrological Model of a High‐Elevation, Andean Catchment.”
Water Resources Research, vol. 56, no. 8, e2020WR027188, American Geophysical
Union, 2020, doi:10.1029/2020wr027188.
short: T.E. Shaw, A. Caro, P. Mendoza, Á. Ayala, F. Pellicciotti, S. Gascoin, J.
McPhee, Water Resources Research 56 (2020).
date_created: 2023-02-20T08:12:22Z
date_published: 2020-08-01T00:00:00Z
date_updated: 2023-02-28T12:41:45Z
day: '01'
doi: 10.1029/2020wr027188
extern: '1'
intvolume: ' 56'
issue: '8'
keyword:
- Water Science and Technology
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1029/2020WR027188
month: '08'
oa: 1
oa_version: Published Version
publication: Water Resources Research
publication_identifier:
eissn:
- 1944-7973
issn:
- 0043-1397
publication_status: published
publisher: American Geophysical Union
quality_controlled: '1'
scopus_import: '1'
status: public
title: The utility of optical satellite winter snow depths for initializing a glacio‐hydrological
model of a High‐Elevation, Andean catchment
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 56
year: '2020'
...
---
_id: '12595'
abstract:
- lang: eng
text: The seasonal dynamic changes of Tibetan glaciers have seen little prior investigation,
despite the increase in geodetic studies of multi-year changes. This study compares
seasonal glacier dynamics (“cold” and “warm” seasons) in the ablation zone of
Parlung No. 4 Glacier, a temperate glacier in the monsoon-influenced southeastern
Tibetan Plateau, by using repeat unpiloted aerial vehicle (UAV) surveys combined
with Structure-from-Motion (SfM) photogrammetry and ground stake measurements.
Our results showed that the surveyed ablation zone had a mean change of −2.7 m
of ice surface elevation during the period of September 2018 to October 2019 but
is characterized by significant seasonal cyclic variations with ice surface elevation
lifting (+2.0 m) in the cold season (September 2018 to June 2019) but lowering
(−4.7 m) in the warm season (June 2019 to October 2019). Over an annual timescale,
surface lowering was greatly suppressed by the resupply of ice from the glacier’s
accumulation area—the annual emergence velocity compensates for about 55% of surface
ablation in our study area. Cold season emergence velocities (3.0 ± 1.2 m) were
~5-times larger than those observed in the warm season (0.6 ± 1.0 m). Distinct
spring precipitation patterns may contribute to these distinct seasonal signals.
Such seasonal dynamic conditions are possibly critical for different glacier responses
to climate change in this region of the Tibetan Plateau, and perhaps further afield.
article_number: '2389'
article_processing_charge: No
article_type: original
author:
- first_name: Wei
full_name: Yang, Wei
last_name: Yang
- first_name: Chuanxi
full_name: Zhao, Chuanxi
last_name: Zhao
- first_name: Matthew
full_name: Westoby, Matthew
last_name: Westoby
- first_name: Tandong
full_name: Yao, Tandong
last_name: Yao
- first_name: Yongjie
full_name: Wang, Yongjie
last_name: Wang
- first_name: Francesca
full_name: Pellicciotti, Francesca
id: b28f055a-81ea-11ed-b70c-a9fe7f7b0e70
last_name: Pellicciotti
- first_name: Jianmin
full_name: Zhou, Jianmin
last_name: Zhou
- first_name: Zhen
full_name: He, Zhen
last_name: He
- first_name: Evan
full_name: Miles, Evan
last_name: Miles
citation:
ama: Yang W, Zhao C, Westoby M, et al. Seasonal dynamics of a temperate Tibetan
glacier revealed by high-resolution UAV photogrammetry and in situ measurements.
Remote Sensing. 2020;12(15). doi:10.3390/rs12152389
apa: Yang, W., Zhao, C., Westoby, M., Yao, T., Wang, Y., Pellicciotti, F., … Miles,
E. (2020). Seasonal dynamics of a temperate Tibetan glacier revealed by high-resolution
UAV photogrammetry and in situ measurements. Remote Sensing. MDPI. https://doi.org/10.3390/rs12152389
chicago: Yang, Wei, Chuanxi Zhao, Matthew Westoby, Tandong Yao, Yongjie Wang, Francesca
Pellicciotti, Jianmin Zhou, Zhen He, and Evan Miles. “Seasonal Dynamics of a Temperate
Tibetan Glacier Revealed by High-Resolution UAV Photogrammetry and in Situ Measurements.”
Remote Sensing. MDPI, 2020. https://doi.org/10.3390/rs12152389.
ieee: W. Yang et al., “Seasonal dynamics of a temperate Tibetan glacier revealed
by high-resolution UAV photogrammetry and in situ measurements,” Remote Sensing,
vol. 12, no. 15. MDPI, 2020.
ista: Yang W, Zhao C, Westoby M, Yao T, Wang Y, Pellicciotti F, Zhou J, He Z, Miles
E. 2020. Seasonal dynamics of a temperate Tibetan glacier revealed by high-resolution
UAV photogrammetry and in situ measurements. Remote Sensing. 12(15), 2389.
mla: Yang, Wei, et al. “Seasonal Dynamics of a Temperate Tibetan Glacier Revealed
by High-Resolution UAV Photogrammetry and in Situ Measurements.” Remote Sensing,
vol. 12, no. 15, 2389, MDPI, 2020, doi:10.3390/rs12152389.
short: W. Yang, C. Zhao, M. Westoby, T. Yao, Y. Wang, F. Pellicciotti, J. Zhou,
Z. He, E. Miles, Remote Sensing 12 (2020).
date_created: 2023-02-20T08:12:29Z
date_published: 2020-07-24T00:00:00Z
date_updated: 2023-02-28T12:36:22Z
day: '24'
doi: 10.3390/rs12152389
extern: '1'
intvolume: ' 12'
issue: '15'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.3390/rs12152389
month: '07'
oa: 1
oa_version: Published Version
publication: Remote Sensing
publication_identifier:
issn:
- 2072-4292
publication_status: published
publisher: MDPI
quality_controlled: '1'
scopus_import: '1'
status: public
title: Seasonal dynamics of a temperate Tibetan glacier revealed by high-resolution
UAV photogrammetry and in situ measurements
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 12
year: '2020'
...
---
_id: '12596'
abstract:
- lang: eng
text: As glaciers adjust their size in response to climate variations, long-term
changes in meltwater production can be expected, affecting the local availability
of water resources. We investigate glacier runoff in the period 1955–2016 in the
Maipo River basin (4843 km2, 33.0–34.3∘ S, 69.8–70.5∘ W), in the semiarid Andes
of Chile. The basin contains more than 800 glaciers, which cover 378 km2 in total
(inventoried in 2000). We model the mass balance and runoff contribution of 26
glaciers with the physically oriented and fully distributed TOPKAPI (Topographic
Kinematic Approximation and Integration)-ETH glacio-hydrological model and extrapolate
the results to the entire basin. TOPKAPI-ETH is run at a daily time step using
several glaciological and meteorological datasets, and its results are evaluated
against streamflow records, remotely sensed snow cover, and geodetic mass balances
for the periods 1955–2000 and 2000–2013. Results show that in 1955–2016 glacier
mass balance had a general decreasing trend as a basin average but also had differences
between the main sub-catchments. Glacier volume decreased by one-fifth (from 18.6±4.5
to 14.9±2.9 km3). Runoff from the initially glacierized areas was 177±25 mm yr−1
(16±7 % of the total contributions to the basin), but it shows a decreasing sequence
of maxima, which can be linked to the interplay between a decrease in precipitation
since the 1980s and the reduction of ice melt. Glaciers in the Maipo River basin
will continue retreating because they are not in equilibrium with the current
climate. In a hypothetical constant climate scenario, glacier volume would reduce
to 81±38 % of the year 2000 volume, and glacier runoff would be 78±30 % of the
1955–2016 average. This would considerably decrease the drought mitigation capacity
of the basin.
article_processing_charge: No
article_type: original
author:
- first_name: Álvaro
full_name: Ayala, Álvaro
last_name: Ayala
- first_name: David
full_name: Farías-Barahona, David
last_name: Farías-Barahona
- first_name: Matthias
full_name: Huss, Matthias
last_name: Huss
- first_name: Francesca
full_name: Pellicciotti, Francesca
id: b28f055a-81ea-11ed-b70c-a9fe7f7b0e70
last_name: Pellicciotti
- first_name: James
full_name: McPhee, James
last_name: McPhee
- first_name: Daniel
full_name: Farinotti, Daniel
last_name: Farinotti
citation:
ama: Ayala Á, Farías-Barahona D, Huss M, Pellicciotti F, McPhee J, Farinotti D.
Glacier runoff variations since 1955 in the Maipo River basin, in the semiarid
Andes of central Chile. The Cryosphere. 2020;14(6):2005-2027. doi:10.5194/tc-14-2005-2020
apa: Ayala, Á., Farías-Barahona, D., Huss, M., Pellicciotti, F., McPhee, J., &
Farinotti, D. (2020). Glacier runoff variations since 1955 in the Maipo River
basin, in the semiarid Andes of central Chile. The Cryosphere. Copernicus
Publications. https://doi.org/10.5194/tc-14-2005-2020
chicago: Ayala, Álvaro, David Farías-Barahona, Matthias Huss, Francesca Pellicciotti,
James McPhee, and Daniel Farinotti. “Glacier Runoff Variations since 1955 in the
Maipo River Basin, in the Semiarid Andes of Central Chile.” The Cryosphere.
Copernicus Publications, 2020. https://doi.org/10.5194/tc-14-2005-2020.
ieee: Á. Ayala, D. Farías-Barahona, M. Huss, F. Pellicciotti, J. McPhee, and D.
Farinotti, “Glacier runoff variations since 1955 in the Maipo River basin, in
the semiarid Andes of central Chile,” The Cryosphere, vol. 14, no. 6. Copernicus
Publications, pp. 2005–2027, 2020.
ista: Ayala Á, Farías-Barahona D, Huss M, Pellicciotti F, McPhee J, Farinotti D.
2020. Glacier runoff variations since 1955 in the Maipo River basin, in the semiarid
Andes of central Chile. The Cryosphere. 14(6), 2005–2027.
mla: Ayala, Álvaro, et al. “Glacier Runoff Variations since 1955 in the Maipo River
Basin, in the Semiarid Andes of Central Chile.” The Cryosphere, vol. 14,
no. 6, Copernicus Publications, 2020, pp. 2005–27, doi:10.5194/tc-14-2005-2020.
short: Á. Ayala, D. Farías-Barahona, M. Huss, F. Pellicciotti, J. McPhee, D. Farinotti,
The Cryosphere 14 (2020) 2005–2027.
date_created: 2023-02-20T08:12:36Z
date_published: 2020-06-24T00:00:00Z
date_updated: 2023-02-28T12:32:31Z
day: '24'
doi: 10.5194/tc-14-2005-2020
extern: '1'
intvolume: ' 14'
issue: '6'
keyword:
- Earth-Surface Processes
- Water Science and Technology
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.5194/tc-14-2005-2020
month: '06'
oa: 1
oa_version: Published Version
page: 2005-2027
publication: The Cryosphere
publication_identifier:
issn:
- 1994-0424
publication_status: published
publisher: Copernicus Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Glacier runoff variations since 1955 in the Maipo River basin, in the semiarid
Andes of central Chile
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14
year: '2020'
...
---
_id: '12597'
abstract:
- lang: eng
text: We examine the spatial patterns of near-surface air temperature (Ta) over
a melting glacier using a multi-annual dataset from McCall Glacier, Alaska. The
dataset consists of a 10-year (2005–2014) meteorological record along the glacier
centreline up to an upper glacier cirque, spanning an elevation difference of
900 m. We test the validity of on-glacier linear lapse rates, and a model that
calculates Ta based on the influence of katabatic winds and other heat sources
along the glacier flow line. During the coldest hours of each summer (10% of time),
average lapse rates across the entire glacier range from −4.7 to −6.7°C km−1,
with a strong relationship between Ta and elevation (R2 > 0.7). During warm conditions,
Ta shows more complex, non-linear patterns that are better explained by the flow
line-dependent model, reducing errors by up to 0.5°C compared with linear lapse
rates, although more uncertainty might be associated with these observations due
to occasionally poor sensor ventilation. We conclude that Ta spatial distribution
can vary significantly from year to year, and from one glacier section to another.
Importantly, extrapolations using linear lapse rates from the ablation zone might
lead to large underestimations of Ta on the upper glacier areas.
article_processing_charge: No
article_type: original
author:
- first_name: Patrick
full_name: Troxler, Patrick
last_name: Troxler
- first_name: Álvaro
full_name: Ayala, Álvaro
last_name: Ayala
- first_name: Thomas E.
full_name: Shaw, Thomas E.
last_name: Shaw
- first_name: Matt
full_name: Nolan, Matt
last_name: Nolan
- first_name: Ben W.
full_name: Brock, Ben W.
last_name: Brock
- first_name: Francesca
full_name: Pellicciotti, Francesca
id: b28f055a-81ea-11ed-b70c-a9fe7f7b0e70
last_name: Pellicciotti
citation:
ama: Troxler P, Ayala Á, Shaw TE, Nolan M, Brock BW, Pellicciotti F. Modelling spatial
patterns of near-surface air temperature over a decade of melt seasons on McCall
Glacier, Alaska. Journal of Glaciology. 2020;66(257):386-400. doi:10.1017/jog.2020.12
apa: Troxler, P., Ayala, Á., Shaw, T. E., Nolan, M., Brock, B. W., & Pellicciotti,
F. (2020). Modelling spatial patterns of near-surface air temperature over a decade
of melt seasons on McCall Glacier, Alaska. Journal of Glaciology. Cambridge
University Press. https://doi.org/10.1017/jog.2020.12
chicago: Troxler, Patrick, Álvaro Ayala, Thomas E. Shaw, Matt Nolan, Ben W. Brock,
and Francesca Pellicciotti. “Modelling Spatial Patterns of Near-Surface Air Temperature
over a Decade of Melt Seasons on McCall Glacier, Alaska.” Journal of Glaciology.
Cambridge University Press, 2020. https://doi.org/10.1017/jog.2020.12.
ieee: P. Troxler, Á. Ayala, T. E. Shaw, M. Nolan, B. W. Brock, and F. Pellicciotti,
“Modelling spatial patterns of near-surface air temperature over a decade of melt
seasons on McCall Glacier, Alaska,” Journal of Glaciology, vol. 66, no.
257. Cambridge University Press, pp. 386–400, 2020.
ista: Troxler P, Ayala Á, Shaw TE, Nolan M, Brock BW, Pellicciotti F. 2020. Modelling
spatial patterns of near-surface air temperature over a decade of melt seasons
on McCall Glacier, Alaska. Journal of Glaciology. 66(257), 386–400.
mla: Troxler, Patrick, et al. “Modelling Spatial Patterns of Near-Surface Air Temperature
over a Decade of Melt Seasons on McCall Glacier, Alaska.” Journal of Glaciology,
vol. 66, no. 257, Cambridge University Press, 2020, pp. 386–400, doi:10.1017/jog.2020.12.
short: P. Troxler, Á. Ayala, T.E. Shaw, M. Nolan, B.W. Brock, F. Pellicciotti, Journal
of Glaciology 66 (2020) 386–400.
date_created: 2023-02-20T08:12:42Z
date_published: 2020-06-01T00:00:00Z
date_updated: 2023-02-28T12:28:45Z
day: '01'
doi: 10.1017/jog.2020.12
extern: '1'
intvolume: ' 66'
issue: '257'
keyword:
- Earth-Surface Processes
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1017/jog.2020.12
month: '06'
oa: 1
oa_version: Published Version
page: 386-400
publication: Journal of Glaciology
publication_identifier:
eissn:
- 1727-5652
issn:
- 0022-1430
publication_status: published
publisher: Cambridge University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Modelling spatial patterns of near-surface air temperature over a decade of
melt seasons on McCall Glacier, Alaska
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 66
year: '2020'
...
---
_id: '12598'
abstract:
- lang: eng
text: Obtaining detailed information about high mountain snowpacks is often limited
by insufficient ground-based observations and uncertainty in the (re)distribution
of solid precipitation. We utilize high-resolution optical images from Pléiades
satellites to generate a snow depth map, at a spatial resolution of 4 m, for a
high mountain catchment of central Chile. Results are negatively biased (median
difference of −0.22 m) when compared against observations from a terrestrial Light
Detection And Ranging scan, though replicate general snow depth variability well.
Additionally, the Pléiades dataset is subject to data gaps (17% of total pixels),
negative values for shallow snow (12%), and noise on slopes >40–50° (2%). We correct
and filter the Pléiades snow depths using surface classification techniques of
snow-free areas and a random forest model for data gap filling. Snow depths (with
an estimated error of ~0.36 m) average 1.66 m and relate well to topographical
parameters such as elevation and northness in a similar way to previous studies.
However, estimations of snow depth based upon topography (TOPO) or physically
based modeling (DBSM) cannot resolve localized processes (i.e., avalanching or
wind scouring) that are detected by Pléiades, even when forced with locally calibrated
data. Comparing these alternative model approaches to corrected Pléiades snow
depths reveals total snow volume differences between −28% (DBSM) and +54% (TOPO)
for the catchment and large differences across most elevation bands. Pléiades
represents an important contribution to understanding snow accumulation at sparsely
monitored catchments, though ideally requires a careful systematic validation
procedure to identify catchment-scale biases and errors in the snow depth derivation.
article_number: e2019WR024880
article_processing_charge: No
article_type: original
author:
- first_name: Thomas E.
full_name: Shaw, Thomas E.
last_name: Shaw
- first_name: Simon
full_name: Gascoin, Simon
last_name: Gascoin
- first_name: Pablo A.
full_name: Mendoza, Pablo A.
last_name: Mendoza
- first_name: Francesca
full_name: Pellicciotti, Francesca
id: b28f055a-81ea-11ed-b70c-a9fe7f7b0e70
last_name: Pellicciotti
- first_name: James
full_name: McPhee, James
last_name: McPhee
citation:
ama: Shaw TE, Gascoin S, Mendoza PA, Pellicciotti F, McPhee J. Snow depth patterns
in a high mountain Andean catchment from satellite optical tristereoscopic remote
sensing. Water Resources Research. 2020;56(2). doi:10.1029/2019wr024880
apa: Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., & McPhee, J.
(2020). Snow depth patterns in a high mountain Andean catchment from satellite
optical tristereoscopic remote sensing. Water Resources Research. American
Geophysical Union. https://doi.org/10.1029/2019wr024880
chicago: Shaw, Thomas E., Simon Gascoin, Pablo A. Mendoza, Francesca Pellicciotti,
and James McPhee. “Snow Depth Patterns in a High Mountain Andean Catchment from
Satellite Optical Tristereoscopic Remote Sensing.” Water Resources Research.
American Geophysical Union, 2020. https://doi.org/10.1029/2019wr024880.
ieee: T. E. Shaw, S. Gascoin, P. A. Mendoza, F. Pellicciotti, and J. McPhee, “Snow
depth patterns in a high mountain Andean catchment from satellite optical tristereoscopic
remote sensing,” Water Resources Research, vol. 56, no. 2. American Geophysical
Union, 2020.
ista: Shaw TE, Gascoin S, Mendoza PA, Pellicciotti F, McPhee J. 2020. Snow depth
patterns in a high mountain Andean catchment from satellite optical tristereoscopic
remote sensing. Water Resources Research. 56(2), e2019WR024880.
mla: Shaw, Thomas E., et al. “Snow Depth Patterns in a High Mountain Andean Catchment
from Satellite Optical Tristereoscopic Remote Sensing.” Water Resources Research,
vol. 56, no. 2, e2019WR024880, American Geophysical Union, 2020, doi:10.1029/2019wr024880.
short: T.E. Shaw, S. Gascoin, P.A. Mendoza, F. Pellicciotti, J. McPhee, Water Resources
Research 56 (2020).
date_created: 2023-02-20T08:12:47Z
date_published: 2020-02-01T00:00:00Z
date_updated: 2023-02-28T12:26:14Z
day: '01'
doi: 10.1029/2019wr024880
extern: '1'
intvolume: ' 56'
issue: '2'
keyword:
- Water Science and Technology
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1029/2019WR024880
month: '02'
oa: 1
oa_version: Published Version
publication: Water Resources Research
publication_identifier:
eissn:
- 1944-7973
issn:
- 0043-1397
publication_status: published
publisher: American Geophysical Union
quality_controlled: '1'
scopus_import: '1'
status: public
title: Snow depth patterns in a high mountain Andean catchment from satellite optical
tristereoscopic remote sensing
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 56
year: '2020'
...