---
_id: '6354'
abstract:
- lang: eng
text: Blood platelets are critical for hemostasis and thrombosis, but also play
diverse roles during immune responses. We have recently reported that platelets
migrate at sites of infection in vitro and in vivo. Importantly, platelets use
their ability to migrate to collect and bundle fibrin (ogen)-bound bacteria accomplishing
efficient intravascular bacterial trapping. Here, we describe a method that allows
analyzing platelet migration in vitro, focusing on their ability to collect bacteria
and trap bacteria under flow.
acknowledgement: ' FöFoLe project 947 (F.G.), the Friedrich-Baur-Stiftung project
41/16 (F.G.)'
article_number: e3018
author:
- first_name: Shuxia
full_name: Fan, Shuxia
last_name: Fan
- first_name: Michael
full_name: Lorenz, Michael
last_name: Lorenz
- first_name: Steffen
full_name: Massberg, Steffen
last_name: Massberg
- first_name: Florian R
full_name: Gärtner, Florian R
id: 397A88EE-F248-11E8-B48F-1D18A9856A87
last_name: Gärtner
orcid: 0000-0001-6120-3723
citation:
ama: Fan S, Lorenz M, Massberg S, Gärtner FR. Platelet migration and bacterial trapping
assay under flow. Bio-Protocol. 2018;8(18). doi:10.21769/bioprotoc.3018
apa: Fan, S., Lorenz, M., Massberg, S., & Gärtner, F. R. (2018). Platelet migration
and bacterial trapping assay under flow. Bio-Protocol. Bio-Protocol. https://doi.org/10.21769/bioprotoc.3018
chicago: Fan, Shuxia, Michael Lorenz, Steffen Massberg, and Florian R Gärtner. “Platelet
Migration and Bacterial Trapping Assay under Flow.” Bio-Protocol. Bio-Protocol,
2018. https://doi.org/10.21769/bioprotoc.3018.
ieee: S. Fan, M. Lorenz, S. Massberg, and F. R. Gärtner, “Platelet migration and
bacterial trapping assay under flow,” Bio-Protocol, vol. 8, no. 18. Bio-Protocol,
2018.
ista: Fan S, Lorenz M, Massberg S, Gärtner FR. 2018. Platelet migration and bacterial
trapping assay under flow. Bio-Protocol. 8(18), e3018.
mla: Fan, Shuxia, et al. “Platelet Migration and Bacterial Trapping Assay under
Flow.” Bio-Protocol, vol. 8, no. 18, e3018, Bio-Protocol, 2018, doi:10.21769/bioprotoc.3018.
short: S. Fan, M. Lorenz, S. Massberg, F.R. Gärtner, Bio-Protocol 8 (2018).
date_created: 2019-04-29T09:40:33Z
date_published: 2018-09-20T00:00:00Z
date_updated: 2021-01-12T08:07:12Z
day: '20'
ddc:
- '570'
department:
- _id: MiSi
doi: 10.21769/bioprotoc.3018
ec_funded: 1
file:
- access_level: open_access
checksum: d4588377e789da7f360b553ae02c5119
content_type: application/pdf
creator: dernst
date_created: 2019-04-30T08:04:33Z
date_updated: 2020-07-14T12:47:28Z
file_id: '6360'
file_name: 2018_BioProtocol_Fan.pdf
file_size: 2928337
relation: main_file
file_date_updated: 2020-07-14T12:47:28Z
has_accepted_license: '1'
intvolume: ' 8'
issue: '18'
keyword:
- Platelets
- Cell migration
- Bacteria
- Shear flow
- Fibrinogen
- E. coli
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 260AA4E2-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '747687'
name: Mechanical Adaptation of Lamellipodial Actin Networks in Migrating Cells
publication: Bio-Protocol
publication_identifier:
issn:
- 2331-8325
publication_status: published
publisher: Bio-Protocol
quality_controlled: '1'
status: public
title: Platelet migration and bacterial trapping assay under flow
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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 8
year: '2018'
...
---
_id: '6459'
author:
- first_name: Barbara
full_name: Petritsch, Barbara
id: 406048EC-F248-11E8-B48F-1D18A9856A87
last_name: Petritsch
orcid: 0000-0003-2724-4614
citation:
ama: Petritsch B. Open Access at IST Austria 2009-2017. IST Austria; 2018.
doi:10.5281/zenodo.1410279
apa: 'Petritsch, B. (2018). Open Access at IST Austria 2009-2017. Presented
at the Open-Access-Tage, Graz, Austria: IST Austria. https://doi.org/10.5281/zenodo.1410279'
chicago: Petritsch, Barbara. Open Access at IST Austria 2009-2017. IST Austria,
2018. https://doi.org/10.5281/zenodo.1410279.
ieee: B. Petritsch, Open Access at IST Austria 2009-2017. IST Austria, 2018.
ista: Petritsch B. 2018. Open Access at IST Austria 2009-2017, IST Austria,p.
mla: Petritsch, Barbara. Open Access at IST Austria 2009-2017. IST Austria,
2018, doi:10.5281/zenodo.1410279.
short: B. Petritsch, Open Access at IST Austria 2009-2017, IST Austria, 2018.
conference:
end_date: 2018-09-26
location: Graz, Austria
name: Open-Access-Tage
start_date: 2018-09-24
date_created: 2019-05-16T07:27:14Z
date_published: 2018-09-24T00:00:00Z
date_updated: 2020-07-14T23:06:21Z
day: '24'
ddc:
- '020'
department:
- _id: E-Lib
doi: 10.5281/zenodo.1410279
file:
- access_level: open_access
checksum: 9063ab4d10ea93353c3a03bbf53fbcf1
content_type: application/pdf
creator: dernst
date_created: 2019-05-16T07:26:25Z
date_updated: 2020-07-14T12:47:30Z
file_id: '6460'
file_name: Poster_Beitrag_125_Petritsch.pdf
file_size: 1967778
relation: main_file
file_date_updated: 2020-07-14T12:47:30Z
has_accepted_license: '1'
keyword:
- Open Access
- Publication Analysis
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
publication_status: published
publisher: IST Austria
status: public
title: Open Access at IST Austria 2009-2017
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference_poster
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '690'
abstract:
- lang: eng
text: We consider spectral properties and the edge universality of sparse random
matrices, the class of random matrices that includes the adjacency matrices of
the Erdős–Rényi graph model G(N, p). We prove a local law for the eigenvalue density
up to the spectral edges. Under a suitable condition on the sparsity, we also
prove that the rescaled extremal eigenvalues exhibit GOE Tracy–Widom fluctuations
if a deterministic shift of the spectral edge due to the sparsity is included.
For the adjacency matrix of the Erdős–Rényi graph this establishes the Tracy–Widom
fluctuations of the second largest eigenvalue when p is much larger than N−2/3
with a deterministic shift of order (Np)−1.
article_number: 543-616
author:
- first_name: Jii
full_name: Lee, Jii
last_name: Lee
- first_name: Kevin
full_name: Schnelli, Kevin
id: 434AD0AE-F248-11E8-B48F-1D18A9856A87
last_name: Schnelli
orcid: 0000-0003-0954-3231
citation:
ama: Lee J, Schnelli K. Local law and Tracy–Widom limit for sparse random matrices.
Probability Theory and Related Fields. 2018;171(1-2). doi:10.1007/s00440-017-0787-8
apa: Lee, J., & Schnelli, K. (2018). Local law and Tracy–Widom limit for sparse
random matrices. Probability Theory and Related Fields. Springer. https://doi.org/10.1007/s00440-017-0787-8
chicago: Lee, Jii, and Kevin Schnelli. “Local Law and Tracy–Widom Limit for Sparse
Random Matrices.” Probability Theory and Related Fields. Springer, 2018.
https://doi.org/10.1007/s00440-017-0787-8.
ieee: J. Lee and K. Schnelli, “Local law and Tracy–Widom limit for sparse random
matrices,” Probability Theory and Related Fields, vol. 171, no. 1–2. Springer,
2018.
ista: Lee J, Schnelli K. 2018. Local law and Tracy–Widom limit for sparse random
matrices. Probability Theory and Related Fields. 171(1–2), 543–616.
mla: Lee, Jii, and Kevin Schnelli. “Local Law and Tracy–Widom Limit for Sparse Random
Matrices.” Probability Theory and Related Fields, vol. 171, no. 1–2, 543–616,
Springer, 2018, doi:10.1007/s00440-017-0787-8.
short: J. Lee, K. Schnelli, Probability Theory and Related Fields 171 (2018).
date_created: 2018-12-11T11:47:56Z
date_published: 2018-06-14T00:00:00Z
date_updated: 2021-01-12T08:09:33Z
day: '14'
department:
- _id: LaEr
doi: 10.1007/s00440-017-0787-8
ec_funded: 1
external_id:
arxiv:
- '1605.08767'
intvolume: ' 171'
issue: 1-2
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1605.08767
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 258DCDE6-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '338804'
name: Random matrices, universality and disordered quantum systems
publication: Probability Theory and Related Fields
publication_status: published
publisher: Springer
publist_id: '7017'
quality_controlled: '1'
scopus_import: 1
status: public
title: Local law and Tracy–Widom limit for sparse random matrices
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 171
year: '2018'
...
---
_id: '703'
abstract:
- lang: eng
text: We consider the NP-hard problem of MAP-inference for undirected discrete graphical
models. We propose a polynomial time and practically efficient algorithm for finding
a part of its optimal solution. Specifically, our algorithm marks some labels
of the considered graphical model either as (i) optimal, meaning that they belong
to all optimal solutions of the inference problem; (ii) non-optimal if they provably
do not belong to any solution. With access to an exact solver of a linear programming
relaxation to the MAP-inference problem, our algorithm marks the maximal possible
(in a specified sense) number of labels. We also present a version of the algorithm,
which has access to a suboptimal dual solver only and still can ensure the (non-)optimality
for the marked labels, although the overall number of the marked labels may decrease.
We propose an efficient implementation, which runs in time comparable to a single
run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art
results on computational benchmarks from machine learning and computer vision.
author:
- first_name: Alexander
full_name: Shekhovtsov, Alexander
last_name: Shekhovtsov
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Bogdan
full_name: Savchynskyy, Bogdan
last_name: Savchynskyy
citation:
ama: Shekhovtsov A, Swoboda P, Savchynskyy B. Maximum persistency via iterative
relaxed inference with graphical models. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 2018;40(7):1668-1682. doi:10.1109/TPAMI.2017.2730884
apa: Shekhovtsov, A., Swoboda, P., & Savchynskyy, B. (2018). Maximum persistency
via iterative relaxed inference with graphical models. IEEE Transactions on
Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2017.2730884
chicago: Shekhovtsov, Alexander, Paul Swoboda, and Bogdan Savchynskyy. “Maximum
Persistency via Iterative Relaxed Inference with Graphical Models.” IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2017.2730884.
ieee: A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative
relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.
ista: Shekhovtsov A, Swoboda P, Savchynskyy B. 2018. Maximum persistency via iterative
relaxed inference with graphical models. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 40(7), 1668–1682.
mla: Shekhovtsov, Alexander, et al. “Maximum Persistency via Iterative Relaxed Inference
with Graphical Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 40, no. 7, IEEE, 2018, pp. 1668–82, doi:10.1109/TPAMI.2017.2730884.
short: A. Shekhovtsov, P. Swoboda, B. Savchynskyy, IEEE Transactions on Pattern
Analysis and Machine Intelligence 40 (2018) 1668–1682.
date_created: 2018-12-11T11:48:01Z
date_published: 2018-07-01T00:00:00Z
date_updated: 2021-01-12T08:11:32Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/TPAMI.2017.2730884
external_id:
arxiv:
- '1508.07902'
intvolume: ' 40'
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1508.07902
month: '07'
oa: 1
oa_version: Preprint
page: 1668-1682
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- '01628828'
publication_status: published
publisher: IEEE
publist_id: '6992'
quality_controlled: '1'
scopus_import: 1
status: public
title: Maximum persistency via iterative relaxed inference with graphical models
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 40
year: '2018'
...
---
_id: '7116'
abstract:
- lang: eng
text: 'Training deep learning models has received tremendous research interest recently.
In particular, there has been intensive research on reducing the communication
cost of training when using multiple computational devices, through reducing the
precision of the underlying data representation. Naturally, such methods induce
system trade-offs—lowering communication precision could de-crease communication
overheads and improve scalability; but, on the other hand, it can also reduce
the accuracy of training. In this paper, we study this trade-off space, and ask:Can
low-precision communication consistently improve the end-to-end performance of
training modern neural networks, with no accuracy loss?From the performance point
of view, the answer to this question may appear deceptively easy: compressing
communication through low precision should help when the ratio between communication
and computation is high. However, this answer is less straightforward when we
try to generalize this principle across various neural network architectures (e.g.,
AlexNet vs. ResNet),number of GPUs (e.g., 2 vs. 8 GPUs), machine configurations(e.g.,
EC2 instances vs. NVIDIA DGX-1), communication primitives (e.g., MPI vs. NCCL),
and even different GPU architectures(e.g., Kepler vs. Pascal). Currently, it is
not clear how a realistic realization of all these factors maps to the speed up
provided by low-precision communication. In this paper, we conduct an empirical
study to answer this question and report the insights.'
article_processing_charge: No
author:
- first_name: Demjan
full_name: Grubic, Demjan
last_name: Grubic
- first_name: Leo
full_name: Tam, Leo
last_name: Tam
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Ce
full_name: Zhang, Ce
last_name: Zhang
citation:
ama: 'Grubic D, Tam L, Alistarh D-A, Zhang C. Synchronous multi-GPU training for
deep learning with low-precision communications: An empirical study. In: Proceedings
of the 21st International Conference on Extending Database Technology. OpenProceedings;
2018:145-156. doi:10.5441/002/EDBT.2018.14'
apa: 'Grubic, D., Tam, L., Alistarh, D.-A., & Zhang, C. (2018). Synchronous
multi-GPU training for deep learning with low-precision communications: An empirical
study. In Proceedings of the 21st International Conference on Extending Database
Technology (pp. 145–156). Vienna, Austria: OpenProceedings. https://doi.org/10.5441/002/EDBT.2018.14'
chicago: 'Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “Synchronous
Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical
Study.” In Proceedings of the 21st International Conference on Extending Database
Technology, 145–56. OpenProceedings, 2018. https://doi.org/10.5441/002/EDBT.2018.14.'
ieee: 'D. Grubic, L. Tam, D.-A. Alistarh, and C. Zhang, “Synchronous multi-GPU training
for deep learning with low-precision communications: An empirical study,” in Proceedings
of the 21st International Conference on Extending Database Technology, Vienna,
Austria, 2018, pp. 145–156.'
ista: 'Grubic D, Tam L, Alistarh D-A, Zhang C. 2018. Synchronous multi-GPU training
for deep learning with low-precision communications: An empirical study. Proceedings
of the 21st International Conference on Extending Database Technology. EDBT: Conference
on Extending Database Technology, 145–156.'
mla: 'Grubic, Demjan, et al. “Synchronous Multi-GPU Training for Deep Learning with
Low-Precision Communications: An Empirical Study.” Proceedings of the 21st
International Conference on Extending Database Technology, OpenProceedings,
2018, pp. 145–56, doi:10.5441/002/EDBT.2018.14.'
short: D. Grubic, L. Tam, D.-A. Alistarh, C. Zhang, in:, Proceedings of the 21st
International Conference on Extending Database Technology, OpenProceedings, 2018,
pp. 145–156.
conference:
end_date: 2018-03-29
location: Vienna, Austria
name: 'EDBT: Conference on Extending Database Technology'
start_date: 2018-03-26
date_created: 2019-11-26T14:19:11Z
date_published: 2018-03-26T00:00:00Z
date_updated: 2023-02-23T12:59:17Z
day: '26'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.5441/002/EDBT.2018.14
file:
- access_level: open_access
checksum: ec979b56abc71016d6e6adfdadbb4afe
content_type: application/pdf
creator: dernst
date_created: 2019-11-26T14:23:04Z
date_updated: 2020-07-14T12:47:49Z
file_id: '7118'
file_name: 2018_OpenProceedings_Grubic.pdf
file_size: 1603204
relation: main_file
file_date_updated: 2020-07-14T12:47:49Z
has_accepted_license: '1'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 145-156
publication: Proceedings of the 21st International Conference on Extending Database
Technology
publication_identifier:
isbn:
- '9783893180783'
issn:
- 2367-2005
publication_status: published
publisher: OpenProceedings
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Synchronous multi-GPU training for deep learning with low-precision communications:
An empirical study'
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...