---
_id: '43'
abstract:
- lang: eng
text: 'The initial amount of pathogens required to start an infection within a susceptible
host is called the infective dose and is known to vary to a large extent between
different pathogen species. We investigate the hypothesis that the differences
in infective doses are explained by the mode of action in the underlying mechanism
of pathogenesis: Pathogens with locally acting mechanisms tend to have smaller
infective doses than pathogens with distantly acting mechanisms. While empirical
evidence tends to support the hypothesis, a formal theoretical explanation has
been lacking. We give simple analytical models to gain insight into this phenomenon
and also investigate a stochastic, spatially explicit, mechanistic within-host
model for toxin-dependent bacterial infections. The model shows that pathogens
secreting locally acting toxins have smaller infective doses than pathogens secreting
diffusive toxins, as hypothesized. While local pathogenetic mechanisms require
smaller infective doses, pathogens with distantly acting toxins tend to spread
faster and may cause more damage to the host. The proposed model can serve as
a basis for the spatially explicit analysis of various virulence factors also
in the context of other problems in infection dynamics.'
acknowledgement: J.R. and J.V.A. were also supported by the Academy of Finland Grants
1273253 and 267541.
article_processing_charge: No
author:
- first_name: Joel
full_name: Rybicki, Joel
id: 334EFD2E-F248-11E8-B48F-1D18A9856A87
last_name: Rybicki
orcid: 0000-0002-6432-6646
- first_name: Eva
full_name: Kisdi, Eva
last_name: Kisdi
- first_name: Jani
full_name: Anttila, Jani
last_name: Anttila
citation:
ama: Rybicki J, Kisdi E, Anttila J. Model of bacterial toxin-dependent pathogenesis
explains infective dose. Proceedings of the National Academy of Sciences of
the United States of America. 2018;115(42):10690-10695. doi:10.1073/pnas.1721061115
apa: Rybicki, J., Kisdi, E., & Anttila, J. (2018). Model of bacterial toxin-dependent
pathogenesis explains infective dose. Proceedings of the National Academy of
Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.1721061115
chicago: Rybicki, Joel, Eva Kisdi, and Jani Anttila. “Model of Bacterial Toxin-Dependent
Pathogenesis Explains Infective Dose.” Proceedings of the National Academy
of Sciences of the United States of America. National Academy of Sciences,
2018. https://doi.org/10.1073/pnas.1721061115.
ieee: J. Rybicki, E. Kisdi, and J. Anttila, “Model of bacterial toxin-dependent
pathogenesis explains infective dose,” Proceedings of the National Academy
of Sciences of the United States of America, vol. 115, no. 42. National Academy
of Sciences, pp. 10690–10695, 2018.
ista: Rybicki J, Kisdi E, Anttila J. 2018. Model of bacterial toxin-dependent pathogenesis
explains infective dose. Proceedings of the National Academy of Sciences of the
United States of America. 115(42), 10690–10695.
mla: Rybicki, Joel, et al. “Model of Bacterial Toxin-Dependent Pathogenesis Explains
Infective Dose.” Proceedings of the National Academy of Sciences of the United
States of America, vol. 115, no. 42, National Academy of Sciences, 2018, pp.
10690–95, doi:10.1073/pnas.1721061115.
short: J. Rybicki, E. Kisdi, J. Anttila, Proceedings of the National Academy of
Sciences of the United States of America 115 (2018) 10690–10695.
date_created: 2018-12-11T11:44:19Z
date_published: 2018-10-02T00:00:00Z
date_updated: 2025-06-03T11:16:28Z
day: '02'
ddc:
- '570'
- '577'
department:
- _id: DaAl
doi: 10.1073/pnas.1721061115
ec_funded: 1
external_id:
isi:
- '000447491300057'
file:
- access_level: open_access
checksum: df7ac544a587c06b75692653b9fabd18
content_type: application/pdf
creator: dernst
date_created: 2019-04-09T08:02:50Z
date_updated: 2020-07-14T12:46:26Z
file_id: '6258'
file_name: 2018_PNAS_Rybicki.pdf
file_size: 4070777
relation: main_file
file_date_updated: 2020-07-14T12:46:26Z
has_accepted_license: '1'
intvolume: ' 115'
isi: 1
issue: '42'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 10690 - 10695
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- 1091-6490
issn:
- 0027-8424
publication_status: published
publisher: National Academy of Sciences
publist_id: '8011'
pubrep_id: '1063'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Model of bacterial toxin-dependent pathogenesis explains infective dose
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 115
year: '2018'
...
---
_id: '5965'
abstract:
- lang: eng
text: Relaxed concurrent data structures have become increasingly popular, due to
their scalability in graph processing and machine learning applications (\citeNguyen13,
gonzalez2012powergraph ). Despite considerable interest, there exist families
of natural, high performing randomized relaxed concurrent data structures, such
as the popular MultiQueue~\citeMQ pattern for implementing relaxed priority queue
data structures, for which no guarantees are known in the concurrent setting~\citeAKLN17.
Our main contribution is in showing for the first time that, under a set of analytic
assumptions, a family of relaxed concurrent data structures, including variants
of MultiQueues, but also a new approximate counting algorithm we call the MultiCounter,
provides strong probabilistic guarantees on the degree of relaxation with respect
to the sequential specification, in arbitrary concurrent executions. We formalize
these guarantees via a new correctness condition called distributional linearizability,
tailored to concurrent implementations with randomized relaxations. Our result
is based on a new analysis of an asynchronous variant of the classic power-of-two-choices
load balancing algorithm, in which placement choices can be based on inconsistent,
outdated information (this result may be of independent interest). We validate
our results empirically, showing that the MultiCounter algorithm can implement
scalable relaxed timestamps.
article_processing_charge: No
arxiv: 1
author:
- 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: Trevor A
full_name: Brown, Trevor A
id: 3569F0A0-F248-11E8-B48F-1D18A9856A87
last_name: Brown
- first_name: Justin
full_name: Kopinsky, Justin
last_name: Kopinsky
- first_name: Jerry Z.
full_name: Li, Jerry Z.
last_name: Li
- first_name: Giorgi
full_name: Nadiradze, Giorgi
last_name: Nadiradze
citation:
ama: 'Alistarh D-A, Brown TA, Kopinsky J, Li JZ, Nadiradze G. Distributionally linearizable
data structures. In: Proceedings of the 30th on Symposium on Parallelism in
Algorithms and Architectures - SPAA ’18. ACM; 2018:133-142. doi:10.1145/3210377.3210411'
apa: 'Alistarh, D.-A., Brown, T. A., Kopinsky, J., Li, J. Z., & Nadiradze, G.
(2018). Distributionally linearizable data structures. In Proceedings of the
30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18
(pp. 133–142). Vienna, Austria: ACM. https://doi.org/10.1145/3210377.3210411'
chicago: Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, Jerry Z. Li, and
Giorgi Nadiradze. “Distributionally Linearizable Data Structures.” In Proceedings
of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA
’18, 133–42. ACM, 2018. https://doi.org/10.1145/3210377.3210411.
ieee: D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally
linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism
in Algorithms and Architectures - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
ista: 'Alistarh D-A, Brown TA, Kopinsky J, Li JZ, Nadiradze G. 2018. Distributionally
linearizable data structures. Proceedings of the 30th on Symposium on Parallelism
in Algorithms and Architectures - SPAA ’18. SPAA: Symposium on Parallelism in
Algorithms and Architectures, 133–142.'
mla: Alistarh, Dan-Adrian, et al. “Distributionally Linearizable Data Structures.”
Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures
- SPAA ’18, ACM, 2018, pp. 133–42, doi:10.1145/3210377.3210411.
short: D.-A. Alistarh, T.A. Brown, J. Kopinsky, J.Z. Li, G. Nadiradze, in:, Proceedings
of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA
’18, ACM, 2018, pp. 133–142.
conference:
end_date: 2018-07-18
location: Vienna, Austria
name: 'SPAA: Symposium on Parallelism in Algorithms and Architectures'
start_date: 2018-07-16
date_created: 2019-02-13T10:17:19Z
date_published: 2018-07-16T00:00:00Z
date_updated: 2026-04-08T07:00:45Z
day: '16'
department:
- _id: DaAl
doi: 10.1145/3210377.3210411
external_id:
arxiv:
- '1804.01018'
isi:
- '000545269600016'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1804.01018
month: '07'
oa: 1
oa_version: Preprint
page: 133-142
publication: Proceedings of the 30th on Symposium on Parallelism in Algorithms and
Architectures - SPAA '18
publication_identifier:
isbn:
- '9781450357999'
publication_status: published
publisher: ACM
quality_controlled: '1'
related_material:
record:
- id: '10429'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Distributionally linearizable data structures
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '536'
abstract:
- lang: eng
text: 'We consider the problem of consensus in the challenging classic model. In
this model, the adversary is adaptive; it can choose which processors crash at
any point during the course of the algorithm. Further, communication is via asynchronous
message passing: there is no known upper bound on the time to send a message from
one processor to another, and all messages and coin flips are seen by the adversary.
We describe a new randomized consensus protocol with expected message complexity
O(n2log2n) when fewer than n / 2 processes may fail by crashing. This is an almost-linear
improvement over the best previously known protocol, and within logarithmic factors
of a known Ω(n2) message lower bound. The protocol further ensures that no process
sends more than O(nlog3n) messages in expectation, which is again within logarithmic
factors of optimal. We also present a generalization of the algorithm to an arbitrary
number of failures t, which uses expected O(nt+t2log2t) total messages. Our approach
is to build a message-efficient, resilient mechanism for aggregating individual
processor votes, implementing the message-passing equivalent of a weak shared
coin. Roughly, in our protocol, a processor first announces its votes to small
groups, then propagates them to increasingly larger groups as it generates more
and more votes. To bound the number of messages that an individual process might
have to send or receive, the protocol progressively increases the weight of generated
votes. The main technical challenge is bounding the impact of votes that are still
“in flight” (generated, but not fully propagated) on the final outcome of the
shared coin, especially since such votes might have different weights. We achieve
this by leveraging the structure of the algorithm, and a technical argument based
on martingale concentration bounds. Overall, we show that it is possible to build
an efficient message-passing implementation of a shared coin, and in the process
(almost-optimally) solve the classic consensus problem in the asynchronous message-passing
model.'
article_processing_charge: Yes (via OA deal)
author:
- 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: James
full_name: Aspnes, James
last_name: Aspnes
- first_name: Valerie
full_name: King, Valerie
last_name: King
- first_name: Jared
full_name: Saia, Jared
last_name: Saia
citation:
ama: Alistarh D-A, Aspnes J, King V, Saia J. Communication-efficient randomized
consensus. Distributed Computing. 2018;31(6):489-501. doi:10.1007/s00446-017-0315-1
apa: Alistarh, D.-A., Aspnes, J., King, V., & Saia, J. (2018). Communication-efficient
randomized consensus. Distributed Computing. Springer. https://doi.org/10.1007/s00446-017-0315-1
chicago: Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient
Randomized Consensus.” Distributed Computing. Springer, 2018. https://doi.org/10.1007/s00446-017-0315-1.
ieee: D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient
randomized consensus,” Distributed Computing, vol. 31, no. 6. Springer,
pp. 489–501, 2018.
ista: Alistarh D-A, Aspnes J, King V, Saia J. 2018. Communication-efficient randomized
consensus. Distributed Computing. 31(6), 489–501.
mla: Alistarh, Dan-Adrian, et al. “Communication-Efficient Randomized Consensus.”
Distributed Computing, vol. 31, no. 6, Springer, 2018, pp. 489–501, doi:10.1007/s00446-017-0315-1.
short: D.-A. Alistarh, J. Aspnes, V. King, J. Saia, Distributed Computing 31 (2018)
489–501.
corr_author: '1'
date_created: 2018-12-11T11:47:01Z
date_published: 2018-11-01T00:00:00Z
date_updated: 2026-04-16T09:53:54Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.1007/s00446-017-0315-1
external_id:
isi:
- '000443832300005'
file:
- access_level: open_access
checksum: 69b46e537acdcac745237ddb853fcbb5
content_type: application/pdf
creator: dernst
date_created: 2019-01-22T07:25:51Z
date_updated: 2020-07-14T12:46:38Z
file_id: '5867'
file_name: 2017_DistribComp_Alistarh.pdf
file_size: 595707
relation: main_file
file_date_updated: 2020-07-14T12:46:38Z
has_accepted_license: '1'
intvolume: ' 31'
isi: 1
issue: '6'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 489-501
project:
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: Distributed Computing
publication_identifier:
issn:
- 0178-2770
publication_status: published
publisher: Springer
publist_id: '7281'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Communication-efficient randomized consensus
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 31
year: '2018'
...
---
_id: '85'
abstract:
- lang: eng
text: Concurrent accesses to shared data structures must be synchronized to avoid
data races. Coarse-grained synchronization, which locks the entire data structure,
is easy to implement but does not scale. Fine-grained synchronization can scale
well, but can be hard to reason about. Hand-over-hand locking, in which operations
are pipelined as they traverse the data structure, combines fine-grained synchronization
with ease of use. However, the traditional implementation suffers from inherent
overheads. This paper introduces snapshot-based synchronization (SBS), a novel
hand-over-hand locking mechanism. SBS decouples the synchronization state from
the data, significantly improving cache utilization. Further, it relies on guarantees
provided by pipelining to minimize synchronization that requires cross-thread
communication. Snapshot-based synchronization thus scales much better than traditional
hand-over-hand locking, while maintaining the same ease of use.
acknowledgement: Trevor Brown was supported in part by the ISF (grants 2005/17 & 1749/14)
and by a NSERC post-doctoral fellowship.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Eran
full_name: Gilad, Eran
last_name: Gilad
- first_name: Trevor A
full_name: Brown, Trevor A
id: 3569F0A0-F248-11E8-B48F-1D18A9856A87
last_name: Brown
- first_name: Mark
full_name: Oskin, Mark
last_name: Oskin
- first_name: Yoav
full_name: Etsion, Yoav
last_name: Etsion
citation:
ama: 'Gilad E, Brown TA, Oskin M, Etsion Y. Snapshot based synchronization: A fast
replacement for Hand-over-Hand locking. In: Vol 11014. Springer; 2018:465-479.
doi:10.1007/978-3-319-96983-1_33'
apa: 'Gilad, E., Brown, T. A., Oskin, M., & Etsion, Y. (2018). Snapshot based
synchronization: A fast replacement for Hand-over-Hand locking (Vol. 11014, pp.
465–479). Presented at the Euro-Par: European Conference on Parallel Processing,
Turin, Italy: Springer. https://doi.org/10.1007/978-3-319-96983-1_33'
chicago: 'Gilad, Eran, Trevor A Brown, Mark Oskin, and Yoav Etsion. “Snapshot Based
Synchronization: A Fast Replacement for Hand-over-Hand Locking,” 11014:465–79.
Springer, 2018. https://doi.org/10.1007/978-3-319-96983-1_33.'
ieee: 'E. Gilad, T. A. Brown, M. Oskin, and Y. Etsion, “Snapshot based synchronization:
A fast replacement for Hand-over-Hand locking,” presented at the Euro-Par: European
Conference on Parallel Processing, Turin, Italy, 2018, vol. 11014, pp. 465–479.'
ista: 'Gilad E, Brown TA, Oskin M, Etsion Y. 2018. Snapshot based synchronization:
A fast replacement for Hand-over-Hand locking. Euro-Par: European Conference on
Parallel Processing, LNCS, vol. 11014, 465–479.'
mla: 'Gilad, Eran, et al. Snapshot Based Synchronization: A Fast Replacement
for Hand-over-Hand Locking. Vol. 11014, Springer, 2018, pp. 465–79, doi:10.1007/978-3-319-96983-1_33.'
short: E. Gilad, T.A. Brown, M. Oskin, Y. Etsion, in:, Springer, 2018, pp. 465–479.
conference:
end_date: 2018-08-31
location: Turin, Italy
name: 'Euro-Par: European Conference on Parallel Processing'
start_date: 2018-08-27
date_created: 2018-12-11T11:44:33Z
date_published: 2018-08-01T00:00:00Z
date_updated: 2026-04-16T09:53:41Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.1007/978-3-319-96983-1_33
external_id:
isi:
- '000851042300031'
file:
- access_level: open_access
checksum: 13a3f250be8878405e791b53c19722ad
content_type: application/pdf
creator: dernst
date_created: 2019-02-12T07:40:40Z
date_updated: 2020-07-14T12:48:14Z
file_id: '5954'
file_name: 2018_Brown.pdf
file_size: 665372
relation: main_file
file_date_updated: 2020-07-14T12:48:14Z
has_accepted_license: '1'
intvolume: ' 11014'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Preprint
page: 465 - 479
project:
- _id: 26450934-B435-11E9-9278-68D0E5697425
name: NSERC Postdoctoral fellowship
publication_identifier:
issn:
- 0302-9743
publication_status: published
publisher: Springer
publist_id: '7969'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Snapshot based synchronization: A fast replacement for Hand-over-Hand locking'
type: conference
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 11014
year: '2018'
...
---
_id: '791'
abstract:
- lang: eng
text: 'Consider the following random process: we are given n queues, into which
elements of increasing labels are inserted uniformly at random. To remove an element,
we pick two queues at random, and remove the element of lower label (higher priority)
among the two. The cost of a removal is the rank of the label removed, among labels
still present in any of the queues, that is, the distance from the optimal choice
at each step. Variants of this strategy are prevalent in state-of-the-art concurrent
priority queue implementations. Nonetheless, it is not known whether such implementations
provide any rank guarantees, even in a sequential model. We answer this question,
showing that this strategy provides surprisingly strong guarantees: Although the
single-choice process, where we always insert and remove from a single randomly
chosen queue, has degrading cost, going to infinity as we increase the number
of steps, in the two choice process, the expected rank of a removed element is
O(n) while the expected worst-case cost is O(n log n). These bounds are tight,
and hold irrespective of the number of steps for which we run the process. The
argument is based on a new technical connection between "heavily loaded"
balls-into-bins processes and priority scheduling. Our analytic results inspire
a new concurrent priority queue implementation, which improves upon the state
of the art in terms of practical performance.'
article_processing_charge: No
arxiv: 1
author:
- 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: Justin
full_name: Kopinsky, Justin
last_name: Kopinsky
- first_name: Jerry
full_name: Li, Jerry
last_name: Li
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
orcid: 0000-0001-5634-0731
citation:
ama: 'Alistarh D-A, Kopinsky J, Li J, Nadiradze G. The power of choice in priority
scheduling. In: Proceedings of the ACM Symposium on Principles of Distributed
Computing. Vol Part F129314. ACM; 2017:283-292. doi:10.1145/3087801.3087810'
apa: 'Alistarh, D.-A., Kopinsky, J., Li, J., & Nadiradze, G. (2017). The power
of choice in priority scheduling. In Proceedings of the ACM Symposium on Principles
of Distributed Computing (Vol. Part F129314, pp. 283–292). Washington, WA,
USA: ACM. https://doi.org/10.1145/3087801.3087810'
chicago: Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze.
“The Power of Choice in Priority Scheduling.” In Proceedings of the ACM Symposium
on Principles of Distributed Computing, Part F129314:283–92. ACM, 2017. https://doi.org/10.1145/3087801.3087810.
ieee: D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice
in priority scheduling,” in Proceedings of the ACM Symposium on Principles
of Distributed Computing, Washington, WA, USA, 2017, vol. Part F129314, pp.
283–292.
ista: 'Alistarh D-A, Kopinsky J, Li J, Nadiradze G. 2017. The power of choice in
priority scheduling. Proceedings of the ACM Symposium on Principles of Distributed
Computing. PODC: Principles of Distributed Computing vol. Part F129314, 283–292.'
mla: Alistarh, Dan-Adrian, et al. “The Power of Choice in Priority Scheduling.”
Proceedings of the ACM Symposium on Principles of Distributed Computing,
vol. Part F129314, ACM, 2017, pp. 283–92, doi:10.1145/3087801.3087810.
short: D.-A. Alistarh, J. Kopinsky, J. Li, G. Nadiradze, in:, Proceedings of the
ACM Symposium on Principles of Distributed Computing, ACM, 2017, pp. 283–292.
conference:
end_date: 2017-07-27
location: Washington, WA, USA
name: 'PODC: Principles of Distributed Computing'
start_date: 2017-07-25
date_created: 2018-12-11T11:48:31Z
date_published: 2017-07-26T00:00:00Z
date_updated: 2025-06-04T09:44:47Z
day: '26'
department:
- _id: DaAl
doi: 10.1145/3087801.3087810
external_id:
arxiv:
- '1706.04178'
isi:
- '000462995000035'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1706.04178
month: '07'
oa: 1
oa_version: Submitted Version
page: 283 - 292
publication: Proceedings of the ACM Symposium on Principles of Distributed Computing
publication_identifier:
isbn:
- 978-145034992-5
publication_status: published
publisher: ACM
publist_id: '6864'
quality_controlled: '1'
scopus_import: '1'
status: public
title: The power of choice in priority scheduling
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: Part F129314
year: '2017'
...
---
_id: '487'
abstract:
- lang: eng
text: In this paper we study network architecture for unlicensed cellular networking
for outdoor coverage in TV white spaces. The main technology proposed for TV white
spaces is 802.11af, a Wi-Fi variant adapted for TV frequencies. However, 802.11af
is originally designed for improved indoor propagation. We show that long links,
typical for outdoor use, exacerbate known Wi-Fi issues, such as hidden and exposed
terminal, and significantly reduce its efficiency. Instead, we propose CellFi,
an alternative architecture based on LTE. LTE is designed for long-range coverage
and throughput efficiency, but it is also designed to operate in tightly controlled
and centrally managed networks. CellFi overcomes these problems by designing an
LTE-compatible spectrum database component, mandatory for TV white space networking,
and introducing an interference management component for distributed coordination.
CellFi interference management is compatible with existing LTE mechanisms, requires
no explicit communication between base stations, and is more efficient than CSMA
for long links. We evaluate our design through extensive real world evaluation
on of-the-shelf LTE equipment and simulations. We show that, compared to 802.11af,
it increases coverage by 40% and reduces median flow completion times by 2.3x.
article_processing_charge: No
author:
- first_name: Ghufran
full_name: Baig, Ghufran
last_name: Baig
- first_name: Bozidar
full_name: Radunovic, Bozidar
last_name: Radunovic
- 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: Matthew
full_name: Balkwill, Matthew
last_name: Balkwill
- first_name: Thomas
full_name: Karagiannis, Thomas
last_name: Karagiannis
- first_name: Lili
full_name: Qiu, Lili
last_name: Qiu
citation:
ama: 'Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. Towards
unlicensed cellular networks in TV white spaces. In: Proceedings of the 2017
13th International Conference on Emerging Networking EXperiments and Technologies.
ACM; 2017:2-14. doi:10.1145/3143361.3143367'
apa: 'Baig, G., Radunovic, B., Alistarh, D.-A., Balkwill, M., Karagiannis, T., &
Qiu, L. (2017). Towards unlicensed cellular networks in TV white spaces. In Proceedings
of the 2017 13th International Conference on emerging Networking EXperiments and
Technologies (pp. 2–14). Incheon, South Korea: ACM. https://doi.org/10.1145/3143361.3143367'
chicago: Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill,
Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV
White Spaces.” In Proceedings of the 2017 13th International Conference on
Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017. https://doi.org/10.1145/3143361.3143367.
ieee: G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L.
Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings
of the 2017 13th International Conference on emerging Networking EXperiments and
Technologies, Incheon, South Korea, 2017, pp. 2–14.
ista: 'Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. 2017.
Towards unlicensed cellular networks in TV white spaces. Proceedings of the 2017
13th International Conference on emerging Networking EXperiments and Technologies.
CoNEXT: Conference on emerging Networking EXperiments and Technologies, 2–14.'
mla: Baig, Ghufran, et al. “Towards Unlicensed Cellular Networks in TV White Spaces.”
Proceedings of the 2017 13th International Conference on Emerging Networking
EXperiments and Technologies, ACM, 2017, pp. 2–14, doi:10.1145/3143361.3143367.
short: G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, L. Qiu,
in:, Proceedings of the 2017 13th International Conference on Emerging Networking
EXperiments and Technologies, ACM, 2017, pp. 2–14.
conference:
end_date: 2017-12-15
location: Incheon, South Korea
name: 'CoNEXT: Conference on emerging Networking EXperiments and Technologies'
start_date: 2017-12-12
corr_author: '1'
date_created: 2018-12-11T11:46:45Z
date_published: 2017-11-28T00:00:00Z
date_updated: 2025-09-18T09:50:43Z
day: '28'
department:
- _id: DaAl
doi: 10.1145/3143361.3143367
external_id:
isi:
- '000526087500002'
isi: 1
language:
- iso: eng
month: '11'
oa_version: None
page: 2 - 14
publication: Proceedings of the 2017 13th International Conference on emerging Networking
EXperiments and Technologies
publication_identifier:
isbn:
- 978-145035422-6
publication_status: published
publisher: ACM
publist_id: '7333'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Towards unlicensed cellular networks in TV white spaces
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2017'
...
---
_id: '431'
abstract:
- lang: eng
text: 'Parallel implementations of stochastic gradient descent (SGD) have received
significant research attention, thanks to its excellent scalability properties.
A fundamental barrier when parallelizing SGD is the high bandwidth cost of communicating
gradient updates between nodes; consequently, several lossy compresion heuristics
have been proposed, by which nodes only communicate quantized gradients. Although
effective in practice, these heuristics do not always converge. In this paper,
we propose Quantized SGD (QSGD), a family of compression schemes with convergence
guarantees and good practical performance. QSGD allows the user to smoothly trade
off communication bandwidth and convergence time: nodes can adjust the number
of bits sent per iteration, at the cost of possibly higher variance. We show that
this trade-off is inherent, in the sense that improving it past some threshold
would violate information-theoretic lower bounds. QSGD guarantees convergence
for convex and non-convex objectives, under asynchrony, and can be extended to
stochastic variance-reduced techniques. When applied to training deep neural networks
for image classification and automated speech recognition, QSGD leads to significant
reductions in end-to-end training time. For instance, on 16GPUs, we can train
the ResNet-152 network to full accuracy on ImageNet 1.8 × faster than the full-precision
variant. '
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- 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: Demjan
full_name: Grubic, Demjan
last_name: Grubic
- first_name: Jerry
full_name: Li, Jerry
last_name: Li
- first_name: Ryota
full_name: Tomioka, Ryota
last_name: Tomioka
- first_name: Milan
full_name: Vojnović, Milan
last_name: Vojnović
citation:
ama: 'Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnović M. QSGD: Communication-efficient
SGD via gradient quantization and encoding. In: Vol 2017. Neural Information Processing
Systems Foundation; 2017:1710-1721.'
apa: 'Alistarh, D.-A., Grubic, D., Li, J., Tomioka, R., & Vojnović, M. (2017).
QSGD: Communication-efficient SGD via gradient quantization and encoding (Vol.
2017, pp. 1710–1721). Presented at the NIPS: Neural Information Processing System,
Long Beach, CA, United States: Neural Information Processing Systems Foundation.'
chicago: 'Alistarh, Dan-Adrian, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan
Vojnović. “QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding,”
2017:1710–21. Neural Information Processing Systems Foundation, 2017.'
ieee: 'D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnović, “QSGD: Communication-efficient
SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information
Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.'
ista: 'Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnović M. 2017. QSGD: Communication-efficient
SGD via gradient quantization and encoding. NIPS: Neural Information Processing
System, Advances in Neural Information Processing Systems, vol. 2017, 1710–1721.'
mla: 'Alistarh, Dan-Adrian, et al. QSGD: Communication-Efficient SGD via Gradient
Quantization and Encoding. Vol. 2017, Neural Information Processing Systems
Foundation, 2017, pp. 1710–21.'
short: D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, M. Vojnović, in:, Neural Information
Processing Systems Foundation, 2017, pp. 1710–1721.
conference:
end_date: 2017-12-09
location: Long Beach, CA, United States
name: 'NIPS: Neural Information Processing System'
start_date: 2017-12-04
corr_author: '1'
date_created: 2018-12-11T11:46:26Z
date_published: 2017-01-01T00:00:00Z
date_updated: 2025-09-18T10:07:20Z
day: '01'
department:
- _id: DaAl
external_id:
arxiv:
- '1610.02132'
isi:
- '000452649401072'
intvolume: ' 2017'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1610.02132
month: '01'
oa: 1
oa_version: Submitted Version
page: 1710-1721
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
publist_id: '7392'
quality_controlled: '1'
status: public
title: 'QSGD: Communication-efficient SGD via gradient quantization and encoding'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2017
year: '2017'
...
---
_id: '432'
abstract:
- lang: eng
text: 'Recently there has been significant interest in training machine-learning
models at low precision: by reducing precision, one can reduce computation and
communication by one order of magnitude. We examine training at reduced precision,
both from a theoretical and practical perspective, and ask: is it possible to
train models at end-to-end low precision with provable guarantees? Can this lead
to consistent order-of-magnitude speedups? We mainly focus on linear models, and
the answer is yes for linear models. We develop a simple framework called ZipML
based on one simple but novel strategy called double sampling. Our ZipML framework
is able to execute training at low precision with no bias, guaranteeing convergence,
whereas naive quanti- zation would introduce significant bias. We val- idate our
framework across a range of applica- tions, and show that it enables an FPGA proto-
type that is up to 6.5 × faster than an implemen- tation using full 32-bit precision.
We further de- velop a variance-optimal stochastic quantization strategy and show
that it can make a significant difference in a variety of settings. When applied
to linear models together with double sampling, we save up to another 1.7 × in
data movement compared with uniform quantization. When training deep networks
with quantized models, we achieve higher accuracy than the state-of-the- art XNOR-Net. '
alternative_title:
- PMLR Press
article_processing_charge: No
author:
- first_name: Hantian
full_name: Zhang, Hantian
last_name: Zhang
- first_name: Jerry
full_name: Li, Jerry
last_name: Li
- first_name: Kaan
full_name: Kara, Kaan
last_name: Kara
- 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: Ji
full_name: Liu, Ji
last_name: Liu
- first_name: Ce
full_name: Zhang, Ce
last_name: Zhang
citation:
ama: 'Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. ZipML: Training linear
models with end-to-end low precision, and a little bit of deep learning. In: Proceedings
of Machine Learning Research. Vol 70. ML Research Press; 2017:4035-4043.'
apa: 'Zhang, H., Li, J., Kara, K., Alistarh, D.-A., Liu, J., & Zhang, C. (2017).
ZipML: Training linear models with end-to-end low precision, and a little bit
of deep learning. In Proceedings of Machine Learning Research (Vol. 70,
pp. 4035–4043). Sydney, Australia: ML Research Press.'
chicago: 'Zhang, Hantian, Jerry Li, Kaan Kara, Dan-Adrian Alistarh, Ji Liu, and
Ce Zhang. “ZipML: Training Linear Models with End-to-End Low Precision, and a
Little Bit of Deep Learning.” In Proceedings of Machine Learning Research,
70:4035–43. ML Research Press, 2017.'
ieee: 'H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, and C. Zhang, “ZipML: Training
linear models with end-to-end low precision, and a little bit of deep learning,”
in Proceedings of Machine Learning Research, Sydney, Australia, 2017, vol.
70, pp. 4035–4043.'
ista: 'Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. 2017. ZipML: Training
linear models with end-to-end low precision, and a little bit of deep learning.
Proceedings of Machine Learning Research. ICML: International Conference on Machine
Learning, PMLR Press, vol. 70, 4035–4043.'
mla: 'Zhang, Hantian, et al. “ZipML: Training Linear Models with End-to-End Low
Precision, and a Little Bit of Deep Learning.” Proceedings of Machine Learning
Research, vol. 70, ML Research Press, 2017, pp. 4035–43.'
short: H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, C. Zhang, in:, Proceedings
of Machine Learning Research, ML Research Press, 2017, pp. 4035–4043.
conference:
end_date: 2017-08-11
location: Sydney, Australia
name: 'ICML: International Conference on Machine Learning'
start_date: 2017-08-06
corr_author: '1'
date_created: 2018-12-11T11:46:26Z
date_published: 2017-01-01T00:00:00Z
date_updated: 2025-09-18T10:06:02Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
external_id:
isi:
- '000683309504015'
file:
- access_level: open_access
checksum: 86156ba7f4318e47cef3eb9092593c10
content_type: application/pdf
creator: dernst
date_created: 2019-01-22T08:23:58Z
date_updated: 2020-07-14T12:46:26Z
file_id: '5869'
file_name: 2017_ICML_Zhang.pdf
file_size: 849345
relation: main_file
file_date_updated: 2020-07-14T12:46:26Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Submitted Version
page: 4035 - 4043
publication: Proceedings of Machine Learning Research
publication_identifier:
isbn:
- 978-151085514-4
publication_status: published
publisher: ML Research Press
publist_id: '7391'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'ZipML: Training linear models with end-to-end low precision, and a little
bit of deep learning'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: ' 70'
year: '2017'
...