---
_id: '9929'
abstract:
- lang: eng
text: 'The evolution of assortative mating is a key part of the speciation process.
Stronger assortment, or greater divergence in mating traits, between species pairs
with overlapping ranges is commonly observed, but possible causes of this pattern
of reproductive character displacement are difficult to distinguish. We use a
multidisciplinary approach to provide a rare example where it is possible to distinguish
among hypotheses concerning the evolution of reproductive character displacement.
We build on an earlier comparative analysis that illustrated a strong pattern
of greater divergence in penis form between pairs of sister species with overlapping
ranges than between allopatric sister-species pairs, in a large clade of marine
gastropods (Littorinidae). We investigate both assortative mating and divergence
in male genitalia in one of the sister-species pairs, discriminating among three
contrasting processes each of which can generate a pattern of reproductive character
displacement: reinforcement, reproductive interference and the Templeton effect.
We demonstrate reproductive character displacement in assortative mating, but
not in genital form between this pair of sister species and use demographic models
to distinguish among the different processes. Our results support a model with
no gene flow since secondary contact and thus favour reproductive interference
as the cause of reproductive character displacement for mate choice, rather than
reinforcement. High gene flow within species argues against the Templeton effect.
Secondary contact appears to have had little impact on genital divergence.'
article_processing_charge: No
author:
- first_name: Johan
full_name: Hollander, Johan
last_name: Hollander
- first_name: Mauricio
full_name: Montaño-Rendón, Mauricio
last_name: Montaño-Rendón
- first_name: Giuseppe
full_name: Bianco, Giuseppe
last_name: Bianco
- first_name: Xi
full_name: Yang, Xi
last_name: Yang
- first_name: Anja M
full_name: Westram, Anja M
id: 3C147470-F248-11E8-B48F-1D18A9856A87
last_name: Westram
orcid: 0000-0003-1050-4969
- first_name: Ludovic
full_name: Duvaux, Ludovic
last_name: Duvaux
- first_name: David G.
full_name: Reid, David G.
last_name: Reid
- first_name: Roger K.
full_name: Butlin, Roger K.
last_name: Butlin
citation:
ama: 'Hollander J, Montaño-Rendón M, Bianco G, et al. Data from: Are assortative
mating and genital divergence driven by reinforcement? 2018. doi:10.5061/dryad.51sd2p5'
apa: 'Hollander, J., Montaño-Rendón, M., Bianco, G., Yang, X., Westram, A. M., Duvaux,
L., … Butlin, R. K. (2018). Data from: Are assortative mating and genital divergence
driven by reinforcement? Dryad. https://doi.org/10.5061/dryad.51sd2p5'
chicago: 'Hollander, Johan, Mauricio Montaño-Rendón, Giuseppe Bianco, Xi Yang, Anja
M Westram, Ludovic Duvaux, David G. Reid, and Roger K. Butlin. “Data from: Are
Assortative Mating and Genital Divergence Driven by Reinforcement?” Dryad, 2018.
https://doi.org/10.5061/dryad.51sd2p5.'
ieee: 'J. Hollander et al., “Data from: Are assortative mating and genital
divergence driven by reinforcement?” Dryad, 2018.'
ista: 'Hollander J, Montaño-Rendón M, Bianco G, Yang X, Westram AM, Duvaux L, Reid
DG, Butlin RK. 2018. Data from: Are assortative mating and genital divergence
driven by reinforcement?, Dryad, 10.5061/dryad.51sd2p5.'
mla: 'Hollander, Johan, et al. Data from: Are Assortative Mating and Genital
Divergence Driven by Reinforcement? Dryad, 2018, doi:10.5061/dryad.51sd2p5.'
short: J. Hollander, M. Montaño-Rendón, G. Bianco, X. Yang, A.M. Westram, L. Duvaux,
D.G. Reid, R.K. Butlin, (2018).
date_created: 2021-08-17T08:51:06Z
date_published: 2018-10-17T00:00:00Z
date_updated: 2023-09-19T15:08:53Z
day: '17'
department:
- _id: BeVi
doi: 10.5061/dryad.51sd2p5
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.51sd2p5
month: '10'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '9915'
relation: used_in_publication
status: public
status: public
title: 'Data from: Are assortative mating and genital divergence driven by reinforcement?'
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2018'
...
---
_id: '10882'
abstract:
- lang: eng
text: 'We introduce Intelligent Annotation Dialogs for bounding box annotation.
We train an agent to automatically choose a sequence of actions for a human annotator
to produce a bounding box in a minimal amount of time. Specifically, we consider
two actions: box verification [34], where the annotator verifies a box generated
by an object detector, and manual box drawing. We explore two kinds of agents,
one based on predicting the probability that a box will be positively verified,
and the other based on reinforcement learning. We demonstrate that (1) our agents
are able to learn efficient annotation strategies in several scenarios, automatically
adapting to the image difficulty, the desired quality of the boxes, and the detector
strength; (2) in all scenarios the resulting annotation dialogs speed up annotation
compared to manual box drawing alone and box verification alone, while also outperforming
any fixed combination of verification and drawing in most scenarios; (3) in a
realistic scenario where the detector is iteratively re-trained, our agents evolve
a series of strategies that reflect the shifting trade-off between verification
and drawing as the detector grows stronger.'
article_processing_charge: No
author:
- first_name: Jasper
full_name: Uijlings, Jasper
last_name: Uijlings
- first_name: Ksenia
full_name: Konyushkova, Ksenia
last_name: Konyushkova
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. Learning intelligent dialogs
for bounding box annotation. In: 2018 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. IEEE; 2018:9175-9184. doi:10.1109/cvpr.2018.00956'
apa: 'Uijlings, J., Konyushkova, K., Lampert, C., & Ferrari, V. (2018). Learning
intelligent dialogs for bounding box annotation. In 2018 IEEE/CVF Conference
on Computer Vision and Pattern Recognition (pp. 9175–9184). Salt Lake City,
UT, United States: IEEE. https://doi.org/10.1109/cvpr.2018.00956'
chicago: Uijlings, Jasper, Ksenia Konyushkova, Christoph Lampert, and Vittorio Ferrari.
“Learning Intelligent Dialogs for Bounding Box Annotation.” In 2018 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 9175–84. IEEE, 2018.
https://doi.org/10.1109/cvpr.2018.00956.
ieee: J. Uijlings, K. Konyushkova, C. Lampert, and V. Ferrari, “Learning intelligent
dialogs for bounding box annotation,” in 2018 IEEE/CVF Conference on Computer
Vision and Pattern Recognition, Salt Lake City, UT, United States, 2018, pp.
9175–9184.
ista: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. 2018. Learning intelligent
dialogs for bounding box annotation. 2018 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. CVF: Conference on Computer Vision and Pattern Recognition,
9175–9184.'
mla: Uijlings, Jasper, et al. “Learning Intelligent Dialogs for Bounding Box Annotation.”
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE,
2018, pp. 9175–84, doi:10.1109/cvpr.2018.00956.
short: J. Uijlings, K. Konyushkova, C. Lampert, V. Ferrari, in:, 2018 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–9184.
conference:
end_date: 2018-06-23
location: Salt Lake City, UT, United States
name: 'CVF: Conference on Computer Vision and Pattern Recognition'
start_date: 2018-06-18
date_created: 2022-03-18T12:45:09Z
date_published: 2018-12-17T00:00:00Z
date_updated: 2023-09-19T15:11:49Z
day: '17'
department:
- _id: ChLa
doi: 10.1109/cvpr.2018.00956
external_id:
arxiv:
- '1712.08087'
isi:
- '000457843609036'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.1712.08087'
month: '12'
oa: 1
oa_version: Preprint
page: 9175-9184
publication: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781538664209'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning intelligent dialogs for bounding box annotation
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '6558'
abstract:
- lang: eng
text: This paper studies the problem of distributed stochastic optimization in an
adversarial setting where, out of m machines which allegedly compute stochastic
gradients every iteration, an α-fraction are Byzantine, and may behave adversarially.
Our main result is a variant of stochastic gradient descent (SGD) which finds
ε-approximate minimizers of convex functions in T=O~(1/ε²m+α²/ε²) iterations.
In contrast, traditional mini-batch SGD needs T=O(1/ε²m) iterations, but cannot
tolerate Byzantine failures. Further, we provide a lower bound showing that, up
to logarithmic factors, our algorithm is information-theoretically optimal both
in terms of sample complexity and time complexity.
article_processing_charge: No
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: Zeyuan
full_name: Allen-Zhu, Zeyuan
last_name: Allen-Zhu
- first_name: Jerry
full_name: Li, Jerry
last_name: Li
citation:
ama: 'Alistarh D-A, Allen-Zhu Z, Li J. Byzantine stochastic gradient descent. In:
Advances in Neural Information Processing Systems. Vol 2018. Neural Information
Processing Systems Foundation; 2018:4613-4623.'
apa: 'Alistarh, D.-A., Allen-Zhu, Z., & Li, J. (2018). Byzantine stochastic
gradient descent. In Advances in Neural Information Processing Systems
(Vol. 2018, pp. 4613–4623). Montreal, Canada: Neural Information Processing Systems
Foundation.'
chicago: Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic
Gradient Descent.” In Advances in Neural Information Processing Systems,
2018:4613–23. Neural Information Processing Systems Foundation, 2018.
ieee: D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine stochastic gradient descent,”
in Advances in Neural Information Processing Systems, Montreal, Canada,
2018, vol. 2018, pp. 4613–4623.
ista: 'Alistarh D-A, Allen-Zhu Z, Li J. 2018. Byzantine stochastic gradient descent.
Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural
Information Processing Systems vol. 2018, 4613–4623.'
mla: Alistarh, Dan-Adrian, et al. “Byzantine Stochastic Gradient Descent.” Advances
in Neural Information Processing Systems, vol. 2018, Neural Information Processing
Systems Foundation, 2018, pp. 4613–23.
short: D.-A. Alistarh, Z. Allen-Zhu, J. Li, in:, Advances in Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2018, pp.
4613–4623.
conference:
end_date: 2018-12-08
location: Montreal, Canada
name: 'NeurIPS: Conference on Neural Information Processing Systems'
start_date: 2018-12-02
date_created: 2019-06-13T08:22:37Z
date_published: 2018-12-01T00:00:00Z
date_updated: 2023-09-19T15:12:45Z
day: '01'
department:
- _id: DaAl
external_id:
arxiv:
- '1803.08917'
isi:
- '000461823304061'
intvolume: ' 2018'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1803.08917
month: '12'
oa: 1
oa_version: Published Version
page: 4613-4623
publication: Advances in Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Byzantine stochastic gradient descent
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2018
year: '2018'
...
---
_id: '6032'
abstract:
- lang: eng
text: The main result of this article is a generalization of the classical blossom
algorithm for finding perfect matchings. Our algorithm can efficiently solve Boolean
CSPs where each variable appears in exactly two constraints (we call it edge CSP)
and all constraints are even Δ-matroid relations (represented by lists of tuples).
As a consequence of this, we settle the complexity classification of planar Boolean
CSPs started by Dvorak and Kupec. Using a reduction to even Δ-matroids, we then
extend the tractability result to larger classes of Δ-matroids that we call efficiently
coverable. It properly includes classes that were known to be tractable before,
namely, co-independent, compact, local, linear, and binary, with the following
caveat:We represent Δ-matroids by lists of tuples, while the last two use a representation
by matrices. Since an n ×n matrix can represent exponentially many tuples, our
tractability result is not strictly stronger than the known algorithm for linear
and binary Δ-matroids.
article_number: '22'
article_processing_charge: No
article_type: original
author:
- first_name: Alexandr
full_name: Kazda, Alexandr
id: 3B32BAA8-F248-11E8-B48F-1D18A9856A87
last_name: Kazda
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Michal
full_name: Rolinek, Michal
id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87
last_name: Rolinek
citation:
ama: Kazda A, Kolmogorov V, Rolinek M. Even delta-matroids and the complexity of
planar boolean CSPs. ACM Transactions on Algorithms. 2018;15(2). doi:10.1145/3230649
apa: Kazda, A., Kolmogorov, V., & Rolinek, M. (2018). Even delta-matroids and
the complexity of planar boolean CSPs. ACM Transactions on Algorithms.
ACM. https://doi.org/10.1145/3230649
chicago: Kazda, Alexandr, Vladimir Kolmogorov, and Michal Rolinek. “Even Delta-Matroids
and the Complexity of Planar Boolean CSPs.” ACM Transactions on Algorithms.
ACM, 2018. https://doi.org/10.1145/3230649.
ieee: A. Kazda, V. Kolmogorov, and M. Rolinek, “Even delta-matroids and the complexity
of planar boolean CSPs,” ACM Transactions on Algorithms, vol. 15, no. 2.
ACM, 2018.
ista: Kazda A, Kolmogorov V, Rolinek M. 2018. Even delta-matroids and the complexity
of planar boolean CSPs. ACM Transactions on Algorithms. 15(2), 22.
mla: Kazda, Alexandr, et al. “Even Delta-Matroids and the Complexity of Planar Boolean
CSPs.” ACM Transactions on Algorithms, vol. 15, no. 2, 22, ACM, 2018, doi:10.1145/3230649.
short: A. Kazda, V. Kolmogorov, M. Rolinek, ACM Transactions on Algorithms 15 (2018).
date_created: 2019-02-17T22:59:25Z
date_published: 2018-12-01T00:00:00Z
date_updated: 2023-09-20T11:20:26Z
day: '01'
department:
- _id: VlKo
doi: 10.1145/3230649
ec_funded: 1
external_id:
arxiv:
- '1602.03124'
isi:
- '000468036500007'
intvolume: ' 15'
isi: 1
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1602.03124
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: ACM Transactions on Algorithms
publication_status: published
publisher: ACM
quality_controlled: '1'
related_material:
record:
- id: '1192'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Even delta-matroids and the complexity of planar boolean CSPs
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 15
year: '2018'
...
---
_id: '200'
abstract:
- lang: eng
text: This thesis is concerned with the inference of current population structure
based on geo-referenced genetic data. The underlying idea is that population structure
affects its spatial genetic structure. Therefore, genotype information can be
utilized to estimate important demographic parameters such as migration rates.
These indirect estimates of population structure have become very attractive,
as genotype data is now widely available. However, there also has been much concern
about these approaches. Importantly, genetic structure can be influenced by many
complex patterns, which often cannot be disentangled. Moreover, many methods merely
fit heuristic patterns of genetic structure, and do not build upon population
genetics theory. Here, I describe two novel inference methods that address these
shortcomings. In Chapter 2, I introduce an inference scheme based on a new type
of signal, identity by descent (IBD) blocks. Recently, it has become feasible
to detect such long blocks of genome shared between pairs of samples. These blocks
are direct traces of recent coalescence events. As such, they contain ample signal
for inferring recent demography. I examine sharing of IBD blocks in two-dimensional
populations with local migration. Using a diffusion approximation, I derive formulas
for an isolation by distance pattern of long IBD blocks and show that sharing
of long IBD blocks approaches rapid exponential decay for growing sample distance.
I describe an inference scheme based on these results. It can robustly estimate
the dispersal rate and population density, which is demonstrated on simulated
data. I also show an application to estimate mean migration and the rate of recent
population growth within Eastern Europe. Chapter 3 is about a novel method to
estimate barriers to gene flow in a two dimensional population. This inference
scheme utilizes geographically localized allele frequency fluctuations - a classical
isolation by distance signal. The strength of these local fluctuations increases
on average next to a barrier, and there is less correlation across it. I again
use a framework of diffusion of ancestral lineages to model this effect, and provide
an efficient numerical implementation to fit the results to geo-referenced biallelic
SNP data. This inference scheme is able to robustly estimate strong barriers to
gene flow, as tests on simulated data confirm.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Harald
full_name: Ringbauer, Harald
id: 417FCFF4-F248-11E8-B48F-1D18A9856A87
last_name: Ringbauer
orcid: 0000-0002-4884-9682
citation:
ama: Ringbauer H. Inferring recent demography from spatial genetic structure. 2018.
doi:10.15479/AT:ISTA:th_963
apa: Ringbauer, H. (2018). Inferring recent demography from spatial genetic structure.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_963
chicago: Ringbauer, Harald. “Inferring Recent Demography from Spatial Genetic Structure.”
Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:th_963.
ieee: H. Ringbauer, “Inferring recent demography from spatial genetic structure,”
Institute of Science and Technology Austria, 2018.
ista: Ringbauer H. 2018. Inferring recent demography from spatial genetic structure.
Institute of Science and Technology Austria.
mla: Ringbauer, Harald. Inferring Recent Demography from Spatial Genetic Structure.
Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th_963.
short: H. Ringbauer, Inferring Recent Demography from Spatial Genetic Structure,
Institute of Science and Technology Austria, 2018.
date_created: 2018-12-11T11:45:10Z
date_published: 2018-02-21T00:00:00Z
date_updated: 2023-09-20T12:00:56Z
day: '21'
ddc:
- '576'
degree_awarded: PhD
department:
- _id: NiBa
doi: 10.15479/AT:ISTA:th_963
file:
- access_level: open_access
checksum: 8cc534d2b528ae017acf80874cce48c9
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:55Z
date_updated: 2020-07-14T12:45:23Z
file_id: '5111'
file_name: IST-2018-963-v1+1_thesis.pdf
file_size: 5792935
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checksum: 6af18d7e5a7e2728ceda2f41ee24f628
content_type: application/zip
creator: dernst
date_created: 2019-04-05T09:30:12Z
date_updated: 2020-07-14T12:45:23Z
file_id: '6224'
file_name: 2018_thesis_ringbauer_source.zip
file_size: 113365
relation: source_file
file_date_updated: 2020-07-14T12:45:23Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/4.0/
month: '02'
oa: 1
oa_version: Published Version
page: '146'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7713'
pubrep_id: '963'
related_material:
record:
- id: '563'
relation: part_of_dissertation
status: public
- id: '1074'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
title: Inferring recent demography from spatial genetic structure
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...