--- _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 relation: main_file - access_level: closed 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' ...