[{"publication_status":"published","file":[{"access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_id":"5326","checksum":"3c0dcacc900bc45cc65a453dfda4ca43","creator":"system","date_updated":"2020-07-14T12:45:19Z","file_size":1546914,"date_created":"2018-12-12T10:18:07Z","file_name":"IST-2015-329-v1+1_manuscript.pdf"}],"language":[{"iso":"eng"}],"issue":"5","volume":372,"ec_funded":1,"abstract":[{"text":"Entomopathogenic fungi are potent biocontrol agents that are widely used against insect pests, many of which are social insects. Nevertheless, theoretical investigations of their particular life history are scarce. We develop a model that takes into account the main distinguishing features between traditionally studied diseases and obligate killing pathogens, like the (biocontrol-relevant) insect-pathogenic fungi Metarhizium and Beauveria. First, obligate killing entomopathogenic fungi produce new infectious particles (conidiospores) only after host death and not yet on the living host. Second, the killing rates of entomopathogenic fungi depend strongly on the initial exposure dosage, thus we explicitly consider the pathogen load of individual hosts. Further, we make the model applicable not only to solitary host species, but also to group living species by incorporating social interactions between hosts, like the collective disease defences of insect societies. Our results identify the optimal killing rate for the pathogen that minimises its invasion threshold. Furthermore, we find that the rate of contact between hosts has an ambivalent effect: dense interaction networks between individuals are considered to facilitate disease outbreaks because of increased pathogen transmission. In social insects, this is compensated by their collective disease defences, i.e., social immunity. For the type of pathogens considered here, we show that even without social immunity, high contact rates between live individuals dilute the pathogen in the host colony and hence can reduce individual pathogen loads below disease-causing levels.","lang":"eng"}],"oa_version":"Submitted Version","scopus_import":1,"month":"05","intvolume":" 372","date_updated":"2021-01-12T06:53:37Z","ddc":["576"],"file_date_updated":"2020-07-14T12:45:19Z","department":[{"_id":"NiBa"},{"_id":"SyCr"}],"_id":"1850","type":"journal_article","status":"public","pubrep_id":"329","has_accepted_license":"1","year":"2015","day":"07","publication":"Journal of Theoretical Biology","page":"54 - 64","doi":"10.1016/j.jtbi.2015.02.018","date_published":"2015-05-07T00:00:00Z","date_created":"2018-12-11T11:54:21Z","quality_controlled":"1","publisher":"Elsevier","oa":1,"citation":{"mla":"Novak, Sebastian, and Sylvia Cremer. “Fungal Disease Dynamics in Insect Societies: Optimal Killing Rates and the Ambivalent Effect of High Social Interaction Rates.” Journal of Theoretical Biology, vol. 372, no. 5, Elsevier, 2015, pp. 54–64, doi:10.1016/j.jtbi.2015.02.018.","apa":"Novak, S., & Cremer, S. (2015). Fungal disease dynamics in insect societies: Optimal killing rates and the ambivalent effect of high social interaction rates. Journal of Theoretical Biology. Elsevier. https://doi.org/10.1016/j.jtbi.2015.02.018","ama":"Novak S, Cremer S. Fungal disease dynamics in insect societies: Optimal killing rates and the ambivalent effect of high social interaction rates. Journal of Theoretical Biology. 2015;372(5):54-64. doi:10.1016/j.jtbi.2015.02.018","short":"S. Novak, S. Cremer, Journal of Theoretical Biology 372 (2015) 54–64.","ieee":"S. Novak and S. Cremer, “Fungal disease dynamics in insect societies: Optimal killing rates and the ambivalent effect of high social interaction rates,” Journal of Theoretical Biology, vol. 372, no. 5. Elsevier, pp. 54–64, 2015.","chicago":"Novak, Sebastian, and Sylvia Cremer. “Fungal Disease Dynamics in Insect Societies: Optimal Killing Rates and the Ambivalent Effect of High Social Interaction Rates.” Journal of Theoretical Biology. Elsevier, 2015. https://doi.org/10.1016/j.jtbi.2015.02.018.","ista":"Novak S, Cremer S. 2015. Fungal disease dynamics in insect societies: Optimal killing rates and the ambivalent effect of high social interaction rates. Journal of Theoretical Biology. 372(5), 54–64."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5251","author":[{"full_name":"Novak, Sebastian","last_name":"Novak","first_name":"Sebastian","id":"461468AE-F248-11E8-B48F-1D18A9856A87"},{"id":"2F64EC8C-F248-11E8-B48F-1D18A9856A87","first_name":"Sylvia","last_name":"Cremer","full_name":"Cremer, Sylvia","orcid":"0000-0002-2193-3868"}],"title":"Fungal disease dynamics in insect societies: Optimal killing rates and the ambivalent effect of high social interaction rates","project":[{"grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"call_identifier":"FP7","_id":"25DC711C-B435-11E9-9278-68D0E5697425","name":"Social Vaccination in Ant Colonies: from Individual Mechanisms to Society Effects","grant_number":"243071"}]},{"has_accepted_license":"1","year":"2015","day":"09","publication":"Evolution","page":"1015 - 1026","doi":"10.1111/evo.12618","date_published":"2015-02-09T00:00:00Z","date_created":"2018-12-11T11:54:21Z","publisher":"Wiley","quality_controlled":"1","oa":1,"citation":{"short":"T. Priklopil, E. Kisdi, M. Gyllenberg, Evolution 69 (2015) 1015–1026.","ieee":"T. Priklopil, E. Kisdi, and M. Gyllenberg, “Evolutionarily stable mating decisions for sequentially searching females and the stability of reproductive isolation by assortative mating,” Evolution, vol. 69, no. 4. Wiley, pp. 1015–1026, 2015.","ama":"Priklopil T, Kisdi E, Gyllenberg M. Evolutionarily stable mating decisions for sequentially searching females and the stability of reproductive isolation by assortative mating. Evolution. 2015;69(4):1015-1026. doi:10.1111/evo.12618","apa":"Priklopil, T., Kisdi, E., & Gyllenberg, M. (2015). Evolutionarily stable mating decisions for sequentially searching females and the stability of reproductive isolation by assortative mating. Evolution. Wiley. https://doi.org/10.1111/evo.12618","mla":"Priklopil, Tadeas, et al. “Evolutionarily Stable Mating Decisions for Sequentially Searching Females and the Stability of Reproductive Isolation by Assortative Mating.” Evolution, vol. 69, no. 4, Wiley, 2015, pp. 1015–26, doi:10.1111/evo.12618.","ista":"Priklopil T, Kisdi E, Gyllenberg M. 2015. Evolutionarily stable mating decisions for sequentially searching females and the stability of reproductive isolation by assortative mating. Evolution. 69(4), 1015–1026.","chicago":"Priklopil, Tadeas, Eva Kisdi, and Mats Gyllenberg. “Evolutionarily Stable Mating Decisions for Sequentially Searching Females and the Stability of Reproductive Isolation by Assortative Mating.” Evolution. Wiley, 2015. https://doi.org/10.1111/evo.12618."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5249","author":[{"id":"3C869AA0-F248-11E8-B48F-1D18A9856A87","first_name":"Tadeas","last_name":"Priklopil","full_name":"Priklopil, Tadeas"},{"last_name":"Kisdi","full_name":"Kisdi, Eva","first_name":"Eva"},{"last_name":"Gyllenberg","full_name":"Gyllenberg, Mats","first_name":"Mats"}],"article_processing_charge":"No","external_id":{"pmid":["25662095"]},"title":"Evolutionarily stable mating decisions for sequentially searching females and the stability of reproductive isolation by assortative mating","project":[{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"publication_identifier":{"issn":["0014-3820"],"eissn":["1558-5646"]},"publication_status":"published","file":[{"date_created":"2020-05-15T09:05:34Z","file_name":"2015_Evolution_Priklopil.pdf","date_updated":"2020-07-14T12:45:19Z","file_size":967214,"creator":"dernst","file_id":"7855","checksum":"1e8be0b1d7598a78cd2623d8ee8e7798","content_type":"application/pdf","access_level":"open_access","relation":"main_file"}],"language":[{"iso":"eng"}],"volume":69,"issue":"4","ec_funded":1,"abstract":[{"text":"We consider mating strategies for females who search for males sequentially during a season of limited length. We show that the best strategy rejects a given male type if encountered before a time-threshold but accepts him after. For frequency-independent benefits, we obtain the optimal time-thresholds explicitly for both discrete and continuous distributions of males, and allow for mistakes being made in assessing the correct male type. When the benefits are indirect (genes for the offspring) and the population is under frequency-dependent ecological selection, the benefits depend on the mating strategy of other females as well. This case is particularly relevant to speciation models that seek to explore the stability of reproductive isolation by assortative mating under frequency-dependent ecological selection. We show that the indirect benefits are to be quantified by the reproductive values of couples, and describe how the evolutionarily stable time-thresholds can be found. We conclude with an example based on the Levene model, in which we analyze the evolutionarily stable assortative mating strategies and the strength of reproductive isolation provided by them.","lang":"eng"}],"pmid":1,"oa_version":"Submitted Version","scopus_import":"1","month":"02","intvolume":" 69","date_updated":"2022-06-07T10:52:37Z","ddc":["570"],"file_date_updated":"2020-07-14T12:45:19Z","department":[{"_id":"NiBa"},{"_id":"KrCh"}],"_id":"1851","article_type":"original","type":"journal_article","status":"public"},{"department":[{"_id":"VlKo"},{"_id":"ChLa"}],"title":"A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle","author":[{"last_name":"Shah","full_name":"Shah, Neel","id":"31ABAF80-F248-11E8-B48F-1D18A9856A87","first_name":"Neel"},{"full_name":"Kolmogorov, Vladimir","last_name":"Kolmogorov","id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87","first_name":"Vladimir"},{"orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph"}],"publist_id":"5240","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Shah, Neel, Vladimir Kolmogorov, and Christoph Lampert. “A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly Max-Oracle,” 2737–45. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298890.","ista":"Shah N, Kolmogorov V, Lampert C. 2015. A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle. CVPR: Computer Vision and Pattern Recognition, 2737–2745.","mla":"Shah, Neel, et al. A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly Max-Oracle. IEEE, 2015, pp. 2737–45, doi:10.1109/CVPR.2015.7298890.","short":"N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745.","ieee":"N. Shah, V. Kolmogorov, and C. Lampert, “A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 2737–2745.","apa":"Shah, N., Kolmogorov, V., & Lampert, C. (2015). A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle (pp. 2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE. https://doi.org/10.1109/CVPR.2015.7298890","ama":"Shah N, Kolmogorov V, Lampert C. A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle. In: IEEE; 2015:2737-2745. doi:10.1109/CVPR.2015.7298890"},"date_updated":"2021-01-12T06:53:40Z","project":[{"name":"Lifelong Learning of Visual Scene Understanding","grant_number":"308036","call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425"},{"grant_number":"616160","name":"Discrete Optimization in Computer Vision: Theory and Practice","_id":"25FBA906-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"status":"public","conference":{"start_date":"2015-06-07","location":"Boston, MA, USA","end_date":"2015-06-12","name":"CVPR: Computer Vision and Pattern Recognition"},"type":"conference","_id":"1859","date_created":"2018-12-11T11:54:24Z","ec_funded":1,"date_published":"2015-06-01T00:00:00Z","doi":"10.1109/CVPR.2015.7298890","page":"2737 - 2745","language":[{"iso":"eng"}],"day":"01","year":"2015","publication_status":"published","month":"06","oa":1,"main_file_link":[{"url":"http://arxiv.org/abs/1408.6804","open_access":"1"}],"quality_controlled":"1","scopus_import":1,"publisher":"IEEE","oa_version":"Preprint","abstract":[{"lang":"eng","text":"Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called \\emph{max-oracle}), which has to solve an optimization problem itself, e.g. a graph cut.\r\nIn this work, we introduce a new algorithm for SSVM training that is more efficient than earlier techniques when the max-oracle is computationally expensive, as it is frequently the case in computer vision tasks. The main idea is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm with efficient hyperplane caching, and (ii) use an automatic selection rule for deciding whether to call the exact max-oracle or to rely on an approximate one based on the cached hyperplanes.\r\nWe show experimentally that this strategy leads to faster convergence to the optimum with respect to the number of requires oracle calls, and that this translates into faster convergence with respect to the total runtime when the max-oracle is slow compared to the other steps of the algorithm. "}]},{"date_updated":"2021-01-12T06:53:41Z","citation":{"mla":"Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746.","ieee":"A. Royer and C. Lampert, “Classifier adaptation at prediction time,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 1401–1409.","short":"A. Royer, C. Lampert, in:, IEEE, 2015, pp. 1401–1409.","ama":"Royer A, Lampert C. Classifier adaptation at prediction time. In: IEEE; 2015:1401-1409. doi:10.1109/CVPR.2015.7298746","apa":"Royer, A., & Lampert, C. (2015). Classifier adaptation at prediction time (pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298746","chicago":"Royer, Amélie, and Christoph Lampert. “Classifier Adaptation at Prediction Time,” 1401–9. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298746.","ista":"Royer A, Lampert C. 2015. Classifier adaptation at prediction time. CVPR: Computer Vision and Pattern Recognition, 1401–1409."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Royer","full_name":"Royer, Amélie","first_name":"Amélie"},{"first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph"}],"publist_id":"5239","title":"Classifier adaptation at prediction time","department":[{"_id":"ChLa"}],"_id":"1860","type":"conference","conference":{"name":"CVPR: Computer Vision and Pattern Recognition","end_date":"2015-06-12","location":"Boston, MA, United States","start_date":"2015-06-07"},"status":"public","project":[{"name":"Lifelong Learning of Visual Scene Understanding","grant_number":"308036","_id":"2532554C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"year":"2015","publication_status":"published","day":"01","language":[{"iso":"eng"}],"page":"1401 - 1409","doi":"10.1109/CVPR.2015.7298746","date_published":"2015-06-01T00:00:00Z","ec_funded":1,"date_created":"2018-12-11T11:54:24Z","abstract":[{"text":"Classifiers for object categorization are usually evaluated by their accuracy on a set of i.i.d. test examples. This provides us with an estimate of the expected error when applying the classifiers to a single new image. In real application, however, classifiers are rarely only used for a single image and then discarded. Instead, they are applied sequentially to many images, and these are typically not i.i.d. samples from a fixed data distribution, but they carry dependencies and their class distribution varies over time. In this work, we argue that the phenomenon of correlated data at prediction time is not a nuisance, but a blessing in disguise. We describe a probabilistic method for adapting classifiers at prediction time without having to retrain them. We also introduce a framework for creating realistically distributed image sequences, which offers a way to benchmark classifier adaptation methods, such as the one we propose. Experiments on the ILSVRC2010 and ILSVRC2012 datasets show that adapting object classification systems at prediction time can significantly reduce their error rate, even with no additional human feedback.","lang":"eng"}],"oa_version":"Submitted Version","scopus_import":1,"quality_controlled":"1","publisher":"IEEE","main_file_link":[{"url":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Royer_Classifier_Adaptation_at_2015_CVPR_paper.pdf","open_access":"1"}],"oa":1,"month":"06"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:53:40Z","citation":{"mla":"Lampert, Christoph. Predicting the Future Behavior of a Time-Varying Probability Distribution. IEEE, 2015, pp. 942–50, doi:10.1109/CVPR.2015.7298696.","apa":"Lampert, C. (2015). Predicting the future behavior of a time-varying probability distribution (pp. 942–950). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298696","ama":"Lampert C. Predicting the future behavior of a time-varying probability distribution. In: IEEE; 2015:942-950. doi:10.1109/CVPR.2015.7298696","short":"C. Lampert, in:, IEEE, 2015, pp. 942–950.","ieee":"C. Lampert, “Predicting the future behavior of a time-varying probability distribution,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 942–950.","chicago":"Lampert, Christoph. “Predicting the Future Behavior of a Time-Varying Probability Distribution,” 942–50. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298696.","ista":"Lampert C. 2015. Predicting the future behavior of a time-varying probability distribution. CVPR: Computer Vision and Pattern Recognition, 942–950."},"department":[{"_id":"ChLa"}],"title":"Predicting the future behavior of a time-varying probability distribution","external_id":{"arxiv":["1406.5362"]},"author":[{"first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887"}],"publist_id":"5241","_id":"1858","status":"public","conference":{"end_date":"2015-06-12","location":"Boston, MA, United States","start_date":"2015-06-07","name":"CVPR: Computer Vision and Pattern Recognition"},"type":"conference","language":[{"iso":"eng"}],"day":"15","publication_status":"published","year":"2015","date_created":"2018-12-11T11:54:24Z","date_published":"2015-10-15T00:00:00Z","doi":"10.1109/CVPR.2015.7298696","page":"942 - 950","oa_version":"Preprint","abstract":[{"text":"We study the problem of predicting the future, though only in the probabilistic sense of estimating a future state of a time-varying probability distribution. This is not only an interesting academic problem, but solving this extrapolation problem also has many practical application, e.g. for training classifiers that have to operate under time-varying conditions. Our main contribution is a method for predicting the next step of the time-varying distribution from a given sequence of sample sets from earlier time steps. For this we rely on two recent machine learning techniques: embedding probability distributions into a reproducing kernel Hilbert space, and learning operators by vector-valued regression. We illustrate the working principles and the practical usefulness of our method by experiments on synthetic and real data. We also highlight an exemplary application: training a classifier in a domain adaptation setting without having access to examples from the test time distribution at training time.","lang":"eng"}],"month":"10","main_file_link":[{"url":"https://arxiv.org/abs/1406.5362","open_access":"1"}],"oa":1,"quality_controlled":"1","publisher":"IEEE","scopus_import":1}]