[{"language":[{"iso":"eng"}],"intvolume":"       119","page":"5533-5543","day":"12","month":"07","abstract":[{"text":"Optimizing convolutional neural networks for fast inference has recently become an extremely active area of research. One of the go-to solutions in this context is weight pruning, which aims to reduce computational and memory footprint by removing large subsets of the connections in a neural network. Surprisingly, much less attention has been given to exploiting sparsity in the activation maps, which tend to be naturally sparse in many settings thanks to the structure of rectified linear (ReLU) activation functions. In this paper, we present an in-depth analysis of methods for maximizing the sparsity of the activations in a trained neural network, and show that, when coupled with an efficient sparse-input convolution algorithm, we can leverage this sparsity for significant performance gains. To induce highly sparse activation maps without accuracy loss, we introduce a new regularization technique, coupled with a new threshold-based sparsification method based on a parameterized activation function called Forced-Activation-Threshold Rectified Linear Unit (FATReLU). We examine the impact of our methods on popular image classification models, showing that most architectures can adapt to significantly sparser activation maps without any accuracy loss. Our second contribution is showing that these these compression gains can be translated into inference speedups: we provide a new algorithm to enable fast convolution operations over networks with sparse activations, and show that it can enable significant speedups for end-to-end inference on a range of popular models on the large-scale ImageNet image classification task on modern Intel CPUs, with little or no retraining cost. ","lang":"eng"}],"status":"public","publication_identifier":{"issn":["2640-3498"]},"year":"2020","volume":119,"article_processing_charge":"No","publication":"37th International Conference on Machine Learning, ICML 2020","date_created":"2021-05-23T22:01:45Z","ddc":["000"],"title":"Inducing and exploiting activation sparsity for fast neural network inference","date_published":"2020-07-12T00:00:00Z","quality_controlled":"1","conference":{"name":"ICML: International Conference on Machine Learning","location":"Online","start_date":"2020-07-12","end_date":"2020-07-18"},"department":[{"_id":"DaAl"}],"file":[{"success":1,"date_created":"2021-05-25T09:51:36Z","file_id":"9421","access_level":"open_access","file_size":741899,"content_type":"application/pdf","date_updated":"2021-05-25T09:51:36Z","file_name":"2020_PMLR_Kurtz.pdf","creator":"kschuh","relation":"main_file","checksum":"2aaaa7d7226e49161311d91627cf783b"}],"date_updated":"2023-02-23T13:57:24Z","oa_version":"Published Version","author":[{"last_name":"Kurtz","full_name":"Kurtz, Mark","first_name":"Mark"},{"last_name":"Kopinsky","full_name":"Kopinsky, Justin","first_name":"Justin"},{"last_name":"Gelashvili","full_name":"Gelashvili, Rati","first_name":"Rati"},{"first_name":"Alexander","full_name":"Matveev, Alexander","last_name":"Matveev"},{"full_name":"Carr, John","first_name":"John","last_name":"Carr"},{"last_name":"Goin","full_name":"Goin, Michael","first_name":"Michael"},{"first_name":"William","full_name":"Leiserson, William","last_name":"Leiserson"},{"full_name":"Moore, Sage","first_name":"Sage","last_name":"Moore"},{"first_name":"Bill","full_name":"Nell, Bill","last_name":"Nell"},{"full_name":"Shavit, Nir","first_name":"Nir","last_name":"Shavit"},{"orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","last_name":"Alistarh"}],"oa":1,"citation":{"chicago":"Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In <i>37th International Conference on Machine Learning, ICML 2020</i>, 119:5533–43, 2020.","ama":"Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: <i>37th International Conference on Machine Learning, ICML 2020</i>. Vol 119. ; 2020:5533-5543.","short":"M. Kurtz, J. Kopinsky, R. Gelashvili, A. Matveev, J. Carr, M. Goin, W. Leiserson, S. Moore, B. Nell, N. Shavit, D.-A. Alistarh, in:, 37th International Conference on Machine Learning, ICML 2020, 2020, pp. 5533–5543.","ieee":"M. Kurtz <i>et al.</i>, “Inducing and exploiting activation sparsity for fast neural network inference,” in <i>37th International Conference on Machine Learning, ICML 2020</i>, Online, 2020, vol. 119, pp. 5533–5543.","mla":"Kurtz, Mark, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” <i>37th International Conference on Machine Learning, ICML 2020</i>, vol. 119, 2020, pp. 5533–43.","apa":"Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M., … Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural network inference. In <i>37th International Conference on Machine Learning, ICML 2020</i> (Vol. 119, pp. 5533–5543). Online.","ista":"Kurtz M, Kopinsky J, Gelashvili R, Matveev A, Carr J, Goin M, Leiserson W, Moore S, Nell B, Shavit N, Alistarh D-A. 2020. Inducing and exploiting activation sparsity for fast neural network inference. 37th International Conference on Machine Learning, ICML 2020. ICML: International Conference on Machine Learning vol. 119, 5533–5543."},"has_accepted_license":"1","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","type":"conference","scopus_import":"1","file_date_updated":"2021-05-25T09:51:36Z","_id":"9415"},{"_id":"9630","scopus_import":"1","file_date_updated":"2021-08-11T11:55:11Z","type":"journal_article","file":[{"relation":"main_file","checksum":"f02d0b2b3838e7891a6c417fc34ffdcd","creator":"asandaue","file_name":"2020_JournalOfComputationalGeometry_Edelsbrunner.pdf","file_size":1449234,"date_updated":"2021-08-11T11:55:11Z","content_type":"application/pdf","access_level":"open_access","date_created":"2021-08-11T11:55:11Z","success":1,"file_id":"9882"}],"department":[{"_id":"HeEd"}],"date_updated":"2021-08-11T12:26:34Z","oa_version":"Published Version","oa":1,"author":[{"last_name":"Edelsbrunner","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","first_name":"Herbert","full_name":"Edelsbrunner, Herbert","orcid":"0000-0002-9823-6833"},{"id":"2E36B656-F248-11E8-B48F-1D18A9856A87","first_name":"Ziga","full_name":"Virk, Ziga","last_name":"Virk"},{"last_name":"Wagner","first_name":"Hubert","id":"379CA8B8-F248-11E8-B48F-1D18A9856A87","full_name":"Wagner, Hubert"}],"citation":{"chicago":"Edelsbrunner, Herbert, Ziga Virk, and Hubert Wagner. “Topological Data Analysis in Information Space.” <i>Journal of Computational Geometry</i>. Carleton University, 2020. <a href=\"https://doi.org/10.20382/jocg.v11i2a7\">https://doi.org/10.20382/jocg.v11i2a7</a>.","short":"H. Edelsbrunner, Z. Virk, H. Wagner, Journal of Computational Geometry 11 (2020) 162–182.","ama":"Edelsbrunner H, Virk Z, Wagner H. Topological data analysis in information space. <i>Journal of Computational Geometry</i>. 2020;11(2):162-182. doi:<a href=\"https://doi.org/10.20382/jocg.v11i2a7\">10.20382/jocg.v11i2a7</a>","ieee":"H. Edelsbrunner, Z. Virk, and H. Wagner, “Topological data analysis in information space,” <i>Journal of Computational Geometry</i>, vol. 11, no. 2. Carleton University, pp. 162–182, 2020.","apa":"Edelsbrunner, H., Virk, Z., &#38; Wagner, H. (2020). Topological data analysis in information space. <i>Journal of Computational Geometry</i>. Carleton University. <a href=\"https://doi.org/10.20382/jocg.v11i2a7\">https://doi.org/10.20382/jocg.v11i2a7</a>","mla":"Edelsbrunner, Herbert, et al. “Topological Data Analysis in Information Space.” <i>Journal of Computational Geometry</i>, vol. 11, no. 2, Carleton University, 2020, pp. 162–82, doi:<a href=\"https://doi.org/10.20382/jocg.v11i2a7\">10.20382/jocg.v11i2a7</a>.","ista":"Edelsbrunner H, Virk Z, Wagner H. 2020. Topological data analysis in information space. Journal of Computational Geometry. 11(2), 162–182."},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","has_accepted_license":"1","tmp":{"name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","image":"/images/cc_by.png","short":"CC BY (3.0)"},"date_published":"2020-12-14T00:00:00Z","title":"Topological data analysis in information space","project":[{"grant_number":"I4887","name":"Discretization in Geometry and Dynamics","_id":"0aa4bc98-070f-11eb-9043-e6fff9c6a316"}],"quality_controlled":"1","date_created":"2021-07-04T22:01:26Z","ddc":["510","000"],"year":"2020","article_processing_charge":"Yes","volume":11,"publication":"Journal of Computational Geometry","status":"public","publication_identifier":{"eissn":["1920180X"]},"license":"https://creativecommons.org/licenses/by/3.0/","issue":"2","abstract":[{"lang":"eng","text":"Various kinds of data are routinely represented as discrete probability distributions. Examples include text documents summarized by histograms of word occurrences and images represented as histograms of oriented gradients. Viewing a discrete probability distribution as a point in the standard simplex of the appropriate dimension, we can understand collections of such objects in geometric and topological terms.  Importantly, instead of using the standard Euclidean distance, we look into dissimilarity measures with information-theoretic justification, and we develop the theory needed for applying topological data analysis in this setting. In doing so, we emphasize constructions that enable the usage of existing computational topology software in this context."}],"doi":"10.20382/jocg.v11i2a7","month":"12","publication_status":"published","acknowledgement":"This research is partially supported by the Office of Naval Research, through grant no. N62909-18-1-2038, and the DFG Collaborative Research Center TRR 109, ‘Discretization in Geometry and Dynamics’, through grant no. I02979-N35 of the Austrian Science Fund (FWF).","day":"14","publisher":"Carleton University","intvolume":"        11","article_type":"original","page":"162-182","language":[{"iso":"eng"}]},{"type":"conference","department":[{"_id":"DaAl"}],"oa_version":"Published Version","date_updated":"2023-02-23T14:03:03Z","author":[{"last_name":"Aksenov","first_name":"Vitaly","full_name":"Aksenov, Vitaly"},{"orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","last_name":"Alistarh"},{"last_name":"Korhonen","id":"C5402D42-15BC-11E9-A202-CA2BE6697425","full_name":"Korhonen, Janne","first_name":"Janne"}],"oa":1,"citation":{"ista":"Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via relaxed scheduling. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 22361–22372.","mla":"Aksenov, Vitaly, et al. “Scalable Belief Propagation via Relaxed Scheduling.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 22361–72.","apa":"Aksenov, V., Alistarh, D.-A., &#38; Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 22361–22372). Vancouver, Canada: Curran Associates.","ama":"Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. Curran Associates; 2020:22361-22372.","short":"V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 22361–22372.","chicago":"Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief Propagation via Relaxed Scheduling.” In <i>Advances in Neural Information Processing Systems</i>, 33:22361–72. Curran Associates, 2020.","ieee":"V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372."},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","_id":"9631","external_id":{"arxiv":["2002.11505"]},"scopus_import":"1","date_created":"2021-07-04T22:01:26Z","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html"}],"year":"2020","article_processing_charge":"No","volume":33,"publication":"Advances in Neural Information Processing Systems","conference":{"name":"NeurIPS: Conference on Neural Information Processing Systems","start_date":"2020-12-06","location":"Vancouver, Canada","end_date":"2020-12-12"},"date_published":"2020-12-06T00:00:00Z","title":"Scalable belief propagation via relaxed scheduling","project":[{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning"}],"quality_controlled":"1","month":"12","acknowledgement":"We thank Marco Mondelli for discussions related to LDPC decoding, and Giorgi Nadiradze for discussions on analysis of relaxed schedulers. This project has received funding from the European Research Council (ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML).","publication_status":"published","ec_funded":1,"day":"06","status":"public","publication_identifier":{"isbn":["9781713829546"],"issn":["10495258"]},"abstract":[{"text":"The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning. Consequently, there has been considerable effort invested into developing efficient parallel variants of classic machine learning algorithms. However, despite the wealth of knowledge on parallelization, some classic machine learning algorithms often prove hard to parallelize efficiently while maintaining convergence. In this paper, we focus on efficient parallel algorithms for the key machine learning task of inference on graphical models, in particular on the fundamental belief propagation algorithm. We address the challenge of efficiently parallelizing this classic paradigm by showing how to leverage scalable relaxed schedulers in this context. We present an extensive empirical study, showing that our approach outperforms previous parallel belief propagation implementations both in terms of scalability and in terms of wall-clock convergence time, on a range of practical applications.","lang":"eng"}],"intvolume":"        33","page":"22361-22372","arxiv":1,"language":[{"iso":"eng"}],"publisher":"Curran Associates"},{"conference":{"location":"Vancouver, Canada","start_date":"2020-12-06","end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems"},"date_published":"2020-12-06T00:00:00Z","title":"WoodFisher: Efficient second-order approximation for neural network compression","project":[{"name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","date_created":"2021-07-04T22:01:26Z","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html"}],"year":"2020","article_processing_charge":"No","volume":33,"publication":"Advances in Neural Information Processing Systems","_id":"9632","external_id":{"arxiv":["2004.14340"]},"scopus_import":"1","type":"conference","department":[{"_id":"DaAl"},{"_id":"ToHe"}],"oa_version":"Published Version","date_updated":"2023-02-23T14:03:06Z","author":[{"full_name":"Singh, Sidak Pal","first_name":"Sidak Pal","id":"DD138E24-D89D-11E9-9DC0-DEF6E5697425","last_name":"Singh"},{"orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh"}],"oa":1,"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"mla":"Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 18098–109.","apa":"Singh, S. P., &#38; Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 18098–18109). Vancouver, Canada: Curran Associates.","ista":"Singh SP, Alistarh D-A. 2020. WoodFisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 18098–18109.","chicago":"Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” In <i>Advances in Neural Information Processing Systems</i>, 33:18098–109. Curran Associates, 2020.","short":"S.P. Singh, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 18098–18109.","ama":"Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. Curran Associates; 2020:18098-18109.","ieee":"S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109."},"publisher":"Curran Associates","intvolume":"        33","page":"18098-18109","language":[{"iso":"eng"}],"arxiv":1,"status":"public","publication_identifier":{"issn":["10495258"],"isbn":["9781713829546"]},"abstract":[{"text":"Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep\r\nneural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for oneshot pruning. Further, even when iterative, gradual pruning is allowed, our method results in a gain in test accuracy over the state-of-the-art approaches, for standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as\r\nillustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher.","lang":"eng"}],"month":"12","publication_status":"published","acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). Also, we would like to thank Alexander Shevchenko, Alexandra Peste, and other members of the group for fruitful discussions.","ec_funded":1,"day":"06"},{"language":[{"iso":"eng"}],"intvolume":"        33","page":"16398-16408","abstract":[{"lang":"eng","text":"The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by – and fitted to – experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Oja’s rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions."}],"status":"public","publication_identifier":{"issn":["1049-5258"]},"publication_status":"published","acknowledgement":"We would like to thank Chaitanya Chintaluri, Georgia Christodoulou, Bill Podlaski and Merima Šabanovic for useful discussions and comments. This work was supported by a Wellcome Trust ´ Senior Research Fellowship (214316/Z/18/Z), a BBSRC grant (BB/N019512/1), an ERC consolidator Grant (SYNAPSEEK), a Leverhulme Trust Project Grant (RPG-2016-446), and funding from École Polytechnique, Paris.","day":"06","ec_funded":1,"related_material":{"record":[{"status":"public","id":"14422","relation":"dissertation_contains"}],"link":[{"relation":"is_continued_by","url":"https://doi.org/10.1101/2020.10.24.353409"}]},"month":"12","project":[{"grant_number":"214316/Z/18/Z","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87"},{"grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020"}],"title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","date_published":"2020-12-06T00:00:00Z","quality_controlled":"1","conference":{"end_date":"2020-12-12","location":"Vancouver, Canada","start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems"},"year":"2020","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html"}],"publication":"Advances in Neural Information Processing Systems","volume":33,"article_processing_charge":"No","date_created":"2021-07-04T22:01:27Z","scopus_import":"1","_id":"9633","date_updated":"2023-10-18T09:20:55Z","oa_version":"Published Version","department":[{"_id":"TiVo"}],"citation":{"ieee":"B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 16398–16408.","ama":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. ; 2020:16398-16408.","short":"B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances in Neural Information Processing Systems, 2020, pp. 16398–16408.","chicago":"Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap, and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” In <i>Advances in Neural Information Processing Systems</i>, 33:16398–408, 2020.","ista":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 16398–16408.","mla":"Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, 2020, pp. 16398–408.","apa":"Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., &#38; Vogels, T. P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 16398–16408). Vancouver, Canada."},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa":1,"author":[{"first_name":"Basile J","id":"C7610134-B532-11EA-BD9F-F5753DDC885E","full_name":"Confavreux, Basile J","last_name":"Confavreux"},{"first_name":"Friedemann","full_name":"Zenke, Friedemann","last_name":"Zenke"},{"first_name":"Everton J.","full_name":"Agnes, Everton J.","last_name":"Agnes"},{"first_name":"Timothy","full_name":"Lillicrap, Timothy","last_name":"Lillicrap"},{"full_name":"Vogels, Tim P","first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","orcid":"0000-0003-3295-6181","last_name":"Vogels"}],"type":"conference"},{"abstract":[{"text":"We prove that in the absence of topological changes, the notion of BV solutions to planar multiphase mean curvature flow does not allow for a mechanism for (unphysical) non-uniqueness. Our approach is based on the local structure of the energy landscape near a classical evolution by mean curvature. Mean curvature flow being the gradient flow of the surface energy functional, we develop a gradient-flow analogue of the notion of calibrations. Just like the existence of a calibration guarantees that one has reached a global minimum in the energy landscape, the existence of a \"gradient flow calibration\" ensures that the route of steepest descent in the energy landscape is unique and stable.","lang":"eng"}],"external_id":{"arxiv":["2003.05478"]},"status":"public","_id":"10012","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","citation":{"ieee":"J. L. Fischer, S. Hensel, T. Laux, and T. Simon, “The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions,” <i>arXiv</i>. .","chicago":"Fischer, Julian L, Sebastian Hensel, Tim Laux, and Thilo Simon. “The Local Structure of the Energy Landscape in Multiphase Mean Curvature Flow: Weak-Strong Uniqueness and Stability of Evolutions.” <i>ArXiv</i>, n.d.","ama":"Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. <i>arXiv</i>.","short":"J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.).","apa":"Fischer, J. L., Hensel, S., Laux, T., &#38; Simon, T. (n.d.). The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. <i>arXiv</i>.","mla":"Fischer, Julian L., et al. “The Local Structure of the Energy Landscape in Multiphase Mean Curvature Flow: Weak-Strong Uniqueness and Stability of Evolutions.” <i>ArXiv</i>, 2003.05478.","ista":"Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. arXiv, 2003.05478."},"day":"11","oa":1,"ec_funded":1,"author":[{"orcid":"0000-0002-0479-558X","first_name":"Julian L","id":"2C12A0B0-F248-11E8-B48F-1D18A9856A87","full_name":"Fischer, Julian L","last_name":"Fischer"},{"last_name":"Hensel","orcid":"0000-0001-7252-8072","first_name":"Sebastian","id":"4D23B7DA-F248-11E8-B48F-1D18A9856A87","full_name":"Hensel, Sebastian"},{"first_name":"Tim","full_name":"Laux, Tim","last_name":"Laux"},{"last_name":"Simon","first_name":"Thilo","full_name":"Simon, Thilo"}],"date_updated":"2023-09-07T13:30:45Z","publication_status":"submitted","acknowledgement":"Parts of the paper were written during the visit of the authors to the Hausdorff Research Institute for Mathematics (HIM), University of Bonn, in the framework of the trimester program “Evolution of Interfaces”. The support and the hospitality of HIM are gratefully acknowledged. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 665385.","oa_version":"Preprint","department":[{"_id":"JuFi"}],"type":"preprint","related_material":{"record":[{"status":"public","id":"10007","relation":"dissertation_contains"}]},"month":"03","project":[{"grant_number":"665385","name":"International IST Doctoral Program","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"}],"date_published":"2020-03-11T00:00:00Z","title":"The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions","publication":"arXiv","language":[{"iso":"eng"}],"article_processing_charge":"No","arxiv":1,"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2003.05478"}],"year":"2020","article_number":"2003.05478","date_created":"2021-09-13T12:17:11Z"},{"external_id":{"arxiv":["2008.10962"]},"abstract":[{"lang":"eng","text":"We consider finite-volume approximations of Fokker-Planck equations on bounded convex domains in R^d and study the corresponding gradient flow structures. We reprove the convergence of the discrete to continuous Fokker-Planck equation via the method of Evolutionary Γ-convergence, i.e., we pass to the limit at the level of the gradient flow structures, generalising the one-dimensional result obtained by Disser and Liero. The proof is of variational nature and relies on a Mosco convergence result for functionals in the discrete-to-continuum limit that is of independent interest. Our results apply to arbitrary regular meshes, even though the associated discrete transport distances may fail to converge to the Wasserstein distance in this generality."}],"_id":"10022","status":"public","department":[{"_id":"JaMa"}],"date_updated":"2023-09-07T13:31:05Z","publication_status":"submitted","acknowledgement":"This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 716117) and by the Austrian Science Fund (FWF), grants No F65 and W1245.","oa_version":"Preprint","author":[{"id":"35C79D68-F248-11E8-B48F-1D18A9856A87","first_name":"Dominik L","full_name":"Forkert, Dominik L","last_name":"Forkert"},{"last_name":"Maas","orcid":"0000-0002-0845-1338","first_name":"Jan","id":"4C5696CE-F248-11E8-B48F-1D18A9856A87","full_name":"Maas, Jan"},{"first_name":"Lorenzo","full_name":"Portinale, Lorenzo","id":"30AD2CBC-F248-11E8-B48F-1D18A9856A87","last_name":"Portinale"}],"oa":1,"ec_funded":1,"citation":{"ieee":"D. L. Forkert, J. Maas, and L. Portinale, “Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions,” <i>arXiv</i>. .","ama":"Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. <i>arXiv</i>.","short":"D.L. Forkert, J. Maas, L. Portinale, ArXiv (n.d.).","chicago":"Forkert, Dominik L, Jan Maas, and Lorenzo Portinale. “Evolutionary Γ-Convergence of Entropic Gradient Flow Structures for Fokker-Planck Equations in Multiple Dimensions.” <i>ArXiv</i>, n.d.","ista":"Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. arXiv, 2008.10962.","apa":"Forkert, D. L., Maas, J., &#38; Portinale, L. (n.d.). Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. <i>arXiv</i>.","mla":"Forkert, Dominik L., et al. “Evolutionary Γ-Convergence of Entropic Gradient Flow Structures for Fokker-Planck Equations in Multiple Dimensions.” <i>ArXiv</i>, 2008.10962."},"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","day":"25","related_material":{"record":[{"relation":"later_version","id":"11739","status":"public"},{"status":"public","relation":"dissertation_contains","id":"10030"}]},"month":"08","type":"preprint","title":"Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions","date_published":"2020-08-25T00:00:00Z","project":[{"name":"Optimal Transport and Stochastic Dynamics","grant_number":"716117","call_identifier":"H2020","_id":"256E75B8-B435-11E9-9278-68D0E5697425"},{"_id":"fc31cba2-9c52-11eb-aca3-ff467d239cd2","grant_number":"F6504","name":"Taming Complexity in Partial Differential Systems"}],"year":"2020","main_file_link":[{"url":"https://arxiv.org/abs/2008.10962","open_access":"1"}],"language":[{"iso":"eng"}],"arxiv":1,"article_processing_charge":"No","publication":"arXiv","date_created":"2021-09-17T10:57:27Z","article_number":"2008.10962","page":"33"},{"alternative_title":["OSA Technical Digest"],"type":"conference","month":"01","date_updated":"2023-10-18T08:32:34Z","oa_version":"None","publication_status":"published","department":[{"_id":"JoFi"}],"day":"01","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. New designs and noise channels in electro-optic microwave to optical up-conversion. In: <i>OSA Quantum 2.0 Conference</i>. Optica Publishing Group; 2020. doi:<a href=\"https://doi.org/10.1364/QUANTUM.2020.QTu8A.1\">10.1364/QUANTUM.2020.QTu8A.1</a>","short":"N.J. Lambert, S. Mobassem, A.R. Rueda Sanchez, H.G.L. Schwefel, in:, OSA Quantum 2.0 Conference, Optica Publishing Group, 2020.","chicago":"Lambert, Nicholas J., Sonia Mobassem, Alfredo R Rueda Sanchez, and Harald G.L. Schwefel. “New Designs and Noise Channels in Electro-Optic Microwave to Optical up-Conversion.” In <i>OSA Quantum 2.0 Conference</i>. Optica Publishing Group, 2020. <a href=\"https://doi.org/10.1364/QUANTUM.2020.QTu8A.1\">https://doi.org/10.1364/QUANTUM.2020.QTu8A.1</a>.","ieee":"N. J. Lambert, S. Mobassem, A. R. Rueda Sanchez, and H. G. L. Schwefel, “New designs and noise channels in electro-optic microwave to optical up-conversion,” in <i>OSA Quantum 2.0 Conference</i>, Washington, DC, United States, 2020.","ista":"Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. 2020. New designs and noise channels in electro-optic microwave to optical up-conversion. OSA Quantum 2.0 Conference. OSA: Optical Society of America, OSA Technical Digest, , QTu8A.1.","mla":"Lambert, Nicholas J., et al. “New Designs and Noise Channels in Electro-Optic Microwave to Optical up-Conversion.” <i>OSA Quantum 2.0 Conference</i>, QTu8A.1, Optica Publishing Group, 2020, doi:<a href=\"https://doi.org/10.1364/QUANTUM.2020.QTu8A.1\">10.1364/QUANTUM.2020.QTu8A.1</a>.","apa":"Lambert, N. J., Mobassem, S., Rueda Sanchez, A. R., &#38; Schwefel, H. G. L. (2020). New designs and noise channels in electro-optic microwave to optical up-conversion. In <i>OSA Quantum 2.0 Conference</i>. Washington, DC, United States: Optica Publishing Group. <a href=\"https://doi.org/10.1364/QUANTUM.2020.QTu8A.1\">https://doi.org/10.1364/QUANTUM.2020.QTu8A.1</a>"},"author":[{"first_name":"Nicholas J.","full_name":"Lambert, Nicholas J.","last_name":"Lambert"},{"full_name":"Mobassem, Sonia","first_name":"Sonia","last_name":"Mobassem"},{"last_name":"Rueda Sanchez","id":"3B82B0F8-F248-11E8-B48F-1D18A9856A87","first_name":"Alfredo R","full_name":"Rueda Sanchez, Alfredo R","orcid":"0000-0001-6249-5860"},{"first_name":"Harald G.L.","full_name":"Schwefel, Harald G.L.","last_name":"Schwefel"}],"status":"public","_id":"10328","publication_identifier":{"isbn":["9-781-5575-2820-9"]},"doi":"10.1364/QUANTUM.2020.QTu8A.1","abstract":[{"text":"We discus noise channels in coherent electro-optic up-conversion between microwave and optical fields, in particular due to optical heating. We also report on a novel configuration, which promises to be flexible and highly efficient.","lang":"eng"}],"scopus_import":"1","article_number":"QTu8A.1","date_created":"2021-11-21T23:01:31Z","year":"2020","publication":"OSA Quantum 2.0 Conference","language":[{"iso":"eng"}],"article_processing_charge":"No","conference":{"start_date":"2020-09-14","location":"Washington, DC, United States","end_date":"2020-09-17","name":"OSA: Optical Society of America"},"publisher":"Optica Publishing Group","title":"New designs and noise channels in electro-optic microwave to optical up-conversion","date_published":"2020-01-01T00:00:00Z","quality_controlled":"1"},{"month":"10","isi":1,"day":"30","publication_status":"published","acknowledgement":"We would like to thank Ittai Abraham for the discussions and guidance during the initial conception of the project, especially for HAVSS. Furthermore, we would like to thank the anonymous reviewers for pointing out the relevance of this work to MPC protocols.","publication_identifier":{"isbn":["978-1-4503-7089-9"]},"status":"public","doi":"10.1145/3372297.3423364","abstract":[{"lang":"eng","text":"In this paper, we present the first Asynchronous Distributed Key Generation (ADKG) algorithm which is also the first distributed key generation algorithm that can generate cryptographic keys with a dual (f,2f+1)-threshold (where f is the number of faulty parties). As a result, using our ADKG we remove the trusted setup assumption that the most scalable consensus algorithms make. In order to create a DKG with a dual (f,2f+1)- threshold we first answer in the affirmative the open question posed by Cachin et al. [7] on how to create an Asynchronous Verifiable Secret Sharing (AVSS) protocol with a reconstruction threshold of f+1<k łe 2f+1, which is of independent interest. Our High-threshold-AVSS (HAVSS) uses an asymmetric bivariate polynomial to encode the secret. This enables the reconstruction of the secret only if a set of k nodes contribute while allowing an honest node that did not participate in the sharing phase to recover his share with the help of f+1 honest parties. Once we have HAVSS we can use it to bootstrap scalable partially synchronous consensus protocols, but the question on how to get a DKG in asynchrony remains as we need a way to produce common randomness. The solution comes from a novel Eventually Perfect Common Coin (EPCC) abstraction that enables the generation of a common coin from n concurrent HAVSS invocations. EPCC's key property is that it is eventually reliable, as it might fail to agree at most f times (even if invoked a polynomial number of times). Using EPCC we implement an Eventually Efficient Asynchronous Binary Agreement (EEABA) which is optimal when the EPCC agrees and protects safety when EPCC fails. Finally, using EEABA we construct the first ADKG which has the same overhead and expected runtime as the best partially-synchronous DKG (O(n4) words, O(f) rounds). As a corollary of our ADKG, we can also create the first Validated Asynchronous Byzantine Agreement (VABA) that does not need a trusted dealer to setup threshold signatures of degree n-f. Our VABA has an overhead of expected O(n2) words and O(1) time per instance, after an initial O(n4) words and O(f) time bootstrap via ADKG."}],"page":"1751–1767","language":[{"iso":"eng"}],"publisher":"Association for Computing Machinery","type":"conference","author":[{"full_name":"Kokoris Kogias, Eleftherios","id":"f5983044-d7ef-11ea-ac6d-fd1430a26d30","first_name":"Eleftherios","last_name":"Kokoris Kogias"},{"last_name":"Malkhi","full_name":"Malkhi, Dahlia","first_name":"Dahlia"},{"last_name":"Spiegelman","first_name":"Alexander","full_name":"Spiegelman, Alexander"}],"oa":1,"citation":{"short":"E. Kokoris Kogias, D. Malkhi, A. Spiegelman, in:, Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery, 2020, pp. 1751–1767.","ama":"Kokoris Kogias E, Malkhi D, Spiegelman A. Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. In: <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>. Association for Computing Machinery; 2020:1751–1767. doi:<a href=\"https://doi.org/10.1145/3372297.3423364\">10.1145/3372297.3423364</a>","chicago":"Kokoris Kogias, Eleftherios, Dahlia Malkhi, and Alexander Spiegelman. “Asynchronous Distributed Key Generation for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” In <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>, 1751–1767. Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3372297.3423364\">https://doi.org/10.1145/3372297.3423364</a>.","ieee":"E. Kokoris Kogias, D. Malkhi, and A. Spiegelman, “Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures,” in <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>, Virtual, United States, 2020, pp. 1751–1767.","ista":"Kokoris Kogias E, Malkhi D, Spiegelman A. 2020. Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. CCS: Computer and Communications Security, 1751–1767.","apa":"Kokoris Kogias, E., Malkhi, D., &#38; Spiegelman, A. (2020). Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. In <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i> (pp. 1751–1767). Virtual, United States: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3372297.3423364\">https://doi.org/10.1145/3372297.3423364</a>","mla":"Kokoris Kogias, Eleftherios, et al. “Asynchronous Distributed Key Generation for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>, Association for Computing Machinery, 2020, pp. 1751–1767, doi:<a href=\"https://doi.org/10.1145/3372297.3423364\">10.1145/3372297.3423364</a>."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"ElKo"}],"date_updated":"2024-02-22T13:10:45Z","oa_version":"Preprint","_id":"10556","scopus_import":"1","external_id":{"isi":["000768470400104"]},"date_created":"2021-12-16T13:23:27Z","article_processing_charge":"No","publication":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","year":"2020","main_file_link":[{"open_access":"1","url":"https://eprint.iacr.org/2019/1015"}],"conference":{"name":"CCS: Computer and Communications Security","end_date":"2020-11-13","location":"Virtual, United States","start_date":"2020-11-09"},"quality_controlled":"1","title":"Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures","date_published":"2020-10-30T00:00:00Z"},{"publisher":"Springer Nature","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"title":"Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","other_data_license":"CC0 + CC BY (4.0)","date_published":"2020-07-09T00:00:00Z","date_created":"2021-07-23T08:59:15Z","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.6084/m9.figshare.12629697.v1"}],"year":"2020","_id":"9706","status":"public","abstract":[{"lang":"eng","text":"Additional file 2: Supplementary Tables. The association of pre-adjusted protein levels with biological and technical covariates. Protein levels were adjusted for age, sex, array plate and four genetic principal components (population structure) prior to analyses. Significant associations are emboldened. (Table S1). pQTLs associated with inflammatory biomarker levels from Bayesian penalised regression model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary least squares GWAS and EWAS performed on inflammatory protein levels (n = 70) in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary least squares and Bayesian penalised regression models for concordantly identified SNPs. (Table S6). Estimate of heritability for blood protein levels as well as proportion of variance explained attributable to different prior mixtures. (Table S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood) and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant SNPs identified by linear model and Bayesian penalised regression and whether they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression are putatively associated with circulating inflammatory proteins that harbour pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table S12). CpGs associated with inflammatory protein biomarkers as identified by linear model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10. (Table S14). Estimate of variance explained for blood protein levels by DNA methylation as well as proportion of explained attributable to different prior mixtures - BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker levels explained by common genetic and methylation data (joint and conditional estimates from BayesR+). Ordered by combined variance explained by genetic and epigenetic data - smallest to largest. Significant results from t-tests comparing distributions for variance explained by methylation or genetics alone versus combined estimate are emboldened. (Table S17). Genetic and epigenetic factors identified by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian Randomisation analyses to assess whether proteins with concordantly identified genetic signals are causally associated with Alzheimer’s disease risk. (Table S19)."}],"doi":"10.6084/m9.figshare.12629697.v1","license":"https://creativecommons.org/licenses/by/4.0/","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"8133"}]},"month":"07","type":"research_data_reference","author":[{"last_name":"Hillary","full_name":"Hillary, Robert F.","first_name":"Robert F."},{"last_name":"Trejo-Banos","first_name":"Daniel","full_name":"Trejo-Banos, Daniel"},{"first_name":"Athanasios","full_name":"Kousathanas, Athanasios","last_name":"Kousathanas"},{"last_name":"McCartney","first_name":"Daniel L.","full_name":"McCartney, Daniel L."},{"last_name":"Harris","first_name":"Sarah E.","full_name":"Harris, Sarah E."},{"last_name":"Stevenson","full_name":"Stevenson, Anna J.","first_name":"Anna J."},{"last_name":"Patxot","full_name":"Patxot, Marion","first_name":"Marion"},{"full_name":"Ojavee, Sven Erik","first_name":"Sven Erik","last_name":"Ojavee"},{"last_name":"Zhang","full_name":"Zhang, Qian","first_name":"Qian"},{"first_name":"David C.","full_name":"Liewald, David C.","last_name":"Liewald"},{"last_name":"Ritchie","first_name":"Craig W.","full_name":"Ritchie, Craig W."},{"first_name":"Kathryn L.","full_name":"Evans, Kathryn L.","last_name":"Evans"},{"first_name":"Elliot M.","full_name":"Tucker-Drob, Elliot M.","last_name":"Tucker-Drob"},{"full_name":"Wray, Naomi R.","first_name":"Naomi R.","last_name":"Wray"},{"full_name":"McRae, Allan F. ","first_name":"Allan F. ","last_name":"McRae"},{"full_name":"Visscher, Peter M.","first_name":"Peter M.","last_name":"Visscher"},{"full_name":"Deary, Ian J.","first_name":"Ian J.","last_name":"Deary"},{"last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813"},{"last_name":"Marioni","full_name":"Marioni, Riccardo E. ","first_name":"Riccardo E. "}],"oa":1,"day":"09","citation":{"chicago":"Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” Springer Nature, 2020. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>.","ama":"Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. 2020. doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>","short":"R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. McCartney, S.E. Harris, A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie, K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. McRae, P.M. Visscher, I.J. Deary, M.R. Robinson, R.E. Marioni, (2020).","ieee":"R. F. Hillary <i>et al.</i>, “Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults.” Springer Nature, 2020.","mla":"Hillary, Robert F., et al. <i>Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults</i>. Springer Nature, 2020, doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>.","apa":"Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris, S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Springer Nature. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>","ista":"Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults, Springer Nature, <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>."},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","has_accepted_license":"1","department":[{"_id":"MaRo"}],"date_updated":"2023-08-22T07:55:36Z","oa_version":"Published Version"},{"type":"research_data_reference","month":"05","related_material":{"record":[{"id":"7942","relation":"used_in_publication","status":"public"}]},"day":"29","has_accepted_license":"1","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"ista":"Hartstein M, Hsu Y-T, Modic KA, Porras J, Loew T, Le Tacon M, Zuo H, Wang J, Zhu Z, Chan M, McDonald R, Lonzarich G, Keimer B, Sebastian S, Harrison N. 2020. Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors’, Apollo - University of Cambridge, <a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>.","mla":"Hartstein, Mate, et al. <i>Accompanying Dataset for “Hard Antinodal Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.”</i> Apollo - University of Cambridge, 2020, doi:<a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>.","apa":"Hartstein, M., Hsu, Y.-T., Modic, K. A., Porras, J., Loew, T., Le Tacon, M., … Harrison, N. (2020). Accompanying dataset for “Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.” Apollo - University of Cambridge. <a href=\"https://doi.org/10.17863/cam.50169\">https://doi.org/10.17863/cam.50169</a>","ieee":"M. Hartstein <i>et al.</i>, “Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.’” Apollo - University of Cambridge, 2020.","ama":"Hartstein M, Hsu Y-T, Modic KA, et al. Accompanying dataset for “Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.” 2020. doi:<a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>","short":"M. Hartstein, Y.-T. Hsu, K.A. Modic, J. Porras, T. Loew, M. Le Tacon, H. Zuo, J. Wang, Z. Zhu, M. Chan, R. McDonald, G. Lonzarich, B. Keimer, S. Sebastian, N. Harrison, (2020).","chicago":"Hartstein, Mate, Yu-Te Hsu, Kimberly A Modic, Juan Porras, Toshinao Loew, Matthieu Le Tacon, Huakun Zuo, et al. “Accompanying Dataset for ‘Hard Antinodal Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.’” Apollo - University of Cambridge, 2020. <a href=\"https://doi.org/10.17863/cam.50169\">https://doi.org/10.17863/cam.50169</a>."},"author":[{"last_name":"Hartstein","full_name":"Hartstein, Mate","first_name":"Mate"},{"full_name":"Hsu, Yu-Te","first_name":"Yu-Te","last_name":"Hsu"},{"orcid":"0000-0001-9760-3147","first_name":"Kimberly A","full_name":"Modic, Kimberly A","id":"13C26AC0-EB69-11E9-87C6-5F3BE6697425","last_name":"Modic"},{"last_name":"Porras","full_name":"Porras, Juan","first_name":"Juan"},{"last_name":"Loew","first_name":"Toshinao","full_name":"Loew, Toshinao"},{"first_name":"Matthieu","full_name":"Le Tacon, Matthieu","last_name":"Le Tacon"},{"last_name":"Zuo","first_name":"Huakun","full_name":"Zuo, Huakun"},{"full_name":"Wang, Jinhua","first_name":"Jinhua","last_name":"Wang"},{"last_name":"Zhu","full_name":"Zhu, Zengwei","first_name":"Zengwei"},{"last_name":"Chan","first_name":"Mun","full_name":"Chan, Mun"},{"last_name":"McDonald","full_name":"McDonald, Ross","first_name":"Ross"},{"last_name":"Lonzarich","first_name":"Gilbert","full_name":"Lonzarich, Gilbert"},{"last_name":"Keimer","full_name":"Keimer, Bernhard","first_name":"Bernhard"},{"first_name":"Suchitra","full_name":"Sebastian, Suchitra","last_name":"Sebastian"},{"last_name":"Harrison","first_name":"Neil","full_name":"Harrison, Neil"}],"oa":1,"date_updated":"2023-08-21T07:06:48Z","oa_version":"Published Version","department":[{"_id":"KiMo"}],"status":"public","_id":"9708","abstract":[{"lang":"eng","text":"This research data supports 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'. A Readme file for plotting each figure is provided."}],"doi":"10.17863/cam.50169","date_created":"2021-07-23T10:00:35Z","article_processing_charge":"No","year":"2020","main_file_link":[{"open_access":"1","url":"https://doi.org/10.17863/CAM.50169"}],"publisher":"Apollo - University of Cambridge","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2020-05-29T00:00:00Z","title":"Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'"},{"doi":"10.1021/jacs.9b13450.s001","abstract":[{"lang":"eng","text":"Additional analyses of the trajectories"}],"status":"public","_id":"9713","oa_version":"Published Version","date_updated":"2023-08-22T07:49:38Z","department":[{"_id":"LeSa"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"mla":"Gupta, Chitrak, et al. <i>Supporting Information</i>. American Chemical Society , 2020, doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>.","apa":"Gupta, C., Khaniya, U., Chan, C. K., Dehez, F., Shekhar, M., Gunner, M. R., … Singharoy, A. (2020). Supporting information. American Chemical Society . <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">https://doi.org/10.1021/jacs.9b13450.s001</a>","ista":"Gupta C, Khaniya U, Chan CK, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot C, Singharoy A. 2020. Supporting information, American Chemical Society , <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>.","chicago":"Gupta, Chitrak, Umesh Khaniya, Chun Kit Chan, Francois Dehez, Mrinal Shekhar, M.R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Supporting Information.” American Chemical Society , 2020. <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">https://doi.org/10.1021/jacs.9b13450.s001</a>.","short":"C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (2020).","ama":"Gupta C, Khaniya U, Chan CK, et al. Supporting information. 2020. doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>","ieee":"C. Gupta <i>et al.</i>, “Supporting information.” American Chemical Society , 2020."},"day":"20","author":[{"full_name":"Gupta, Chitrak","first_name":"Chitrak","last_name":"Gupta"},{"last_name":"Khaniya","first_name":"Umesh","full_name":"Khaniya, Umesh"},{"last_name":"Chan","first_name":"Chun Kit","full_name":"Chan, Chun Kit"},{"last_name":"Dehez","full_name":"Dehez, Francois","first_name":"Francois"},{"last_name":"Shekhar","full_name":"Shekhar, Mrinal","first_name":"Mrinal"},{"first_name":"M.R.","full_name":"Gunner, M.R.","last_name":"Gunner"},{"first_name":"Leonid A","id":"338D39FE-F248-11E8-B48F-1D18A9856A87","full_name":"Sazanov, Leonid A","orcid":"0000-0002-0977-7989","last_name":"Sazanov"},{"last_name":"Chipot","first_name":"Christophe","full_name":"Chipot, Christophe"},{"last_name":"Singharoy","full_name":"Singharoy, Abhishek","first_name":"Abhishek"}],"type":"research_data_reference","month":"05","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"8040"}]},"date_published":"2020-05-20T00:00:00Z","title":"Supporting information","publisher":"American Chemical Society ","year":"2020","article_processing_charge":"No","date_created":"2021-07-23T12:02:39Z"},{"publisher":"Cold Spring Harbor Laboratory","title":"Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion","date_published":"2020-11-20T00:00:00Z","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"call_identifier":"H2020","_id":"260F1432-B435-11E9-9278-68D0E5697425","name":"Interaction and feedback between cell mechanics and fate specification in vertebrate gastrulation","grant_number":"742573"},{"_id":"2521E28E-B435-11E9-9278-68D0E5697425","name":"Modulation of adhesion function in cell-cell contact formation by cortical tension","grant_number":"187-2013"}],"page":"41","date_created":"2021-07-29T11:29:50Z","language":[{"iso":"eng"}],"article_processing_charge":"No","publication":"bioRxiv","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2020.11.20.391284"}],"year":"2020","_id":"9750","status":"public","abstract":[{"text":"Tension of the actomyosin cell cortex plays a key role in determining cell-cell contact growth and size. The level of cortical tension outside of the cell-cell contact, when pulling at the contact edge, scales with the total size to which a cell-cell contact can grow1,2. Here we show in zebrafish primary germ layer progenitor cells that this monotonic relationship only applies to a narrow range of cortical tension increase, and that above a critical threshold, contact size inversely scales with cortical tension. This switch from cortical tension increasing to decreasing progenitor cell-cell contact size is caused by cortical tension promoting E-cadherin anchoring to the actomyosin cytoskeleton, thereby increasing clustering and stability of E-cadherin at the contact. Once tension-mediated E-cadherin stabilization at the contact exceeds a critical threshold level, the rate by which the contact expands in response to pulling forces from the cortex sharply drops, leading to smaller contacts at physiologically relevant timescales of contact formation. Thus, the activity of cortical tension in expanding cell-cell contact size is limited by tension stabilizing E-cadherin-actin complexes at the contact.","lang":"eng"}],"doi":"10.1101/2020.11.20.391284","acknowledged_ssus":[{"_id":"Bio"},{"_id":"EM-Fac"},{"_id":"SSU"}],"related_material":{"record":[{"status":"public","id":"10766","relation":"later_version"},{"id":"9623","relation":"dissertation_contains","status":"public"}]},"month":"11","type":"preprint","oa":1,"author":[{"last_name":"Slovakova","first_name":"Jana","id":"30F3F2F0-F248-11E8-B48F-1D18A9856A87","full_name":"Slovakova, Jana"},{"last_name":"Sikora","full_name":"Sikora, Mateusz K","first_name":"Mateusz K","id":"2F74BCDE-F248-11E8-B48F-1D18A9856A87"},{"id":"2F1E1758-F248-11E8-B48F-1D18A9856A87","full_name":"Caballero Mancebo, Silvia","first_name":"Silvia","orcid":"0000-0002-5223-3346","last_name":"Caballero Mancebo"},{"full_name":"Krens, Gabriel","first_name":"Gabriel","id":"2B819732-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4761-5996","last_name":"Krens"},{"full_name":"Kaufmann, Walter","first_name":"Walter","id":"3F99E422-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9735-5315","last_name":"Kaufmann"},{"full_name":"Huljev, Karla","first_name":"Karla","id":"44C6F6A6-F248-11E8-B48F-1D18A9856A87","last_name":"Huljev"},{"id":"39427864-F248-11E8-B48F-1D18A9856A87","first_name":"Carl-Philipp J","full_name":"Heisenberg, Carl-Philipp J","orcid":"0000-0002-0912-4566","last_name":"Heisenberg"}],"ec_funded":1,"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","citation":{"ama":"Slovakova J, Sikora MK, Caballero Mancebo S, et al. Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. 2020. doi:<a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>","short":"J. Slovakova, M.K. Sikora, S. Caballero Mancebo, G. Krens, W. Kaufmann, K. Huljev, C.-P.J. Heisenberg, BioRxiv (2020).","chicago":"Slovakova, Jana, Mateusz K Sikora, Silvia Caballero Mancebo, Gabriel Krens, Walter Kaufmann, Karla Huljev, and Carl-Philipp J Heisenberg. “Tension-Dependent Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, 2020. <a href=\"https://doi.org/10.1101/2020.11.20.391284\">https://doi.org/10.1101/2020.11.20.391284</a>.","ieee":"J. Slovakova <i>et al.</i>, “Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory, 2020.","ista":"Slovakova J, Sikora MK, Caballero Mancebo S, Krens G, Kaufmann W, Huljev K, Heisenberg C-PJ. 2020. Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. bioRxiv, <a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>.","apa":"Slovakova, J., Sikora, M. K., Caballero Mancebo, S., Krens, G., Kaufmann, W., Huljev, K., &#38; Heisenberg, C.-P. J. (2020). Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. Cold Spring Harbor Laboratory. <a href=\"https://doi.org/10.1101/2020.11.20.391284\">https://doi.org/10.1101/2020.11.20.391284</a>","mla":"Slovakova, Jana, et al. “Tension-Dependent Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, 2020, doi:<a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>."},"day":"20","department":[{"_id":"CaHe"},{"_id":"EM-Fac"},{"_id":"Bio"}],"date_updated":"2024-03-25T23:30:10Z","acknowledgement":"We would like to thank Edouard Hannezo for discussions, Shayan Shami Pour and Daniel Capek for help with data analysis, Vanessa Barone and other members of the Heisenberg laboratory for thoughtful discussions and comments on the manuscript. We also thank Jack Merrin for preparing the microwells, and the Scientific Service Units at IST Austria, specifically Bioimaging and Electron Microscopy, and the Zebrafish Facility for continuous support. We acknowledge Hitoshi Morita for the kind gift of VinculinB-GFP plasmid. This research was supported by an ERC Advanced Grant (MECSPEC) to C.-P.H, EMBO Long Term grant (ALTF 187-2013) to M.S and IST Fellow Marie-Curie COFUND No. P_IST_EU01 to J.S.","publication_status":"published","oa_version":"Preprint"},{"status":"public","_id":"9776","doi":"10.1371/journal.pcbi.1007642.s001","type":"research_data_reference","month":"02","related_material":{"record":[{"id":"7569","relation":"used_in_publication","status":"public"}]},"oa_version":"Published Version","date_updated":"2023-08-18T06:47:47Z","department":[{"_id":"GaTk"}],"day":"25","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"ista":"Grah R, Friedlander T. 2020. Supporting information, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">10.1371/journal.pcbi.1007642.s001</a>.","apa":"Grah, R., &#38; Friedlander, T. (2020). Supporting information. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">https://doi.org/10.1371/journal.pcbi.1007642.s001</a>","mla":"Grah, Rok, and Tamar Friedlander. <i>Supporting Information</i>. Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">10.1371/journal.pcbi.1007642.s001</a>.","ieee":"R. Grah and T. Friedlander, “Supporting information.” Public Library of Science, 2020.","ama":"Grah R, Friedlander T. Supporting information. 2020. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">10.1371/journal.pcbi.1007642.s001</a>","short":"R. Grah, T. Friedlander, (2020).","chicago":"Grah, Rok, and Tamar Friedlander. “Supporting Information.” Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">https://doi.org/10.1371/journal.pcbi.1007642.s001</a>."},"author":[{"last_name":"Grah","full_name":"Grah, Rok","first_name":"Rok","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2539-3560"},{"last_name":"Friedlander","first_name":"Tamar","full_name":"Friedlander, Tamar"}],"publisher":"Public Library of Science","title":"Supporting information","date_published":"2020-02-25T00:00:00Z","date_created":"2021-08-06T07:15:04Z","year":"2020","article_processing_charge":"No"},{"date_published":"2020-02-25T00:00:00Z","title":"Maximizing crosstalk","publisher":"Public Library of Science","article_processing_charge":"No","year":"2020","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1371/journal.pcbi.1007642.s002"}],"date_created":"2021-08-06T07:21:51Z","doi":"10.1371/journal.pcbi.1007642.s002","_id":"9777","status":"public","oa":1,"author":[{"first_name":"Rok","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","full_name":"Grah, Rok","orcid":"0000-0003-2539-3560","last_name":"Grah"},{"first_name":"Tamar","full_name":"Friedlander, Tamar","last_name":"Friedlander"}],"day":"25","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"short":"R. Grah, T. Friedlander, (2020).","ama":"Grah R, Friedlander T. Maximizing crosstalk. 2020. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s002\">10.1371/journal.pcbi.1007642.s002</a>","chicago":"Grah, Rok, and Tamar Friedlander. “Maximizing Crosstalk.” Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s002\">https://doi.org/10.1371/journal.pcbi.1007642.s002</a>.","ieee":"R. Grah and T. Friedlander, “Maximizing crosstalk.” Public Library of Science, 2020.","ista":"Grah R, Friedlander T. 2020. Maximizing crosstalk, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s002\">10.1371/journal.pcbi.1007642.s002</a>.","mla":"Grah, Rok, and Tamar Friedlander. <i>Maximizing Crosstalk</i>. Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s002\">10.1371/journal.pcbi.1007642.s002</a>.","apa":"Grah, R., &#38; Friedlander, T. (2020). Maximizing crosstalk. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s002\">https://doi.org/10.1371/journal.pcbi.1007642.s002</a>"},"department":[{"_id":"GaTk"}],"oa_version":"None","date_updated":"2023-09-12T11:02:25Z","month":"02","related_material":{"record":[{"id":"7569","relation":"used_in_publication","status":"public"}]},"type":"research_data_reference"},{"author":[{"last_name":"Grah","orcid":"0000-0003-2539-3560","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","full_name":"Grah, Rok","first_name":"Rok"},{"full_name":"Friedlander, Tamar","first_name":"Tamar","last_name":"Friedlander"}],"day":"25","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"chicago":"Grah, Rok, and Tamar Friedlander. “Distribution of Crosstalk Values.” Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s003\">https://doi.org/10.1371/journal.pcbi.1007642.s003</a>.","ama":"Grah R, Friedlander T. Distribution of crosstalk values. 2020. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s003\">10.1371/journal.pcbi.1007642.s003</a>","short":"R. Grah, T. Friedlander, (2020).","ieee":"R. Grah and T. Friedlander, “Distribution of crosstalk values.” Public Library of Science, 2020.","mla":"Grah, Rok, and Tamar Friedlander. <i>Distribution of Crosstalk Values</i>. Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s003\">10.1371/journal.pcbi.1007642.s003</a>.","apa":"Grah, R., &#38; Friedlander, T. (2020). Distribution of crosstalk values. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s003\">https://doi.org/10.1371/journal.pcbi.1007642.s003</a>","ista":"Grah R, Friedlander T. 2020. Distribution of crosstalk values, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s003\">10.1371/journal.pcbi.1007642.s003</a>."},"department":[{"_id":"GaTk"}],"oa_version":"Published Version","date_updated":"2023-08-18T06:47:47Z","month":"02","related_material":{"record":[{"status":"public","relation":"research_data","id":"7569"}]},"type":"research_data_reference","doi":"10.1371/journal.pcbi.1007642.s003","_id":"9779","status":"public","article_processing_charge":"No","year":"2020","date_created":"2021-08-06T07:24:37Z","title":"Distribution of crosstalk values","date_published":"2020-02-25T00:00:00Z","publisher":"Public Library of Science"},{"date_published":"2020-03-22T00:00:00Z","title":"CCDC 1991959: Experimental Crystal Structure Determination","publisher":"CCDC","year":"2020","main_file_link":[{"url":"https://dx.doi.org/10.5517/ccdc.csd.cc24vsrk","open_access":"1"}],"article_processing_charge":"No","date_created":"2021-08-06T07:41:07Z","abstract":[{"lang":"eng","text":"PADREV : 4,4'-dimethoxy[1,1'-biphenyl]-2,2',5,5'-tetrol\r\nSpace Group: C 2 (5), Cell: a 24.488(16)Å b 5.981(4)Å c 3.911(3)Å, α 90° β 91.47(3)° γ 90°"}],"doi":"10.5517/ccdc.csd.cc24vsrk","_id":"9780","status":"public","department":[{"_id":"StFr"}],"oa_version":"Published Version","date_updated":"2023-09-05T16:03:47Z","author":[{"last_name":"Schlemmer","full_name":"Schlemmer, Werner","first_name":"Werner"},{"first_name":"Philipp","full_name":"Nothdurft, Philipp","last_name":"Nothdurft"},{"first_name":"Alina","full_name":"Petzold, Alina","last_name":"Petzold"},{"last_name":"Riess","full_name":"Riess, Gisbert","first_name":"Gisbert"},{"first_name":"Philipp","full_name":"Frühwirt, Philipp","last_name":"Frühwirt"},{"last_name":"Schmallegger","first_name":"Max","full_name":"Schmallegger, Max"},{"last_name":"Gescheidt-Demner","full_name":"Gescheidt-Demner, Georg","first_name":"Georg"},{"last_name":"Fischer","full_name":"Fischer, Roland","first_name":"Roland"},{"orcid":"0000-0003-2902-5319","first_name":"Stefan Alexander","id":"A8CA28E6-CE23-11E9-AD2D-EC27E6697425","full_name":"Freunberger, Stefan Alexander","last_name":"Freunberger"},{"last_name":"Kern","first_name":"Wolfgang","full_name":"Kern, Wolfgang"},{"last_name":"Spirk","first_name":"Stefan","full_name":"Spirk, Stefan"}],"oa":1,"citation":{"ista":"Schlemmer W, Nothdurft P, Petzold A, Riess G, Frühwirt P, Schmallegger M, Gescheidt-Demner G, Fischer R, Freunberger SA, Kern W, Spirk S. 2020. CCDC 1991959: Experimental Crystal Structure Determination, CCDC, <a href=\"https://doi.org/10.5517/ccdc.csd.cc24vsrk\">10.5517/ccdc.csd.cc24vsrk</a>.","mla":"Schlemmer, Werner, et al. <i>CCDC 1991959: Experimental Crystal Structure Determination</i>. CCDC, 2020, doi:<a href=\"https://doi.org/10.5517/ccdc.csd.cc24vsrk\">10.5517/ccdc.csd.cc24vsrk</a>.","apa":"Schlemmer, W., Nothdurft, P., Petzold, A., Riess, G., Frühwirt, P., Schmallegger, M., … Spirk, S. (2020). CCDC 1991959: Experimental Crystal Structure Determination. CCDC. <a href=\"https://doi.org/10.5517/ccdc.csd.cc24vsrk\">https://doi.org/10.5517/ccdc.csd.cc24vsrk</a>","ieee":"W. Schlemmer <i>et al.</i>, “CCDC 1991959: Experimental Crystal Structure Determination.” CCDC, 2020.","short":"W. Schlemmer, P. Nothdurft, A. Petzold, G. Riess, P. Frühwirt, M. Schmallegger, G. Gescheidt-Demner, R. Fischer, S.A. Freunberger, W. Kern, S. Spirk, (2020).","ama":"Schlemmer W, Nothdurft P, Petzold A, et al. CCDC 1991959: Experimental Crystal Structure Determination. 2020. doi:<a href=\"https://doi.org/10.5517/ccdc.csd.cc24vsrk\">10.5517/ccdc.csd.cc24vsrk</a>","chicago":"Schlemmer, Werner, Philipp Nothdurft, Alina Petzold, Gisbert Riess, Philipp Frühwirt, Max Schmallegger, Georg Gescheidt-Demner, et al. “CCDC 1991959: Experimental Crystal Structure Determination.” CCDC, 2020. <a href=\"https://doi.org/10.5517/ccdc.csd.cc24vsrk\">https://doi.org/10.5517/ccdc.csd.cc24vsrk</a>."},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","day":"22","month":"03","related_material":{"record":[{"id":"8329","relation":"used_in_publication","status":"public"}]},"type":"research_data_reference"},{"isi":1,"month":"02","related_material":{"record":[{"relation":"dissertation_contains","id":"9733","status":"public"}]},"day":"12","ec_funded":1,"acknowledgement":"We are grateful for the hospitality at the Mittag-Leffler Institute, where part of this work has been done. The work of the authors was supported by the European Research Council (ERC)under the European Union's Horizon 2020 research and innovation programme grant 694227.","publication_status":"published","publication_identifier":{"eissn":["1095-7154"],"issn":["0036-1410"]},"status":"public","doi":"10.1137/19m126284x","abstract":[{"text":"We consider the Pekar functional on a ball in ℝ3. We prove uniqueness of minimizers, and a quadratic lower bound in terms of the distance to the minimizer. The latter follows from nondegeneracy of the Hessian at the minimum.","lang":"eng"}],"license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","issue":"1","article_type":"original","page":"605-622","intvolume":"        52","language":[{"iso":"eng"}],"arxiv":1,"publisher":"Society for Industrial & Applied Mathematics ","type":"journal_article","has_accepted_license":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"chicago":"Feliciangeli, Dario, and Robert Seiringer. “Uniqueness and Nondegeneracy of Minimizers of the Pekar Functional on a Ball.” <i>SIAM Journal on Mathematical Analysis</i>. Society for Industrial &#38; Applied Mathematics , 2020. <a href=\"https://doi.org/10.1137/19m126284x\">https://doi.org/10.1137/19m126284x</a>.","short":"D. Feliciangeli, R. Seiringer, SIAM Journal on Mathematical Analysis 52 (2020) 605–622.","ama":"Feliciangeli D, Seiringer R. Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball. <i>SIAM Journal on Mathematical Analysis</i>. 2020;52(1):605-622. doi:<a href=\"https://doi.org/10.1137/19m126284x\">10.1137/19m126284x</a>","ieee":"D. Feliciangeli and R. Seiringer, “Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball,” <i>SIAM Journal on Mathematical Analysis</i>, vol. 52, no. 1. Society for Industrial &#38; Applied Mathematics , pp. 605–622, 2020.","mla":"Feliciangeli, Dario, and Robert Seiringer. “Uniqueness and Nondegeneracy of Minimizers of the Pekar Functional on a Ball.” <i>SIAM Journal on Mathematical Analysis</i>, vol. 52, no. 1, Society for Industrial &#38; Applied Mathematics , 2020, pp. 605–22, doi:<a href=\"https://doi.org/10.1137/19m126284x\">10.1137/19m126284x</a>.","apa":"Feliciangeli, D., &#38; Seiringer, R. (2020). Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball. <i>SIAM Journal on Mathematical Analysis</i>. Society for Industrial &#38; Applied Mathematics . <a href=\"https://doi.org/10.1137/19m126284x\">https://doi.org/10.1137/19m126284x</a>","ista":"Feliciangeli D, Seiringer R. 2020. Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball. 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While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA. Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations."}],"_id":"9798","status":"public","department":[{"_id":"BeVi"},{"_id":"NiBa"}],"date_updated":"2023-08-25T10:34:41Z","oa_version":"Published Version","author":[{"full_name":"Fraisse, Christelle","id":"32DF5794-F248-11E8-B48F-1D18A9856A87","first_name":"Christelle","orcid":"0000-0001-8441-5075","last_name":"Fraisse"},{"last_name":"Welch","first_name":"John J.","full_name":"Welch, John J."}],"oa":1,"citation":{"ama":"Fraisse C, Welch JJ. Simulation code for Fig S2 from the distribution of epistasis on simple fitness landscapes. 2020. doi:<a href=\"https://doi.org/10.6084/m9.figshare.7957472.v1\">10.6084/m9.figshare.7957472.v1</a>","short":"C. Fraisse, J.J. Welch, (2020).","chicago":"Fraisse, Christelle, and John J. 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