[{"type":"conference","status":"public","date_updated":"2023-02-23T13:57:24Z","department":[{"_id":"DaAl"}],"oa_version":"Published Version","oa":1,"month":"07","author":[{"full_name":"Kurtz, Mark","first_name":"Mark","last_name":"Kurtz"},{"full_name":"Kopinsky, Justin","first_name":"Justin","last_name":"Kopinsky"},{"last_name":"Gelashvili","first_name":"Rati","full_name":"Gelashvili, Rati"},{"full_name":"Matveev, Alexander","first_name":"Alexander","last_name":"Matveev"},{"full_name":"Carr, John","first_name":"John","last_name":"Carr"},{"last_name":"Goin","first_name":"Michael","full_name":"Goin, Michael"},{"last_name":"Leiserson","full_name":"Leiserson, William","first_name":"William"},{"last_name":"Moore","full_name":"Moore, Sage","first_name":"Sage"},{"last_name":"Nell","full_name":"Nell, Bill","first_name":"Bill"},{"first_name":"Nir","full_name":"Shavit, Nir","last_name":"Shavit"},{"orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}],"ddc":["000"],"conference":{"name":"ICML: International Conference on Machine Learning","start_date":"2020-07-12","location":"Online","end_date":"2020-07-18"},"volume":119,"day":"12","article_processing_charge":"No","publication_identifier":{"issn":["2640-3498"]},"citation":{"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.","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.","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.","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.","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.","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.","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."},"year":"2020","_id":"9415","title":"Inducing and exploiting activation sparsity for fast neural network inference","file_date_updated":"2021-05-25T09:51:36Z","page":"5533-5543","publication":"37th International Conference on Machine Learning, ICML 2020","date_created":"2021-05-23T22:01:45Z","language":[{"iso":"eng"}],"date_published":"2020-07-12T00:00:00Z","scopus_import":"1","has_accepted_license":"1","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"}],"file":[{"creator":"kschuh","date_updated":"2021-05-25T09:51:36Z","relation":"main_file","file_id":"9421","file_size":741899,"file_name":"2020_PMLR_Kurtz.pdf","checksum":"2aaaa7d7226e49161311d91627cf783b","access_level":"open_access","success":1,"content_type":"application/pdf","date_created":"2021-05-25T09:51:36Z"}],"quality_controlled":"1","intvolume":"       119","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87"},{"article_processing_charge":"Yes","day":"14","project":[{"name":"Discretization in Geometry and Dynamics","_id":"0aa4bc98-070f-11eb-9043-e6fff9c6a316","grant_number":"I4887"}],"volume":11,"article_type":"original","year":"2020","_id":"9630","publication_identifier":{"eissn":["1920180X"]},"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.","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>","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.","ista":"Edelsbrunner H, Virk Z, Wagner H. 2020. Topological data analysis in information space. Journal of Computational Geometry. 11(2), 162–182.","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>."},"department":[{"_id":"HeEd"}],"status":"public","date_updated":"2021-08-11T12:26:34Z","type":"journal_article","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).","author":[{"full_name":"Edelsbrunner, Herbert","orcid":"0000-0002-9823-6833","first_name":"Herbert","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","last_name":"Edelsbrunner"},{"last_name":"Virk","id":"2E36B656-F248-11E8-B48F-1D18A9856A87","first_name":"Ziga","full_name":"Virk, Ziga"},{"last_name":"Wagner","id":"379CA8B8-F248-11E8-B48F-1D18A9856A87","first_name":"Hubert","full_name":"Wagner, Hubert"}],"ddc":["510","000"],"issue":"2","oa_version":"Published Version","month":"12","oa":1,"tmp":{"short":"CC BY (3.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode"},"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."}],"file":[{"date_created":"2021-08-11T11:55:11Z","content_type":"application/pdf","success":1,"creator":"asandaue","date_updated":"2021-08-11T11:55:11Z","file_name":"2020_JournalOfComputationalGeometry_Edelsbrunner.pdf","relation":"main_file","file_id":"9882","file_size":1449234,"access_level":"open_access","checksum":"f02d0b2b3838e7891a6c417fc34ffdcd"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","intvolume":"        11","quality_controlled":"1","page":"162-182","doi":"10.20382/jocg.v11i2a7","title":"Topological data analysis in information space","file_date_updated":"2021-08-11T11:55:11Z","license":"https://creativecommons.org/licenses/by/3.0/","publisher":"Carleton University","has_accepted_license":"1","scopus_import":"1","publication_status":"published","date_published":"2020-12-14T00:00:00Z","language":[{"iso":"eng"}],"date_created":"2021-07-04T22:01:26Z","publication":"Journal of Computational Geometry"},{"publisher":"Curran Associates","page":"22361-22372","arxiv":1,"title":"Scalable belief propagation via relaxed scheduling","date_published":"2020-12-06T00:00:00Z","publication":"Advances in Neural Information Processing Systems","date_created":"2021-07-04T22:01:26Z","language":[{"iso":"eng"}],"scopus_import":"1","publication_status":"published","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"}],"quality_controlled":"1","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","intvolume":"        33","date_updated":"2023-02-23T14:03:03Z","status":"public","type":"conference","department":[{"_id":"DaAl"}],"external_id":{"arxiv":["2002.11505"]},"oa":1,"month":"12","oa_version":"Published Version","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).","author":[{"last_name":"Aksenov","full_name":"Aksenov, Vitaly","first_name":"Vitaly"},{"last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian"},{"first_name":"Janne","full_name":"Korhonen, Janne","last_name":"Korhonen","id":"C5402D42-15BC-11E9-A202-CA2BE6697425"}],"volume":33,"project":[{"call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning"}],"conference":{"start_date":"2020-12-06","end_date":"2020-12-12","location":"Vancouver, Canada","name":"NeurIPS: Conference on Neural Information Processing Systems"},"main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html","open_access":"1"}],"article_processing_charge":"No","ec_funded":1,"day":"06","citation":{"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.","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.","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.","short":"V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 22361–22372.","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.","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.","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."},"publication_identifier":{"issn":["10495258"],"isbn":["9781713829546"]},"_id":"9631","year":"2020"},{"external_id":{"arxiv":["2004.14340"]},"department":[{"_id":"DaAl"},{"_id":"ToHe"}],"type":"conference","status":"public","date_updated":"2023-02-23T14:03:06Z","author":[{"first_name":"Sidak Pal","full_name":"Singh, Sidak Pal","last_name":"Singh","id":"DD138E24-D89D-11E9-9DC0-DEF6E5697425"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian"}],"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.","oa_version":"Published Version","oa":1,"month":"12","day":"06","ec_funded":1,"main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html","open_access":"1"}],"article_processing_charge":"No","conference":{"start_date":"2020-12-06","end_date":"2020-12-12","location":"Vancouver, Canada","name":"NeurIPS: Conference on Neural Information Processing Systems"},"volume":33,"project":[{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020"}],"_id":"9632","year":"2020","publication_identifier":{"isbn":["9781713829546"],"issn":["10495258"]},"citation":{"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.","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.","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.","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."},"title":"WoodFisher: Efficient second-order approximation for neural network compression","page":"18098-18109","arxiv":1,"publisher":"Curran Associates","scopus_import":"1","publication_status":"published","date_created":"2021-07-04T22:01:26Z","language":[{"iso":"eng"}],"publication":"Advances in Neural Information Processing Systems","date_published":"2020-12-06T00:00:00Z","abstract":[{"lang":"eng","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."}],"intvolume":"        33","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","quality_controlled":"1"},{"related_material":{"link":[{"url":"https://doi.org/10.1101/2020.10.24.353409","relation":"is_continued_by"}],"record":[{"status":"public","relation":"dissertation_contains","id":"14422"}]},"department":[{"_id":"TiVo"}],"date_updated":"2023-10-18T09:20:55Z","status":"public","type":"conference","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.","author":[{"id":"C7610134-B532-11EA-BD9F-F5753DDC885E","last_name":"Confavreux","full_name":"Confavreux, Basile J","first_name":"Basile J"},{"first_name":"Friedemann","full_name":"Zenke, Friedemann","last_name":"Zenke"},{"last_name":"Agnes","first_name":"Everton J.","full_name":"Agnes, Everton J."},{"last_name":"Lillicrap","full_name":"Lillicrap, Timothy","first_name":"Timothy"},{"full_name":"Vogels, Tim P","first_name":"Tim P","orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels"}],"month":"12","oa":1,"oa_version":"Published Version","article_processing_charge":"No","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html","open_access":"1"}],"day":"06","ec_funded":1,"project":[{"_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","grant_number":"214316/Z/18/Z","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks."},{"call_identifier":"H2020","grant_number":"819603","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"conference":{"name":"NeurIPS: Conference on Neural Information Processing Systems","location":"Vancouver, Canada","end_date":"2020-12-12","start_date":"2020-12-06"},"volume":33,"_id":"9633","year":"2020","citation":{"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.","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.","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.","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.","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."},"publication_identifier":{"issn":["1049-5258"]},"page":"16398-16408","title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","scopus_import":"1","publication_status":"published","date_published":"2020-12-06T00:00:00Z","publication":"Advances in Neural Information Processing Systems","language":[{"iso":"eng"}],"date_created":"2021-07-04T22:01:27Z","abstract":[{"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.","lang":"eng"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","intvolume":"        33","quality_controlled":"1"},{"year":"2020","_id":"9706","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"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>","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.","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>","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).","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>.","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>.","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>."},"other_data_license":"CC0 + CC BY (4.0)","day":"09","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)."}],"main_file_link":[{"url":"https://doi.org/10.6084/m9.figshare.12629697.v1","open_access":"1"}],"tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"article_processing_charge":"No","has_accepted_license":"1","author":[{"last_name":"Hillary","full_name":"Hillary, Robert F.","first_name":"Robert F."},{"last_name":"Trejo-Banos","full_name":"Trejo-Banos, Daniel","first_name":"Daniel"},{"last_name":"Kousathanas","first_name":"Athanasios","full_name":"Kousathanas, Athanasios"},{"last_name":"McCartney","full_name":"McCartney, Daniel L.","first_name":"Daniel L."},{"last_name":"Harris","full_name":"Harris, Sarah E.","first_name":"Sarah E."},{"full_name":"Stevenson, Anna J.","first_name":"Anna J.","last_name":"Stevenson"},{"last_name":"Patxot","first_name":"Marion","full_name":"Patxot, Marion"},{"last_name":"Ojavee","first_name":"Sven Erik","full_name":"Ojavee, Sven Erik"},{"last_name":"Zhang","full_name":"Zhang, Qian","first_name":"Qian"},{"last_name":"Liewald","first_name":"David C.","full_name":"Liewald, David C."},{"last_name":"Ritchie","full_name":"Ritchie, Craig W.","first_name":"Craig W."},{"last_name":"Evans","full_name":"Evans, Kathryn L.","first_name":"Kathryn L."},{"last_name":"Tucker-Drob","full_name":"Tucker-Drob, Elliot M.","first_name":"Elliot M."},{"first_name":"Naomi R.","full_name":"Wray, Naomi R.","last_name":"Wray"},{"full_name":"McRae, Allan F. ","first_name":"Allan F. ","last_name":"McRae"},{"last_name":"Visscher","full_name":"Visscher, Peter M.","first_name":"Peter M."},{"last_name":"Deary","first_name":"Ian J.","full_name":"Deary, Ian J."},{"last_name":"Robinson","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard"},{"full_name":"Marioni, Riccardo E. ","first_name":"Riccardo E. ","last_name":"Marioni"}],"oa_version":"Published Version","month":"07","oa":1,"date_created":"2021-07-23T08:59:15Z","date_published":"2020-07-09T00:00:00Z","title":"Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","department":[{"_id":"MaRo"}],"doi":"10.6084/m9.figshare.12629697.v1","related_material":{"record":[{"status":"public","id":"8133","relation":"used_in_publication"}]},"type":"research_data_reference","status":"public","date_updated":"2023-08-22T07:55:36Z","publisher":"Springer Nature"},{"oa_version":"Published Version","month":"05","oa":1,"date_created":"2021-07-23T10:00:35Z","date_published":"2020-05-29T00:00:00Z","author":[{"last_name":"Hartstein","first_name":"Mate","full_name":"Hartstein, Mate"},{"first_name":"Yu-Te","full_name":"Hsu, Yu-Te","last_name":"Hsu"},{"last_name":"Modic","id":"13C26AC0-EB69-11E9-87C6-5F3BE6697425","first_name":"Kimberly A","orcid":"0000-0001-9760-3147","full_name":"Modic, Kimberly A"},{"last_name":"Porras","full_name":"Porras, Juan","first_name":"Juan"},{"last_name":"Loew","full_name":"Loew, Toshinao","first_name":"Toshinao"},{"full_name":"Le Tacon, Matthieu","first_name":"Matthieu","last_name":"Le Tacon"},{"last_name":"Zuo","first_name":"Huakun","full_name":"Zuo, Huakun"},{"last_name":"Wang","full_name":"Wang, Jinhua","first_name":"Jinhua"},{"full_name":"Zhu, Zengwei","first_name":"Zengwei","last_name":"Zhu"},{"last_name":"Chan","full_name":"Chan, Mun","first_name":"Mun"},{"first_name":"Ross","full_name":"McDonald, Ross","last_name":"McDonald"},{"last_name":"Lonzarich","first_name":"Gilbert","full_name":"Lonzarich, Gilbert"},{"last_name":"Keimer","first_name":"Bernhard","full_name":"Keimer, Bernhard"},{"last_name":"Sebastian","first_name":"Suchitra","full_name":"Sebastian, Suchitra"},{"first_name":"Neil","full_name":"Harrison, Neil","last_name":"Harrison"}],"has_accepted_license":"1","type":"research_data_reference","status":"public","date_updated":"2023-08-21T07:06:48Z","publisher":"Apollo - University of Cambridge","title":"Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'","department":[{"_id":"KiMo"}],"related_material":{"record":[{"status":"public","id":"7942","relation":"used_in_publication"}]},"doi":"10.17863/cam.50169","citation":{"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>","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>","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>.","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).","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>.","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>."},"year":"2020","_id":"9708","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","abstract":[{"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.","lang":"eng"}],"day":"29","article_processing_charge":"No","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"main_file_link":[{"url":"https://doi.org/10.17863/CAM.50169","open_access":"1"}]},{"date_published":"2020-05-20T00:00:00Z","date_created":"2021-07-23T12:02:39Z","month":"05","oa_version":"Published Version","author":[{"full_name":"Gupta, Chitrak","first_name":"Chitrak","last_name":"Gupta"},{"last_name":"Khaniya","full_name":"Khaniya, Umesh","first_name":"Umesh"},{"last_name":"Chan","first_name":"Chun Kit","full_name":"Chan, Chun Kit"},{"full_name":"Dehez, Francois","first_name":"Francois","last_name":"Dehez"},{"last_name":"Shekhar","full_name":"Shekhar, Mrinal","first_name":"Mrinal"},{"first_name":"M.R.","full_name":"Gunner, M.R.","last_name":"Gunner"},{"orcid":"0000-0002-0977-7989","first_name":"Leonid A","full_name":"Sazanov, Leonid A","last_name":"Sazanov","id":"338D39FE-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Chipot","full_name":"Chipot, Christophe","first_name":"Christophe"},{"last_name":"Singharoy","first_name":"Abhishek","full_name":"Singharoy, Abhishek"}],"publisher":"American Chemical Society ","date_updated":"2023-08-22T07:49:38Z","status":"public","type":"research_data_reference","related_material":{"record":[{"relation":"used_in_publication","id":"8040","status":"public"}]},"doi":"10.1021/jacs.9b13450.s001","department":[{"_id":"LeSa"}],"title":"Supporting information","citation":{"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>.","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>.","short":"C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (2020).","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>.","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>","ieee":"C. Gupta <i>et al.</i>, “Supporting information.” American Chemical Society , 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>"},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","_id":"9713","year":"2020","article_processing_charge":"No","abstract":[{"lang":"eng","text":"Additional analyses of the trajectories"}],"day":"20"},{"date_updated":"2024-03-25T23:30:10Z","publisher":"Cold Spring Harbor Laboratory","status":"public","type":"preprint","page":"41","related_material":{"record":[{"status":"public","relation":"later_version","id":"10766"},{"status":"public","relation":"dissertation_contains","id":"9623"}]},"doi":"10.1101/2020.11.20.391284","department":[{"_id":"CaHe"},{"_id":"EM-Fac"},{"_id":"Bio"}],"title":"Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion","date_published":"2020-11-20T00:00:00Z","date_created":"2021-07-29T11:29:50Z","oa":1,"language":[{"iso":"eng"}],"month":"11","publication":"bioRxiv","oa_version":"Preprint","author":[{"first_name":"Jana","full_name":"Slovakova, Jana","last_name":"Slovakova","id":"30F3F2F0-F248-11E8-B48F-1D18A9856A87"},{"id":"2F74BCDE-F248-11E8-B48F-1D18A9856A87","last_name":"Sikora","full_name":"Sikora, Mateusz K","first_name":"Mateusz K"},{"last_name":"Caballero Mancebo","id":"2F1E1758-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5223-3346","first_name":"Silvia","full_name":"Caballero Mancebo, Silvia"},{"full_name":"Krens, Gabriel","first_name":"Gabriel","orcid":"0000-0003-4761-5996","id":"2B819732-F248-11E8-B48F-1D18A9856A87","last_name":"Krens"},{"last_name":"Kaufmann","id":"3F99E422-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9735-5315","first_name":"Walter","full_name":"Kaufmann, Walter"},{"first_name":"Karla","full_name":"Huljev, Karla","last_name":"Huljev","id":"44C6F6A6-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Heisenberg","id":"39427864-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0912-4566","first_name":"Carl-Philipp J","full_name":"Heisenberg, Carl-Philipp J"}],"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","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"},{"call_identifier":"H2020","_id":"260F1432-B435-11E9-9278-68D0E5697425","grant_number":"742573","name":"Interaction and feedback between cell mechanics and fate specification in vertebrate gastrulation"},{"name":"Modulation of adhesion function in cell-cell contact formation by cortical tension","_id":"2521E28E-B435-11E9-9278-68D0E5697425","grant_number":"187-2013"}],"article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2020.11.20.391284"}],"abstract":[{"lang":"eng","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."}],"ec_funded":1,"day":"20","acknowledged_ssus":[{"_id":"Bio"},{"_id":"EM-Fac"},{"_id":"SSU"}],"citation":{"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>.","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>","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>","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>.","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>."},"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","year":"2020","_id":"9750"},{"date_published":"2020-02-25T00:00:00Z","oa_version":"Published Version","date_created":"2021-08-06T07:15:04Z","month":"02","author":[{"last_name":"Grah","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","first_name":"Rok","orcid":"0000-0003-2539-3560","full_name":"Grah, Rok"},{"first_name":"Tamar","full_name":"Friedlander, Tamar","last_name":"Friedlander"}],"status":"public","publisher":"Public Library of Science","date_updated":"2023-08-18T06:47:47Z","type":"research_data_reference","department":[{"_id":"GaTk"}],"doi":"10.1371/journal.pcbi.1007642.s001","related_material":{"record":[{"status":"public","id":"7569","relation":"used_in_publication"}]},"title":"Supporting information","citation":{"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>","ieee":"R. Grah and T. Friedlander, “Supporting information.” Public Library of Science, 2020.","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>","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>.","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>.","ista":"Grah R, Friedlander T. 2020. 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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>.","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>.","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).","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>","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>","ieee":"W. Schlemmer <i>et al.</i>, “CCDC 1991959: Experimental Crystal Structure Determination.” CCDC, 2020."},"day":"22","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°"}],"article_processing_charge":"No","main_file_link":[{"url":"https://dx.doi.org/10.5517/ccdc.csd.cc24vsrk","open_access":"1"}]},{"language":[{"iso":"eng"}],"publication":"SIAM Journal on Mathematical Analysis","date_created":"2021-08-06T07:34:16Z","date_published":"2020-02-12T00:00:00Z","scopus_import":"1","publication_status":"published","isi":1,"has_accepted_license":"1","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","publisher":"Society for Industrial & Applied Mathematics ","title":"Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball","doi":"10.1137/19m126284x","arxiv":1,"page":"605-622","quality_controlled":"1","intvolume":"        52","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"lang":"eng","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."}],"tmp":{"short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode"},"oa":1,"month":"02","oa_version":"Preprint","issue":"1","ddc":["510"],"author":[{"id":"41A639AA-F248-11E8-B48F-1D18A9856A87","last_name":"Feliciangeli","full_name":"Feliciangeli, Dario","first_name":"Dario","orcid":"0000-0003-0754-8530"},{"full_name":"Seiringer, Robert","first_name":"Robert","orcid":"0000-0002-6781-0521","id":"4AFD0470-F248-11E8-B48F-1D18A9856A87","last_name":"Seiringer"}],"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.","type":"journal_article","date_updated":"2023-09-07T13:30:11Z","status":"public","keyword":["Applied Mathematics","Computational Mathematics","Analysis"],"external_id":{"arxiv":["1904.08647 "],"isi":["000546967700022"]},"related_material":{"record":[{"relation":"dissertation_contains","id":"9733","status":"public"}]},"department":[{"_id":"RoSe"}],"citation":{"ista":"Feliciangeli D, Seiringer R. 2020. Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball. SIAM Journal on Mathematical Analysis. 52(1), 605–622.","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>","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. 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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.","lang":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.6084/m9.figshare.7957472.v1"}],"article_processing_charge":"No","year":"2020","_id":"9798","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"ieee":"C. Fraisse and J. J. 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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."}],"day":"15","author":[{"full_name":"Fraisse, Christelle","orcid":"0000-0001-8441-5075","first_name":"Christelle","id":"32DF5794-F248-11E8-B48F-1D18A9856A87","last_name":"Fraisse"},{"last_name":"Welch","full_name":"Welch, John J.","first_name":"John J."}],"date_published":"2020-10-15T00:00:00Z","date_created":"2021-08-06T11:26:57Z","month":"10","oa":1,"oa_version":"Published Version","related_material":{"record":[{"relation":"used_in_publication","id":"6467","status":"public"}]},"doi":"10.6084/m9.figshare.7957469.v1","department":[{"_id":"BeVi"},{"_id":"NiBa"}],"title":"Simulation code for Fig S1 from the distribution of epistasis on simple fitness landscapes","publisher":"Royal Society of London","date_updated":"2023-08-25T10:34:41Z","status":"public","type":"research_data_reference"},{"oa_version":"Published Version","date_created":"2021-08-06T13:09:57Z","month":"10","oa":1,"date_published":"2020-10-15T00:00:00Z","author":[{"orcid":"0000-0003-4783-0389","first_name":"Rasmus","full_name":"Ibsen-Jensen, Rasmus","last_name":"Ibsen-Jensen","id":"3B699956-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Tkadlec, Josef","orcid":"0000-0002-1097-9684","first_name":"Josef","id":"3F24CCC8-F248-11E8-B48F-1D18A9856A87","last_name":"Tkadlec"},{"last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu","orcid":"0000-0002-4561-241X","full_name":"Chatterjee, Krishnendu"},{"full_name":"Nowak, Martin","first_name":"Martin","last_name":"Nowak"}],"type":"research_data_reference","status":"public","date_updated":"2023-10-18T06:36:00Z","publisher":"Royal Society","title":"Data and mathematica notebooks for plotting figures from language learning with communication between learners from language acquisition with communication between learners","department":[{"_id":"KrCh"}],"related_material":{"record":[{"status":"public","id":"198","relation":"used_in_publication"}]},"doi":"10.6084/m9.figshare.5973013.v1","citation":{"mla":"Ibsen-Jensen, Rasmus, et al. <i>Data and Mathematica Notebooks for Plotting Figures from Language Learning with Communication between Learners from Language Acquisition with Communication between Learners</i>. 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The evaluation of the signals is based on rich temporal specifications expressed in extended signal temporal logic, which integrates timed regular expressions within signal temporal logic. The tool features qualitative monitoring (property satisfaction checking), trace diagnostics for explaining and justifying property violations and specification-driven measurement of quantitative features of the signal. 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Springer Nature. <a href=\"https://doi.org/10.1007/s10009-020-00582-z\">https://doi.org/10.1007/s10009-020-00582-z</a>","short":"D. Nickovic, O. Lebeltel, O. Maler, T. Ferrere, D. Ulus, International Journal on Software Tools for Technology Transfer 22 (2020) 741–758.","chicago":"Nickovic, Dejan, Olivier Lebeltel, Oded Maler, Thomas Ferrere, and Dogan Ulus. “AMT 2.0: Qualitative and Quantitative Trace Analysis with Extended Signal Temporal Logic.” <i>International Journal on Software Tools for Technology Transfer</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1007/s10009-020-00582-z\">https://doi.org/10.1007/s10009-020-00582-z</a>.","ama":"Nickovic D, Lebeltel O, Maler O, Ferrere T, Ulus D. AMT 2.0: Qualitative and quantitative trace analysis with extended signal temporal logic. <i>International Journal on Software Tools for Technology Transfer</i>. 2020;22(6):741-758. doi:<a href=\"https://doi.org/10.1007/s10009-020-00582-z\">10.1007/s10009-020-00582-z</a>","ieee":"D. Nickovic, O. Lebeltel, O. Maler, T. Ferrere, and D. Ulus, “AMT 2.0: Qualitative and quantitative trace analysis with extended signal temporal logic,” <i>International Journal on Software Tools for Technology Transfer</i>, vol. 22, no. 6. Springer Nature, pp. 741–758, 2020.","ista":"Nickovic D, Lebeltel O, Maler O, Ferrere T, Ulus D. 2020. AMT 2.0: Qualitative and quantitative trace analysis with extended signal temporal logic. 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