[{"oa_version":"Published Version","type":"journal_article","date_updated":"2023-08-17T13:53:14Z","day":"01","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_processing_charge":"No","department":[{"_id":"JoCs"}],"has_accepted_license":"1","language":[{"iso":"eng"}],"license":"https://creativecommons.org/licenses/by/4.0/","doi":"10.1002/hipo.23144","file":[{"file_id":"6800","date_updated":"2020-07-14T12:47:40Z","checksum":"7b54d22bfbfc0d1188a9ea24d985bfb2","file_name":"2019_Hippocampus_Stella.pdf","access_level":"open_access","relation":"main_file","date_created":"2019-08-12T07:53:33Z","creator":"dernst","content_type":"application/pdf","file_size":2370658}],"external_id":{"isi":["000477299600001"],"pmid":["31339190"]},"date_published":"2020-04-01T00:00:00Z","page":"302-313","scopus_import":"1","publication_identifier":{"eissn":["10981063"],"issn":["10509631"]},"tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"year":"2020","citation":{"ista":"Stella F, Urdapilleta E, Luo Y, Treves A. 2020. Partial coherence and frustration in self-organizing spherical grids. Hippocampus. 30(4), 302–313.","chicago":"Stella, Federico, Eugenio Urdapilleta, Yifan Luo, and Alessandro Treves. “Partial Coherence and Frustration in Self-Organizing Spherical Grids.” <i>Hippocampus</i>. Wiley, 2020. <a href=\"https://doi.org/10.1002/hipo.23144\">https://doi.org/10.1002/hipo.23144</a>.","short":"F. Stella, E. Urdapilleta, Y. Luo, A. Treves, Hippocampus 30 (2020) 302–313.","ama":"Stella F, Urdapilleta E, Luo Y, Treves A. Partial coherence and frustration in self-organizing spherical grids. <i>Hippocampus</i>. 2020;30(4):302-313. doi:<a href=\"https://doi.org/10.1002/hipo.23144\">10.1002/hipo.23144</a>","ieee":"F. Stella, E. Urdapilleta, Y. Luo, and A. Treves, “Partial coherence and frustration in self-organizing spherical grids,” <i>Hippocampus</i>, vol. 30, no. 4. Wiley, pp. 302–313, 2020.","apa":"Stella, F., Urdapilleta, E., Luo, Y., &#38; Treves, A. (2020). Partial coherence and frustration in self-organizing spherical grids. <i>Hippocampus</i>. Wiley. <a href=\"https://doi.org/10.1002/hipo.23144\">https://doi.org/10.1002/hipo.23144</a>","mla":"Stella, Federico, et al. “Partial Coherence and Frustration in Self-Organizing Spherical Grids.” <i>Hippocampus</i>, vol. 30, no. 4, Wiley, 2020, pp. 302–13, doi:<a href=\"https://doi.org/10.1002/hipo.23144\">10.1002/hipo.23144</a>."},"author":[{"last_name":"Stella","first_name":"Federico","id":"39AF1E74-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9439-3148","full_name":"Stella, Federico"},{"first_name":"Eugenio","last_name":"Urdapilleta","full_name":"Urdapilleta, Eugenio"},{"first_name":"Yifan","last_name":"Luo","full_name":"Luo, Yifan"},{"full_name":"Treves, Alessandro","last_name":"Treves","first_name":"Alessandro"}],"quality_controlled":"1","abstract":[{"text":"Nearby grid cells have been observed to express a remarkable degree of long-rangeorder, which is often idealized as extending potentially to infinity. Yet their strict peri-odic firing and ensemble coherence are theoretically possible only in flat environments, much unlike the burrows which rodents usually live in. Are the symmetrical, coherent grid maps inferred in the lab relevant to chart their way in their natural habitat? We consider spheres as simple models of curved environments and waiting for the appropriate experiments to be performed, we use our adaptation model to predict what grid maps would emerge in a network with the same type of recurrent connections, which on the plane produce coherence among the units. We find that on the sphere such connections distort the maps that single grid units would express on their own, and aggregate them into clusters. When remapping to a different spherical environment, units in each cluster maintain only partial coherence, similar to what is observed in disordered materials, such as spin glasses.","lang":"eng"}],"publication_status":"published","pmid":1,"publication":"Hippocampus","title":"Partial coherence and frustration in self-organizing spherical grids","_id":"6796","ddc":["570"],"oa":1,"file_date_updated":"2020-07-14T12:47:40Z","publisher":"Wiley","article_type":"original","issue":"4","volume":30,"month":"04","date_created":"2019-08-11T21:59:24Z","status":"public","intvolume":"        30","isi":1},{"doi":"10.1016/j.ymeth.2019.07.019","language":[{"iso":"eng"}],"department":[{"_id":"JoDa"}],"day":"01","date_updated":"2023-08-17T13:59:57Z","type":"journal_article","oa_version":"Submitted Version","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_processing_charge":"No","publication_identifier":{"issn":["1046-2023"]},"scopus_import":"1","page":"27-41","date_published":"2020-03-01T00:00:00Z","external_id":{"isi":["000525860400005"],"pmid":["31344404"]},"publication_status":"published","abstract":[{"lang":"eng","text":"Super-resolution fluorescence microscopy has become an important catalyst for discovery in the life sciences. In STimulated Emission Depletion (STED) microscopy, a pattern of light drives fluorophores from a signal-emitting on-state to a non-signalling off-state. Only emitters residing in a sub-diffraction volume around an intensity minimum are allowed to fluoresce, rendering them distinguishable from the nearby, but dark fluorophores. STED routinely achieves resolution in the few tens of nanometers range in biological samples and is suitable for live imaging. Here, we review the working principle of STED and provide general guidelines for successful STED imaging. The strive for ever higher resolution comes at the cost of increased light burden. We discuss techniques to reduce light exposure and mitigate its detrimental effects on the specimen. These include specialized illumination strategies as well as protecting fluorophores from photobleaching mediated by high-intensity STED light. This opens up the prospect of volumetric imaging in living cells and tissues with diffraction-unlimited resolution in all three spatial dimensions."}],"project":[{"call_identifier":"FWF","_id":"265CB4D0-B435-11E9-9278-68D0E5697425","grant_number":"I03600","name":"Optical control of synaptic function via adhesion molecules"},{"grant_number":"LT00057","name":"High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration","_id":"2668BFA0-B435-11E9-9278-68D0E5697425"}],"oa":1,"_id":"6808","publication":"Methods","title":"Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens","pmid":1,"year":"2020","quality_controlled":"1","author":[{"id":"425C1CE8-F248-11E8-B48F-1D18A9856A87","full_name":"Jahr, Wiebke","last_name":"Jahr","first_name":"Wiebke"},{"last_name":"Velicky","first_name":"Philipp","id":"39BDC62C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2340-7431","full_name":"Velicky, Philipp"},{"first_name":"Johann G","last_name":"Danzl","id":"42EFD3B6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8559-3973","full_name":"Danzl, Johann G"}],"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100895/"}],"citation":{"ieee":"W. Jahr, P. Velicky, and J. G. Danzl, “Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens,” <i>Methods</i>, vol. 174, no. 3. Elsevier, pp. 27–41, 2020.","ama":"Jahr W, Velicky P, Danzl JG. Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens. <i>Methods</i>. 2020;174(3):27-41. doi:<a href=\"https://doi.org/10.1016/j.ymeth.2019.07.019\">10.1016/j.ymeth.2019.07.019</a>","short":"W. Jahr, P. Velicky, J.G. Danzl, Methods 174 (2020) 27–41.","chicago":"Jahr, Wiebke, Philipp Velicky, and Johann G Danzl. “Strategies to Maximize Performance in STimulated Emission Depletion (STED) Nanoscopy of Biological Specimens.” <i>Methods</i>. Elsevier, 2020. <a href=\"https://doi.org/10.1016/j.ymeth.2019.07.019\">https://doi.org/10.1016/j.ymeth.2019.07.019</a>.","ista":"Jahr W, Velicky P, Danzl JG. 2020. Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens. Methods. 174(3), 27–41.","mla":"Jahr, Wiebke, et al. “Strategies to Maximize Performance in STimulated Emission Depletion (STED) Nanoscopy of Biological Specimens.” <i>Methods</i>, vol. 174, no. 3, Elsevier, 2020, pp. 27–41, doi:<a href=\"https://doi.org/10.1016/j.ymeth.2019.07.019\">10.1016/j.ymeth.2019.07.019</a>.","apa":"Jahr, W., Velicky, P., &#38; Danzl, J. G. (2020). Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens. <i>Methods</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.ymeth.2019.07.019\">https://doi.org/10.1016/j.ymeth.2019.07.019</a>"},"status":"public","date_created":"2019-08-12T16:36:32Z","month":"03","isi":1,"intvolume":"       174","publisher":"Elsevier","article_type":"original","volume":174,"issue":"3"},{"page":"1311-1395","external_id":{"isi":["000536053300012"],"arxiv":["1812.03086"]},"date_published":"2020-06-01T00:00:00Z","arxiv":1,"scopus_import":"1","publication_identifier":{"eissn":["1432-0916"],"issn":["0010-3616"]},"day":"01","oa_version":"Preprint","date_updated":"2024-02-22T13:33:02Z","type":"journal_article","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","acknowledgement":"We would like to thank P. T. Nam and R. Seiringer for several useful discussions and\r\nfor suggesting us to use the localization techniques from [9]. C. Boccato has received funding from the\r\nEuropean Research Council (ERC) under the programme Horizon 2020 (Grant Agreement 694227). B. Schlein gratefully acknowledges support from the NCCR SwissMAP and from the Swiss National Foundation of Science (Grant No. 200020_1726230) through the SNF Grant “Dynamical and energetic properties of Bose–Einstein condensates”.","department":[{"_id":"RoSe"}],"language":[{"iso":"eng"}],"doi":"10.1007/s00220-019-03555-9","ec_funded":1,"article_type":"original","publisher":"Springer","volume":376,"status":"public","month":"06","date_created":"2019-09-24T17:30:59Z","isi":1,"intvolume":"       376","year":"2020","quality_controlled":"1","author":[{"id":"342E7E22-F248-11E8-B48F-1D18A9856A87","full_name":"Boccato, Chiara","last_name":"Boccato","first_name":"Chiara"},{"last_name":"Brennecke","first_name":"Christian","full_name":"Brennecke, Christian"},{"full_name":"Cenatiempo, Serena","last_name":"Cenatiempo","first_name":"Serena"},{"first_name":"Benjamin","last_name":"Schlein","full_name":"Schlein, Benjamin"}],"citation":{"ieee":"C. Boccato, C. Brennecke, S. Cenatiempo, and B. Schlein, “Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime,” <i>Communications in Mathematical Physics</i>, vol. 376. Springer, pp. 1311–1395, 2020.","ama":"Boccato C, Brennecke C, Cenatiempo S, Schlein B. Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime. <i>Communications in Mathematical Physics</i>. 2020;376:1311-1395. doi:<a href=\"https://doi.org/10.1007/s00220-019-03555-9\">10.1007/s00220-019-03555-9</a>","chicago":"Boccato, Chiara, Christian Brennecke, Serena Cenatiempo, and Benjamin Schlein. “Optimal Rate for Bose-Einstein Condensation in the Gross-Pitaevskii Regime.” <i>Communications in Mathematical Physics</i>. Springer, 2020. <a href=\"https://doi.org/10.1007/s00220-019-03555-9\">https://doi.org/10.1007/s00220-019-03555-9</a>.","short":"C. Boccato, C. Brennecke, S. Cenatiempo, B. Schlein, Communications in Mathematical Physics 376 (2020) 1311–1395.","ista":"Boccato C, Brennecke C, Cenatiempo S, Schlein B. 2020. Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime. Communications in Mathematical Physics. 376, 1311–1395.","apa":"Boccato, C., Brennecke, C., Cenatiempo, S., &#38; Schlein, B. (2020). Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime. <i>Communications in Mathematical Physics</i>. Springer. <a href=\"https://doi.org/10.1007/s00220-019-03555-9\">https://doi.org/10.1007/s00220-019-03555-9</a>","mla":"Boccato, Chiara, et al. “Optimal Rate for Bose-Einstein Condensation in the Gross-Pitaevskii Regime.” <i>Communications in Mathematical Physics</i>, vol. 376, Springer, 2020, pp. 1311–95, doi:<a href=\"https://doi.org/10.1007/s00220-019-03555-9\">10.1007/s00220-019-03555-9</a>."},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1812.03086"}],"abstract":[{"lang":"eng","text":"We consider systems of bosons trapped in a box, in the Gross–Pitaevskii regime. We show that low-energy states exhibit complete Bose–Einstein condensation with an optimal bound on the number of orthogonal excitations. This extends recent results obtained in Boccato et al. (Commun Math Phys 359(3):975–1026, 2018), removing the assumption of small interaction potential."}],"publication_status":"published","project":[{"grant_number":"694227","name":"Analysis of quantum many-body systems","call_identifier":"H2020","_id":"25C6DC12-B435-11E9-9278-68D0E5697425"}],"_id":"6906","oa":1,"publication":"Communications in Mathematical Physics","title":"Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime"},{"date_updated":"2023-09-07T13:30:45Z","type":"preprint","oa_version":"Preprint","day":"11","year":"2020","main_file_link":[{"url":"https://arxiv.org/abs/2003.05478","open_access":"1"}],"citation":{"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.","short":"J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.).","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.","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>. .","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>."},"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","article_processing_charge":"No","author":[{"id":"2C12A0B0-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0479-558X","full_name":"Fischer, Julian L","first_name":"Julian L","last_name":"Fischer"},{"full_name":"Hensel, Sebastian","orcid":"0000-0001-7252-8072","id":"4D23B7DA-F248-11E8-B48F-1D18A9856A87","last_name":"Hensel","first_name":"Sebastian"},{"full_name":"Laux, Tim","last_name":"Laux","first_name":"Tim"},{"last_name":"Simon","first_name":"Thilo","full_name":"Simon, Thilo"}],"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.","project":[{"grant_number":"665385","name":"International IST Doctoral Program","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"}],"publication_status":"submitted","abstract":[{"lang":"eng","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."}],"publication":"arXiv","ec_funded":1,"title":"The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions","language":[{"iso":"eng"}],"oa":1,"related_material":{"record":[{"id":"10007","status":"public","relation":"dissertation_contains"}]},"department":[{"_id":"JuFi"}],"_id":"10012","date_published":"2020-03-11T00:00:00Z","external_id":{"arxiv":["2003.05478"]},"date_created":"2021-09-13T12:17:11Z","arxiv":1,"month":"03","status":"public","article_number":"2003.05478"},{"page":"33","date_published":"2020-08-25T00:00:00Z","external_id":{"arxiv":["2008.10962"]},"article_number":"2008.10962","status":"public","date_created":"2021-09-17T10:57:27Z","arxiv":1,"month":"08","day":"25","year":"2020","date_updated":"2023-09-07T13:31:05Z","type":"preprint","oa_version":"Preprint","author":[{"id":"35C79D68-F248-11E8-B48F-1D18A9856A87","full_name":"Forkert, Dominik L","last_name":"Forkert","first_name":"Dominik L"},{"last_name":"Maas","first_name":"Jan","full_name":"Maas, Jan","orcid":"0000-0002-0845-1338","id":"4C5696CE-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Portinale","first_name":"Lorenzo","id":"30AD2CBC-F248-11E8-B48F-1D18A9856A87","full_name":"Portinale, Lorenzo"}],"article_processing_charge":"No","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","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>.","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.","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.","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."},"main_file_link":[{"url":"https://arxiv.org/abs/2008.10962","open_access":"1"}],"publication_status":"submitted","abstract":[{"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.","lang":"eng"}],"project":[{"name":"Optimal Transport and Stochastic Dynamics","grant_number":"716117","_id":"256E75B8-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"name":"Taming Complexity in Partial Differential Systems","grant_number":"F6504","_id":"fc31cba2-9c52-11eb-aca3-ff467d239cd2"}],"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.","related_material":{"record":[{"status":"public","relation":"later_version","id":"11739"},{"id":"10030","relation":"dissertation_contains","status":"public"}]},"language":[{"iso":"eng"}],"oa":1,"department":[{"_id":"JaMa"}],"_id":"10022","publication":"arXiv","ec_funded":1,"title":"Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions"},{"day":"01","year":"2020","date_updated":"2023-10-18T08:32:34Z","type":"conference","oa_version":"None","article_processing_charge":"No","quality_controlled":"1","author":[{"full_name":"Lambert, Nicholas J.","first_name":"Nicholas J.","last_name":"Lambert"},{"last_name":"Mobassem","first_name":"Sonia","full_name":"Mobassem, Sonia"},{"last_name":"Rueda Sanchez","first_name":"Alfredo R","id":"3B82B0F8-F248-11E8-B48F-1D18A9856A87","full_name":"Rueda Sanchez, Alfredo R","orcid":"0000-0001-6249-5860"},{"full_name":"Schwefel, Harald G.L.","first_name":"Harald G.L.","last_name":"Schwefel"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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>.","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>"},"publication_status":"published","abstract":[{"lang":"eng","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."}],"language":[{"iso":"eng"}],"doi":"10.1364/QUANTUM.2020.QTu8A.1","_id":"10328","department":[{"_id":"JoFi"}],"publication":"OSA Quantum 2.0 Conference","title":"New designs and noise channels in electro-optic microwave to optical up-conversion","publisher":"Optica Publishing Group","date_published":"2020-01-01T00:00:00Z","article_number":"QTu8A.1","status":"public","date_created":"2021-11-21T23:01:31Z","month":"01","conference":{"location":"Washington, DC, United States","start_date":"2020-09-14","end_date":"2020-09-17","name":"OSA: Optical Society of America"},"publication_identifier":{"isbn":["9-781-5575-2820-9"]},"scopus_import":"1","alternative_title":["OSA Technical Digest"]},{"author":[{"last_name":"Kokoris Kogias","first_name":"Eleftherios","full_name":"Kokoris Kogias, Eleftherios","id":"f5983044-d7ef-11ea-ac6d-fd1430a26d30"},{"last_name":"Malkhi","first_name":"Dahlia","full_name":"Malkhi, Dahlia"},{"last_name":"Spiegelman","first_name":"Alexander","full_name":"Spiegelman, Alexander"}],"quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://eprint.iacr.org/2019/1015"}],"citation":{"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>.","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>","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>","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.","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.","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>.","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."},"year":"2020","oa":1,"_id":"10556","title":"Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures","publication":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","publication_status":"published","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."}],"publisher":"Association for Computing Machinery","isi":1,"status":"public","date_created":"2021-12-16T13:23:27Z","conference":{"name":"CCS: Computer and Communications Security","end_date":"2020-11-13","start_date":"2020-11-09","location":"Virtual, United States"},"month":"10","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","day":"30","date_updated":"2024-02-22T13:10:45Z","type":"conference","oa_version":"Preprint","language":[{"iso":"eng"}],"doi":"10.1145/3372297.3423364","department":[{"_id":"ElKo"}],"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.","page":"1751–1767","date_published":"2020-10-30T00:00:00Z","external_id":{"isi":["000768470400104"]},"publication_identifier":{"isbn":["978-1-4503-7089-9"]},"scopus_import":"1"},{"department":[{"_id":"ElKo"}],"_id":"10557","related_material":{"link":[{"url":"https://patents.google.com/patent/US20180359096A1/en","relation":"earlier_version"}]},"oa":1,"title":"Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods","abstract":[{"lang":"eng","text":"Data storage and retrieval systems, methods, and computer-readable media utilize a cryptographically verifiable data structure that facilitates verification of a transaction in a decentralized peer-to-peer environment using multi-hop backwards and forwards links. Backward links are cryptographic hashes of past records. Forward links are cryptographic signatures of future records that are added retroactively to records once the target block has been appended to the data structure."}],"applicant":["Ecole Polytechnique Federale de Lausanne"],"article_processing_charge":"No","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","extern":"1","author":[{"first_name":"Bryan","last_name":"Ford","full_name":"Ford, Bryan"},{"full_name":"Gasse, Linus","first_name":"Linus","last_name":"Gasse"},{"id":"f5983044-d7ef-11ea-ac6d-fd1430a26d30","full_name":"Kokoris Kogias, Eleftherios","last_name":"Kokoris Kogias","first_name":"Eleftherios"},{"full_name":"Jovanovic, Philipp","first_name":"Philipp","last_name":"Jovanovic"}],"ipc":" H04L9/3247 ; G06Q20/29 ; G06Q20/382 ; H04L9/3236","main_file_link":[{"open_access":"1","url":"https://patents.google.com/patent/US10581613B2/en"}],"citation":{"ama":"Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods. 2020.","ieee":"B. Ford, L. Gasse, E. Kokoris Kogias, and P. Jovanovic, “Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods.” 2020.","ista":"Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. 2020. Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods.","short":"B. Ford, L. Gasse, E. Kokoris Kogias, P. Jovanovic, (2020).","chicago":"Ford, Bryan, Linus Gasse, Eleftherios Kokoris Kogias, and Philipp Jovanovic. “Cryptographically Verifiable Data Structure Having Multi-Hop Forward and Backwards Links and Associated Systems and Methods,” 2020.","mla":"Ford, Bryan, et al. <i>Cryptographically Verifiable Data Structure Having Multi-Hop Forward and Backwards Links and Associated Systems and Methods</i>. 2020.","apa":"Ford, B., Gasse, L., Kokoris Kogias, E., &#38; Jovanovic, P. (2020). Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods."},"year":"2020","ipn":"10581613","day":"03","oa_version":"Published Version","date_updated":"2021-12-21T10:04:50Z","type":"patent","status":"public","application_date":"2017-06-09","month":"03","date_created":"2021-12-16T13:28:59Z","date_published":"2020-03-03T00:00:00Z","publication_date":"2020-03-03"},{"scopus_import":"1","file":[{"file_size":249431,"creator":"mlechner","content_type":"application/pdf","date_created":"2022-01-26T07:35:17Z","relation":"main_file","access_level":"open_access","file_name":"iclr_2020.pdf","checksum":"ea13d42dd4541ddb239b6a75821fd6c9","date_updated":"2022-01-26T07:35:17Z","file_id":"10677","success":1}],"date_published":"2020-03-11T00:00:00Z","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"has_accepted_license":"1","license":"https://creativecommons.org/licenses/by-nc-nd/3.0/","language":[{"iso":"eng"}],"acknowledgement":"This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23\r\n(Wittgenstein Award).\r\n","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","day":"11","oa_version":"Published Version","type":"conference","date_updated":"2023-04-03T07:33:40Z","status":"public","month":"03","conference":{"name":"ICLR: International Conference on Learning Representations","end_date":"2020-05-01","start_date":"2020-04-26","location":"Virtual ; Addis Ababa, Ethiopia"},"date_created":"2022-01-25T15:50:00Z","publisher":"ICLR","file_date_updated":"2022-01-26T07:35:17Z","_id":"10672","oa":1,"ddc":["000"],"publication":"8th International Conference on Learning Representations","title":"Learning representations for binary-classification without backpropagation","abstract":[{"lang":"eng","text":"The family of feedback alignment (FA) algorithms aims to provide a more biologically motivated alternative to backpropagation (BP), by substituting the computations that are unrealistic to be implemented in physical brains. While FA algorithms have been shown to work well in practice, there is a lack of rigorous theory proofing their learning capabilities. Here we introduce the first feedback alignment algorithm with provable learning guarantees. In contrast to existing work, we do not require any assumption about the size or depth of the network except that it has a single output neuron, i.e., such as for binary classification tasks. We show that our FA algorithm can deliver its theoretical promises in practice, surpassing the learning performance of existing FA methods and matching backpropagation in binary classification tasks. Finally, we demonstrate the limits of our FA variant when the number of output neurons grows beyond a certain quantity."}],"publication_status":"published","project":[{"name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"author":[{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","full_name":"Lechner, Mathias","first_name":"Mathias","last_name":"Lechner"}],"quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=Bke61krFvS"}],"citation":{"mla":"Lechner, Mathias. “Learning Representations for Binary-Classification without Backpropagation.” <i>8th International Conference on Learning Representations</i>, ICLR, 2020.","apa":"Lechner, M. (2020). Learning representations for binary-classification without backpropagation. In <i>8th International Conference on Learning Representations</i>. Virtual ; Addis Ababa, Ethiopia: ICLR.","ama":"Lechner M. Learning representations for binary-classification without backpropagation. In: <i>8th International Conference on Learning Representations</i>. ICLR; 2020.","ieee":"M. Lechner, “Learning representations for binary-classification without backpropagation,” in <i>8th International Conference on Learning Representations</i>, Virtual ; Addis Ababa, Ethiopia, 2020.","chicago":"Lechner, Mathias. “Learning Representations for Binary-Classification without Backpropagation.” In <i>8th International Conference on Learning Representations</i>. ICLR, 2020.","short":"M. Lechner, in:, 8th International Conference on Learning Representations, ICLR, 2020.","ista":"Lechner M. 2020. Learning representations for binary-classification without backpropagation. 8th International Conference on Learning Representations. ICLR: International Conference on Learning Representations."},"year":"2020","tmp":{"short":"CC BY-NC-ND (3.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode","image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)"}},{"file":[{"checksum":"c9a4a29161777fc1a89ef451c040e3b1","file_name":"2020_PMLR_Hasani.pdf","access_level":"open_access","success":1,"file_id":"10691","date_updated":"2022-01-26T11:08:51Z","file_size":2329798,"creator":"cchlebak","content_type":"application/pdf","relation":"main_file","date_created":"2022-01-26T11:08:51Z"}],"date_published":"2020-01-01T00:00:00Z","page":"4082-4093","alternative_title":["PMLR"],"scopus_import":"1","publication_identifier":{"issn":["2640-3498"]},"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","article_processing_charge":"No","oa_version":"Published Version","series_title":"PMLR","date_updated":"2022-01-26T11:14:27Z","type":"conference","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"has_accepted_license":"1","language":[{"iso":"eng"}],"acknowledgement":"RH and RG are partially supported by Horizon-2020 ECSEL Project grant No. 783163 (iDev40), Productive 4.0, and ATBMBFW CPS-IoT Ecosystem. ML was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23\r\n(Wittgenstein Award). AA is supported by the National Science Foundation (NSF) Graduate Research Fellowship\r\nProgram. RH and DR are partially supported by The Boeing Company and JP Morgan Chase. This research work is\r\npartially drawn from the PhD dissertation of RH.\r\n","file_date_updated":"2022-01-26T11:08:51Z","conference":{"name":"ML: Machine Learning","end_date":"2020-07-18","start_date":"2020-07-12","location":"Virtual"},"date_created":"2022-01-25T15:50:34Z","status":"public","citation":{"short":"R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 4082–4093.","chicago":"Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu. “A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits.” In <i>Proceedings of the 37th International Conference on Machine Learning</i>, 4082–93. PMLR, 2020.","ista":"Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2020. A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits. Proceedings of the 37th International Conference on Machine Learning. ML: Machine LearningPMLR, PMLR, , 4082–4093.","ama":"Hasani R, Lechner M, Amini A, Rus D, Grosu R. A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits. In: <i>Proceedings of the 37th International Conference on Machine Learning</i>. PMLR. ; 2020:4082-4093.","ieee":"R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits,” in <i>Proceedings of the 37th International Conference on Machine Learning</i>, Virtual, 2020, pp. 4082–4093.","apa":"Hasani, R., Lechner, M., Amini, A., Rus, D., &#38; Grosu, R. (2020). A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits. In <i>Proceedings of the 37th International Conference on Machine Learning</i> (pp. 4082–4093). Virtual.","mla":"Hasani, Ramin, et al. “A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits.” <i>Proceedings of the 37th International Conference on Machine Learning</i>, 2020, pp. 4082–93."},"main_file_link":[{"open_access":"1","url":"http://proceedings.mlr.press/v119/hasani20a.html"}],"author":[{"full_name":"Hasani, Ramin","first_name":"Ramin","last_name":"Hasani"},{"full_name":"Lechner, Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87","first_name":"Mathias","last_name":"Lechner"},{"full_name":"Amini, Alexander","last_name":"Amini","first_name":"Alexander"},{"full_name":"Rus, Daniela","first_name":"Daniela","last_name":"Rus"},{"full_name":"Grosu, Radu","last_name":"Grosu","first_name":"Radu"}],"quality_controlled":"1","tmp":{"short":"CC BY-NC-ND (3.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode","image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)"},"year":"2020","title":"A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits","publication":"Proceedings of the 37th International Conference on Machine Learning","_id":"10673","oa":1,"ddc":["000"],"project":[{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"Z211","name":"The Wittgenstein Prize"}],"abstract":[{"lang":"eng","text":"We propose a neural information processing system obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model of biological neural circuits reparameterized for the control of alternative tasks. We first demonstrate that ONCs realize networks with higher maximum flow compared to arbitrary wired networks. We then learn instances of ONCs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconfiguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Ordinary neural circuits perform on par and, in some cases, significantly surpass the performance of contemporary deep learning models. ONC networks are compact, 77% sparser than their counterpart neural controllers, and their neural dynamics are fully interpretable at the cell-level."}],"publication_status":"published"},{"has_accepted_license":"1","department":[{"_id":"DaAl"}],"language":[{"iso":"eng"}],"day":"12","oa_version":"Published Version","date_updated":"2023-02-23T13:57:24Z","type":"conference","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","scopus_import":"1","publication_identifier":{"issn":["2640-3498"]},"page":"5533-5543","file":[{"file_size":741899,"content_type":"application/pdf","creator":"kschuh","relation":"main_file","date_created":"2021-05-25T09:51:36Z","file_name":"2020_PMLR_Kurtz.pdf","checksum":"2aaaa7d7226e49161311d91627cf783b","access_level":"open_access","success":1,"file_id":"9421","date_updated":"2021-05-25T09:51:36Z"}],"date_published":"2020-07-12T00:00:00Z","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"}],"_id":"9415","ddc":["000"],"oa":1,"publication":"37th International Conference on Machine Learning, ICML 2020","title":"Inducing and exploiting activation sparsity for fast neural network inference","year":"2020","quality_controlled":"1","author":[{"first_name":"Mark","last_name":"Kurtz","full_name":"Kurtz, Mark"},{"full_name":"Kopinsky, Justin","first_name":"Justin","last_name":"Kopinsky"},{"last_name":"Gelashvili","first_name":"Rati","full_name":"Gelashvili, Rati"},{"last_name":"Matveev","first_name":"Alexander","full_name":"Matveev, Alexander"},{"last_name":"Carr","first_name":"John","full_name":"Carr, John"},{"last_name":"Goin","first_name":"Michael","full_name":"Goin, Michael"},{"first_name":"William","last_name":"Leiserson","full_name":"Leiserson, William"},{"full_name":"Moore, Sage","first_name":"Sage","last_name":"Moore"},{"last_name":"Nell","first_name":"Bill","full_name":"Nell, Bill"},{"full_name":"Shavit, Nir","last_name":"Shavit","first_name":"Nir"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh"}],"citation":{"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.","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.","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.","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."},"status":"public","conference":{"end_date":"2020-07-18","name":"ICML: International Conference on Machine Learning","location":"Online","start_date":"2020-07-12"},"month":"07","date_created":"2021-05-23T22:01:45Z","intvolume":"       119","file_date_updated":"2021-05-25T09:51:36Z","volume":119},{"scopus_import":"1","publication_identifier":{"eissn":["1097-4164"],"issn":["1097-2765"]},"page":"310-323.e7","external_id":{"pmid":["31732458"]},"date_published":"2020-01-16T00:00:00Z","department":[{"_id":"DaZi"}],"doi":"10.1016/j.molcel.2019.10.011","language":[{"iso":"eng"}],"day":"16","oa_version":"Published Version","date_updated":"2021-12-14T07:51:15Z","type":"journal_article","article_processing_charge":"No","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","status":"public","month":"01","date_created":"2021-06-08T06:37:09Z","intvolume":"        77","publisher":"Elsevier","article_type":"original","issue":"2","volume":77,"abstract":[{"lang":"eng","text":"DNA methylation and histone H1 mediate transcriptional silencing of genes and transposable elements, but how they interact is unclear. In plants and animals with mosaic genomic methylation, functionally mysterious methylation is also common within constitutively active housekeeping genes. Here, we show that H1 is enriched in methylated sequences, including genes, of Arabidopsis thaliana, yet this enrichment is independent of DNA methylation. Loss of H1 disperses heterochromatin, globally alters nucleosome organization, and activates H1-bound genes, but only weakly de-represses transposable elements. However, H1 loss strongly activates transposable elements hypomethylated through mutation of DNA methyltransferase MET1. Hypomethylation of genes also activates antisense transcription, which is modestly enhanced by H1 loss. Our results demonstrate that H1 and DNA methylation jointly maintain transcriptional homeostasis by silencing transposable elements and aberrant intragenic transcripts. Such functionality plausibly explains why DNA methylation, a well-known mutagen, has been maintained within coding sequences of crucial plant and animal genes."}],"publication_status":"published","_id":"9526","oa":1,"pmid":1,"title":"DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts","publication":"Molecular Cell","year":"2020","extern":"1","quality_controlled":"1","author":[{"full_name":"Choi, Jaemyung","first_name":"Jaemyung","last_name":"Choi"},{"full_name":"Lyons, David B.","last_name":"Lyons","first_name":"David B."},{"full_name":"Kim, M. Yvonne","first_name":"M. Yvonne","last_name":"Kim"},{"full_name":"Moore, Jonathan D.","last_name":"Moore","first_name":"Jonathan D."},{"id":"6973db13-dd5f-11ea-814e-b3e5455e9ed1","full_name":"Zilberman, Daniel","orcid":"0000-0002-0123-8649","last_name":"Zilberman","first_name":"Daniel"}],"citation":{"ieee":"J. Choi, D. B. Lyons, M. Y. Kim, J. D. Moore, and D. Zilberman, “DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts,” <i>Molecular Cell</i>, vol. 77, no. 2. Elsevier, p. 310–323.e7, 2020.","ama":"Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. <i>Molecular Cell</i>. 2020;77(2):310-323.e7. doi:<a href=\"https://doi.org/10.1016/j.molcel.2019.10.011\">10.1016/j.molcel.2019.10.011</a>","chicago":"Choi, Jaemyung, David B. Lyons, M. Yvonne Kim, Jonathan D. Moore, and Daniel Zilberman. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements and Aberrant Intragenic Transcripts.” <i>Molecular Cell</i>. Elsevier, 2020. <a href=\"https://doi.org/10.1016/j.molcel.2019.10.011\">https://doi.org/10.1016/j.molcel.2019.10.011</a>.","short":"J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77 (2020) 310–323.e7.","ista":"Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. 2020. DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. Molecular Cell. 77(2), 310–323.e7.","mla":"Choi, Jaemyung, et al. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements and Aberrant Intragenic Transcripts.” <i>Molecular Cell</i>, vol. 77, no. 2, Elsevier, 2020, p. 310–323.e7, doi:<a href=\"https://doi.org/10.1016/j.molcel.2019.10.011\">10.1016/j.molcel.2019.10.011</a>.","apa":"Choi, J., Lyons, D. B., Kim, M. Y., Moore, J. D., &#38; Zilberman, D. (2020). DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. <i>Molecular Cell</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.molcel.2019.10.011\">https://doi.org/10.1016/j.molcel.2019.10.011</a>"},"main_file_link":[{"url":"https://doi.org/10.1016/j.molcel.2019.10.011","open_access":"1"}]},{"date_published":"2020-12-14T00:00:00Z","file":[{"access_level":"open_access","checksum":"f02d0b2b3838e7891a6c417fc34ffdcd","file_name":"2020_JournalOfComputationalGeometry_Edelsbrunner.pdf","date_updated":"2021-08-11T11:55:11Z","file_id":"9882","success":1,"file_size":1449234,"content_type":"application/pdf","creator":"asandaue","date_created":"2021-08-11T11:55:11Z","relation":"main_file"}],"page":"162-182","publication_identifier":{"eissn":["1920180X"]},"scopus_import":"1","type":"journal_article","date_updated":"2021-08-11T12:26:34Z","oa_version":"Published Version","day":"14","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"Yes","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).","doi":"10.20382/jocg.v11i2a7","license":"https://creativecommons.org/licenses/by/3.0/","language":[{"iso":"eng"}],"has_accepted_license":"1","department":[{"_id":"HeEd"}],"file_date_updated":"2021-08-11T11:55:11Z","publisher":"Carleton University","article_type":"original","volume":11,"issue":"2","date_created":"2021-07-04T22:01:26Z","month":"12","status":"public","intvolume":"        11","tmp":{"short":"CC BY (3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","image":"/images/cc_by.png"},"year":"2020","citation":{"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>.","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.","ista":"Edelsbrunner H, Virk Z, Wagner H. 2020. Topological data analysis in information space. Journal of Computational Geometry. 11(2), 162–182.","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.","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>"},"author":[{"full_name":"Edelsbrunner, Herbert","orcid":"0000-0002-9823-6833","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","first_name":"Herbert","last_name":"Edelsbrunner"},{"first_name":"Ziga","last_name":"Virk","full_name":"Virk, Ziga","id":"2E36B656-F248-11E8-B48F-1D18A9856A87"},{"id":"379CA8B8-F248-11E8-B48F-1D18A9856A87","full_name":"Wagner, Hubert","last_name":"Wagner","first_name":"Hubert"}],"quality_controlled":"1","project":[{"grant_number":"I4887","name":"Discretization in Geometry and Dynamics","_id":"0aa4bc98-070f-11eb-9043-e6fff9c6a316"}],"publication_status":"published","abstract":[{"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.","lang":"eng"}],"publication":"Journal of Computational Geometry","title":"Topological data analysis in information space","ddc":["510","000"],"oa":1,"_id":"9630"},{"ec_funded":1,"department":[{"_id":"DaAl"}],"language":[{"iso":"eng"}],"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).","article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa_version":"Published Version","date_updated":"2023-02-23T14:03:03Z","type":"conference","day":"06","scopus_import":"1","publication_identifier":{"issn":["10495258"],"isbn":["9781713829546"]},"arxiv":1,"external_id":{"arxiv":["2002.11505"]},"date_published":"2020-12-06T00:00:00Z","page":"22361-22372","title":"Scalable belief propagation via relaxed scheduling","publication":"Advances in Neural Information Processing Systems","_id":"9631","oa":1,"project":[{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning"}],"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"}],"publication_status":"published","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html","open_access":"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.","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.","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.","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.","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."},"quality_controlled":"1","author":[{"first_name":"Vitaly","last_name":"Aksenov","full_name":"Aksenov, Vitaly"},{"first_name":"Dan-Adrian","last_name":"Alistarh","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Korhonen, Janne","id":"C5402D42-15BC-11E9-A202-CA2BE6697425","first_name":"Janne","last_name":"Korhonen"}],"year":"2020","intvolume":"        33","month":"12","conference":{"location":"Vancouver, Canada","start_date":"2020-12-06","end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems"},"date_created":"2021-07-04T22:01:26Z","status":"public","volume":33,"publisher":"Curran Associates"},{"main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html"}],"citation":{"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.","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.","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.","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.","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."},"quality_controlled":"1","author":[{"full_name":"Singh, Sidak Pal","id":"DD138E24-D89D-11E9-9DC0-DEF6E5697425","first_name":"Sidak Pal","last_name":"Singh"},{"full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","last_name":"Alistarh"}],"year":"2020","publication":"Advances in Neural Information Processing Systems","title":"WoodFisher: Efficient second-order approximation for neural network compression","oa":1,"_id":"9632","project":[{"grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"publication_status":"published","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."}],"volume":33,"publisher":"Curran Associates","intvolume":"        33","date_created":"2021-07-04T22:01:26Z","month":"12","conference":{"name":"NeurIPS: Conference on Neural Information Processing Systems","end_date":"2020-12-12","location":"Vancouver, Canada","start_date":"2020-12-06"},"status":"public","article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","type":"conference","date_updated":"2023-02-23T14:03:06Z","oa_version":"Published Version","day":"06","ec_funded":1,"language":[{"iso":"eng"}],"department":[{"_id":"DaAl"},{"_id":"ToHe"}],"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.","date_published":"2020-12-06T00:00:00Z","external_id":{"arxiv":["2004.14340"]},"page":"18098-18109","publication_identifier":{"isbn":["9781713829546"],"issn":["10495258"]},"scopus_import":"1","arxiv":1},{"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.","ec_funded":1,"department":[{"_id":"TiVo"}],"language":[{"iso":"eng"}],"oa_version":"Published Version","type":"conference","date_updated":"2023-10-18T09:20:55Z","day":"06","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","scopus_import":"1","publication_identifier":{"issn":["1049-5258"]},"date_published":"2020-12-06T00:00:00Z","page":"16398-16408","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"},{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603"}],"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"}],"publication_status":"published","title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","publication":"Advances in Neural Information Processing Systems","_id":"9633","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"}]},"oa":1,"year":"2020","citation":{"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.","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.","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."},"main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html"}],"author":[{"id":"C7610134-B532-11EA-BD9F-F5753DDC885E","full_name":"Confavreux, Basile J","first_name":"Basile J","last_name":"Confavreux"},{"full_name":"Zenke, Friedemann","first_name":"Friedemann","last_name":"Zenke"},{"last_name":"Agnes","first_name":"Everton J.","full_name":"Agnes, Everton J."},{"first_name":"Timothy","last_name":"Lillicrap","full_name":"Lillicrap, Timothy"},{"first_name":"Tim P","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P"}],"quality_controlled":"1","month":"12","conference":{"location":"Vancouver, Canada","start_date":"2020-12-06","end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems"},"date_created":"2021-07-04T22:01:27Z","status":"public","intvolume":"        33","volume":33},{"publisher":"Springer Nature","date_published":"2020-07-09T00:00:00Z","month":"07","date_created":"2021-07-23T08:59:15Z","status":"public","oa_version":"Published Version","type":"research_data_reference","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2023-08-22T07:55:36Z","year":"2020","day":"09","main_file_link":[{"open_access":"1","url":"https://doi.org/10.6084/m9.figshare.12629697.v1"}],"citation":{"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>","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>.","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.","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>","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>.","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>."},"article_processing_charge":"No","author":[{"full_name":"Hillary, Robert F.","first_name":"Robert F.","last_name":"Hillary"},{"full_name":"Trejo-Banos, Daniel","last_name":"Trejo-Banos","first_name":"Daniel"},{"full_name":"Kousathanas, Athanasios","last_name":"Kousathanas","first_name":"Athanasios"},{"last_name":"McCartney","first_name":"Daniel L.","full_name":"McCartney, Daniel L."},{"full_name":"Harris, Sarah E.","first_name":"Sarah E.","last_name":"Harris"},{"full_name":"Stevenson, Anna J.","first_name":"Anna J.","last_name":"Stevenson"},{"full_name":"Patxot, Marion","last_name":"Patxot","first_name":"Marion"},{"first_name":"Sven Erik","last_name":"Ojavee","full_name":"Ojavee, Sven Erik"},{"full_name":"Zhang, Qian","first_name":"Qian","last_name":"Zhang"},{"full_name":"Liewald, David C.","last_name":"Liewald","first_name":"David C."},{"first_name":"Craig W.","last_name":"Ritchie","full_name":"Ritchie, Craig W."},{"first_name":"Kathryn L.","last_name":"Evans","full_name":"Evans, Kathryn L."},{"last_name":"Tucker-Drob","first_name":"Elliot M.","full_name":"Tucker-Drob, Elliot M."},{"first_name":"Naomi R.","last_name":"Wray","full_name":"Wray, Naomi R."},{"full_name":"McRae, Allan F. ","last_name":"McRae","first_name":"Allan F. "},{"first_name":"Peter M.","last_name":"Visscher","full_name":"Visscher, Peter M."},{"first_name":"Ian J.","last_name":"Deary","full_name":"Deary, Ian J."},{"orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","last_name":"Robinson"},{"last_name":"Marioni","first_name":"Riccardo E. ","full_name":"Marioni, Riccardo E. "}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","other_data_license":"CC0 + CC BY (4.0)","abstract":[{"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).","lang":"eng"}],"title":"Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","_id":"9706","has_accepted_license":"1","department":[{"_id":"MaRo"}],"doi":"10.6084/m9.figshare.12629697.v1","oa":1,"related_material":{"record":[{"id":"8133","relation":"used_in_publication","status":"public"}]}},{"date_published":"2020-05-29T00:00:00Z","publisher":"Apollo - University of Cambridge","date_created":"2021-07-23T10:00:35Z","month":"05","status":"public","main_file_link":[{"open_access":"1","url":"https://doi.org/10.17863/CAM.50169"}],"citation":{"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>","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>.","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>","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.","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>.","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":[{"full_name":"Hartstein, Mate","first_name":"Mate","last_name":"Hartstein"},{"full_name":"Hsu, Yu-Te","last_name":"Hsu","first_name":"Yu-Te"},{"last_name":"Modic","first_name":"Kimberly A","orcid":"0000-0001-9760-3147","full_name":"Modic, Kimberly A","id":"13C26AC0-EB69-11E9-87C6-5F3BE6697425"},{"full_name":"Porras, Juan","first_name":"Juan","last_name":"Porras"},{"full_name":"Loew, Toshinao","first_name":"Toshinao","last_name":"Loew"},{"last_name":"Le Tacon","first_name":"Matthieu","full_name":"Le Tacon, Matthieu"},{"full_name":"Zuo, Huakun","last_name":"Zuo","first_name":"Huakun"},{"first_name":"Jinhua","last_name":"Wang","full_name":"Wang, Jinhua"},{"first_name":"Zengwei","last_name":"Zhu","full_name":"Zhu, Zengwei"},{"full_name":"Chan, Mun","first_name":"Mun","last_name":"Chan"},{"last_name":"McDonald","first_name":"Ross","full_name":"McDonald, Ross"},{"full_name":"Lonzarich, Gilbert","last_name":"Lonzarich","first_name":"Gilbert"},{"full_name":"Keimer, Bernhard","last_name":"Keimer","first_name":"Bernhard"},{"last_name":"Sebastian","first_name":"Suchitra","full_name":"Sebastian, Suchitra"},{"full_name":"Harrison, Neil","last_name":"Harrison","first_name":"Neil"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","type":"research_data_reference","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2023-08-21T07:06:48Z","oa_version":"Published Version","day":"29","year":"2020","title":"Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'","doi":"10.17863/cam.50169","oa":1,"related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"7942"}]},"_id":"9708","department":[{"_id":"KiMo"}],"has_accepted_license":"1","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"}]},{"abstract":[{"lang":"eng","text":"Additional analyses of the trajectories"}],"related_material":{"record":[{"id":"8040","status":"public","relation":"used_in_publication"}]},"doi":"10.1021/jacs.9b13450.s001","_id":"9713","department":[{"_id":"LeSa"}],"title":"Supporting information","day":"20","year":"2020","type":"research_data_reference","date_updated":"2023-08-22T07:49:38Z","oa_version":"Published Version","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","author":[{"last_name":"Gupta","first_name":"Chitrak","full_name":"Gupta, Chitrak"},{"last_name":"Khaniya","first_name":"Umesh","full_name":"Khaniya, Umesh"},{"full_name":"Chan, Chun Kit","first_name":"Chun Kit","last_name":"Chan"},{"full_name":"Dehez, Francois","first_name":"Francois","last_name":"Dehez"},{"full_name":"Shekhar, Mrinal","last_name":"Shekhar","first_name":"Mrinal"},{"full_name":"Gunner, M.R.","last_name":"Gunner","first_name":"M.R."},{"id":"338D39FE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0977-7989","full_name":"Sazanov, Leonid A","first_name":"Leonid A","last_name":"Sazanov"},{"first_name":"Christophe","last_name":"Chipot","full_name":"Chipot, Christophe"},{"full_name":"Singharoy, Abhishek","first_name":"Abhishek","last_name":"Singharoy"}],"article_processing_charge":"No","citation":{"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.","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>.","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>","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>."},"status":"public","date_created":"2021-07-23T12:02:39Z","month":"05","publisher":"American Chemical Society ","date_published":"2020-05-20T00:00:00Z"},{"status":"public","date_created":"2021-07-29T11:29:50Z","month":"11","page":"41","date_published":"2020-11-20T00:00:00Z","publisher":"Cold Spring Harbor Laboratory","oa":1,"related_material":{"record":[{"id":"10766","relation":"later_version","status":"public"},{"id":"9623","relation":"dissertation_contains","status":"public"}]},"doi":"10.1101/2020.11.20.391284","language":[{"iso":"eng"}],"_id":"9750","department":[{"_id":"CaHe"},{"_id":"EM-Fac"},{"_id":"Bio"}],"title":"Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion","ec_funded":1,"acknowledged_ssus":[{"_id":"Bio"},{"_id":"EM-Fac"},{"_id":"SSU"}],"publication":"bioRxiv","publication_status":"published","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."}],"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.","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"},{"grant_number":"742573","name":"Interaction and feedback between cell mechanics and fate specification in vertebrate gastrulation","_id":"260F1432-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"_id":"2521E28E-B435-11E9-9278-68D0E5697425","name":"Modulation of adhesion function in cell-cell contact formation by cortical tension","grant_number":"187-2013"}],"author":[{"first_name":"Jana","last_name":"Slovakova","id":"30F3F2F0-F248-11E8-B48F-1D18A9856A87","full_name":"Slovakova, Jana"},{"first_name":"Mateusz K","last_name":"Sikora","full_name":"Sikora, Mateusz K","id":"2F74BCDE-F248-11E8-B48F-1D18A9856A87"},{"id":"2F1E1758-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5223-3346","full_name":"Caballero Mancebo, Silvia","first_name":"Silvia","last_name":"Caballero Mancebo"},{"id":"2B819732-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4761-5996","full_name":"Krens, Gabriel","first_name":"Gabriel","last_name":"Krens"},{"orcid":"0000-0001-9735-5315","full_name":"Kaufmann, Walter","id":"3F99E422-F248-11E8-B48F-1D18A9856A87","last_name":"Kaufmann","first_name":"Walter"},{"full_name":"Huljev, Karla","id":"44C6F6A6-F248-11E8-B48F-1D18A9856A87","first_name":"Karla","last_name":"Huljev"},{"first_name":"Carl-Philipp J","last_name":"Heisenberg","id":"39427864-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0912-4566","full_name":"Heisenberg, Carl-Philipp J"}],"article_processing_charge":"No","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","main_file_link":[{"url":"https://doi.org/10.1101/2020.11.20.391284","open_access":"1"}],"citation":{"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>.","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.","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>.","short":"J. Slovakova, M.K. Sikora, S. Caballero Mancebo, G. Krens, W. Kaufmann, K. Huljev, C.-P.J. Heisenberg, BioRxiv (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>."},"day":"20","year":"2020","type":"preprint","date_updated":"2024-03-25T23:30:10Z","oa_version":"Preprint"}]