[{"quality_controlled":"1","volume":9,"department":[{"_id":"LaEr"}],"date_published":"2020-07-01T00:00:00Z","publisher":"World Scientific Publishing","project":[{"grant_number":"338804","_id":"258DCDE6-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Random matrices, universality and disordered quantum systems"},{"name":"International IST Doctoral Program","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385"}],"abstract":[{"text":"We prove a central limit theorem for the difference of linear eigenvalue statistics of a sample covariance matrix W˜ and its minor W. We find that the fluctuation of this difference is much smaller than those of the individual linear statistics, as a consequence of the strong correlation between the eigenvalues of W˜ and W. Our result identifies the fluctuation of the spatial derivative of the approximate Gaussian field in the recent paper by Dumitru and Paquette. Unlike in a similar result for Wigner matrices, for sample covariance matrices, the fluctuation may entirely vanish.","lang":"eng"}],"external_id":{"isi":["000547464400001"],"arxiv":["1806.08751"]},"publication_status":"published","doi":"10.1142/S2010326320500069","_id":"6488","article_type":"original","article_number":"2050006","ec_funded":1,"citation":{"short":"G. Cipolloni, L. Erdös, Random Matrices: Theory and Application 9 (2020).","ama":"Cipolloni G, Erdös L. Fluctuations for differences of linear eigenvalue statistics for sample covariance matrices. <i>Random Matrices: Theory and Application</i>. 2020;9(3). doi:<a href=\"https://doi.org/10.1142/S2010326320500069\">10.1142/S2010326320500069</a>","ieee":"G. Cipolloni and L. Erdös, “Fluctuations for differences of linear eigenvalue statistics for sample covariance matrices,” <i>Random Matrices: Theory and Application</i>, vol. 9, no. 3. World Scientific Publishing, 2020.","ista":"Cipolloni G, Erdös L. 2020. Fluctuations for differences of linear eigenvalue statistics for sample covariance matrices. Random Matrices: Theory and Application. 9(3), 2050006.","chicago":"Cipolloni, Giorgio, and László Erdös. “Fluctuations for Differences of Linear Eigenvalue Statistics for Sample Covariance Matrices.” <i>Random Matrices: Theory and Application</i>. World Scientific Publishing, 2020. <a href=\"https://doi.org/10.1142/S2010326320500069\">https://doi.org/10.1142/S2010326320500069</a>.","mla":"Cipolloni, Giorgio, and László Erdös. “Fluctuations for Differences of Linear Eigenvalue Statistics for Sample Covariance Matrices.” <i>Random Matrices: Theory and Application</i>, vol. 9, no. 3, 2050006, World Scientific Publishing, 2020, doi:<a href=\"https://doi.org/10.1142/S2010326320500069\">10.1142/S2010326320500069</a>.","apa":"Cipolloni, G., &#38; Erdös, L. (2020). Fluctuations for differences of linear eigenvalue statistics for sample covariance matrices. <i>Random Matrices: Theory and Application</i>. World Scientific Publishing. <a href=\"https://doi.org/10.1142/S2010326320500069\">https://doi.org/10.1142/S2010326320500069</a>"},"date_created":"2019-05-26T21:59:14Z","year":"2020","status":"public","type":"journal_article","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","day":"01","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1806.08751"}],"oa_version":"Preprint","publication_identifier":{"issn":["20103263"],"eissn":["20103271"]},"month":"07","arxiv":1,"author":[{"full_name":"Cipolloni, Giorgio","first_name":"Giorgio","last_name":"Cipolloni","id":"42198EFA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4901-7992"},{"full_name":"Erdös, László","first_name":"László","last_name":"Erdös","id":"4DBD5372-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-5366-9603"}],"publication":"Random Matrices: Theory and Application","language":[{"iso":"eng"}],"scopus_import":"1","intvolume":"         9","isi":1,"title":"Fluctuations for differences of linear eigenvalue statistics for sample covariance matrices","issue":"3","article_processing_charge":"No","date_updated":"2023-08-28T08:38:48Z","oa":1},{"day":"01","main_file_link":[{"url":"https://arxiv.org/abs/1312.2337","open_access":"1"}],"oa_version":"Preprint","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication_identifier":{"eissn":["16153383"],"issn":["16153375"]},"month":"04","author":[{"id":"3E8AF77E-F248-11E8-B48F-1D18A9856A87","last_name":"Filakovský","full_name":"Filakovský, Marek","first_name":"Marek"},{"last_name":"Vokřínek","first_name":"Lukas","full_name":"Vokřínek, Lukas"}],"arxiv":1,"status":"public","type":"journal_article","intvolume":"        20","scopus_import":"1","isi":1,"title":"Are two given maps homotopic? An algorithmic viewpoint","date_updated":"2023-08-17T13:50:44Z","oa":1,"article_processing_charge":"No","publication":"Foundations of Computational Mathematics","language":[{"iso":"eng"}],"external_id":{"isi":["000522437400004"],"arxiv":["1312.2337"]},"abstract":[{"lang":"eng","text":"This paper presents two algorithms. The first decides the existence of a pointed homotopy between given simplicial maps 𝑓,𝑔:𝑋→𝑌, and the second computes the group [𝛴𝑋,𝑌]∗ of pointed homotopy classes of maps from a suspension; in both cases, the target Y is assumed simply connected. More generally, these algorithms work relative to 𝐴⊆𝑋."}],"publication_status":"published","department":[{"_id":"UlWa"}],"page":"311-330","quality_controlled":"1","volume":20,"publisher":"Springer Nature","date_published":"2020-04-01T00:00:00Z","project":[{"call_identifier":"FWF","name":"Algorithms for Embeddings and Homotopy Theory","grant_number":"P31312","_id":"26611F5C-B435-11E9-9278-68D0E5697425"}],"date_created":"2019-06-16T21:59:14Z","citation":{"apa":"Filakovský, M., &#38; Vokřínek, L. (2020). Are two given maps homotopic? An algorithmic viewpoint. <i>Foundations of Computational Mathematics</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s10208-019-09419-x\">https://doi.org/10.1007/s10208-019-09419-x</a>","ieee":"M. Filakovský and L. Vokřínek, “Are two given maps homotopic? An algorithmic viewpoint,” <i>Foundations of Computational Mathematics</i>, vol. 20. Springer Nature, pp. 311–330, 2020.","ama":"Filakovský M, Vokřínek L. Are two given maps homotopic? An algorithmic viewpoint. <i>Foundations of Computational Mathematics</i>. 2020;20:311-330. doi:<a href=\"https://doi.org/10.1007/s10208-019-09419-x\">10.1007/s10208-019-09419-x</a>","short":"M. Filakovský, L. Vokřínek, Foundations of Computational Mathematics 20 (2020) 311–330.","chicago":"Filakovský, Marek, and Lukas Vokřínek. “Are Two given Maps Homotopic? An Algorithmic Viewpoint.” <i>Foundations of Computational Mathematics</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1007/s10208-019-09419-x\">https://doi.org/10.1007/s10208-019-09419-x</a>.","mla":"Filakovský, Marek, and Lukas Vokřínek. “Are Two given Maps Homotopic? An Algorithmic Viewpoint.” <i>Foundations of Computational Mathematics</i>, vol. 20, Springer Nature, 2020, pp. 311–30, doi:<a href=\"https://doi.org/10.1007/s10208-019-09419-x\">10.1007/s10208-019-09419-x</a>.","ista":"Filakovský M, Vokřínek L. 2020. Are two given maps homotopic? An algorithmic viewpoint. Foundations of Computational Mathematics. 20, 311–330."},"year":"2020","doi":"10.1007/s10208-019-09419-x","_id":"6563","article_type":"original"},{"month":"05","publication_identifier":{"eissn":["1572-9265"],"issn":["1017-1398"]},"file":[{"file_size":359654,"relation":"main_file","content_type":"application/pdf","access_level":"open_access","creator":"kschuh","date_updated":"2020-07-14T12:47:34Z","date_created":"2019-10-01T13:14:10Z","checksum":"bb1a1eb3ebb2df380863d0db594673ba","file_id":"6927","file_name":"ExtragradientMethodPaper.pdf"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa_version":"Submitted Version","day":"01","author":[{"first_name":"Yekini","full_name":"Shehu, Yekini","orcid":"0000-0001-9224-7139","id":"3FC7CB58-F248-11E8-B48F-1D18A9856A87","last_name":"Shehu"},{"first_name":"Xiao-Huan","full_name":"Li, Xiao-Huan","last_name":"Li"},{"full_name":"Dong, Qiao-Li","first_name":"Qiao-Li","last_name":"Dong"}],"type":"journal_article","status":"public","isi":1,"has_accepted_license":"1","intvolume":"        84","scopus_import":"1","article_processing_charge":"No","oa":1,"date_updated":"2023-08-17T13:51:18Z","title":"An efficient projection-type method for monotone variational inequalities in Hilbert spaces","publication":"Numerical Algorithms","language":[{"iso":"eng"}],"ddc":["000"],"abstract":[{"lang":"eng","text":"We consider the monotone variational inequality problem in a Hilbert space and describe a projection-type method with inertial terms under the following properties: (a) The method generates a strongly convergent iteration sequence; (b) The method requires, at each iteration, only one projection onto the feasible set and two evaluations of the operator; (c) The method is designed for variational inequality for which the underline operator is monotone and uniformly continuous; (d) The method includes an inertial term. The latter is also shown to speed up the convergence in our numerical results. A comparison with some related methods is given and indicates that the new method is promising."}],"external_id":{"isi":["000528979000015"]},"publication_status":"published","date_published":"2020-05-01T00:00:00Z","publisher":"Springer Nature","volume":84,"quality_controlled":"1","department":[{"_id":"VlKo"}],"page":"365-388","project":[{"name":"Discrete Optimization in Computer Vision: Theory and Practice","call_identifier":"FP7","_id":"25FBA906-B435-11E9-9278-68D0E5697425","grant_number":"616160"}],"ec_funded":1,"year":"2020","acknowledgement":"The research of this author is supported by the ERC grant at the IST.","citation":{"ama":"Shehu Y, Li X-H, Dong Q-L. An efficient projection-type method for monotone variational inequalities in Hilbert spaces. <i>Numerical Algorithms</i>. 2020;84:365-388. doi:<a href=\"https://doi.org/10.1007/s11075-019-00758-y\">10.1007/s11075-019-00758-y</a>","ieee":"Y. Shehu, X.-H. Li, and Q.-L. Dong, “An efficient projection-type method for monotone variational inequalities in Hilbert spaces,” <i>Numerical Algorithms</i>, vol. 84. Springer Nature, pp. 365–388, 2020.","short":"Y. Shehu, X.-H. Li, Q.-L. Dong, Numerical Algorithms 84 (2020) 365–388.","chicago":"Shehu, Yekini, Xiao-Huan Li, and Qiao-Li Dong. “An Efficient Projection-Type Method for Monotone Variational Inequalities in Hilbert Spaces.” <i>Numerical Algorithms</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1007/s11075-019-00758-y\">https://doi.org/10.1007/s11075-019-00758-y</a>.","ista":"Shehu Y, Li X-H, Dong Q-L. 2020. An efficient projection-type method for monotone variational inequalities in Hilbert spaces. Numerical Algorithms. 84, 365–388.","mla":"Shehu, Yekini, et al. “An Efficient Projection-Type Method for Monotone Variational Inequalities in Hilbert Spaces.” <i>Numerical Algorithms</i>, vol. 84, Springer Nature, 2020, pp. 365–88, doi:<a href=\"https://doi.org/10.1007/s11075-019-00758-y\">10.1007/s11075-019-00758-y</a>.","apa":"Shehu, Y., Li, X.-H., &#38; Dong, Q.-L. (2020). An efficient projection-type method for monotone variational inequalities in Hilbert spaces. <i>Numerical Algorithms</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s11075-019-00758-y\">https://doi.org/10.1007/s11075-019-00758-y</a>"},"date_created":"2019-06-27T20:09:33Z","doi":"10.1007/s11075-019-00758-y","_id":"6593","article_type":"original","file_date_updated":"2020-07-14T12:47:34Z"},{"ec_funded":1,"year":"2020","citation":{"apa":"Benedikter, N. P., Nam, P. T., Porta, M., Schlein, B., &#38; Seiringer, R. (2020). Optimal upper bound for the correlation energy of a Fermi gas in the mean-field regime. <i>Communications in Mathematical Physics</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s00220-019-03505-5\">https://doi.org/10.1007/s00220-019-03505-5</a>","chicago":"Benedikter, Niels P, Phan Thành Nam, Marcello Porta, Benjamin Schlein, and Robert Seiringer. “Optimal Upper Bound for the Correlation Energy of a Fermi Gas in the Mean-Field Regime.” <i>Communications in Mathematical Physics</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1007/s00220-019-03505-5\">https://doi.org/10.1007/s00220-019-03505-5</a>.","ista":"Benedikter NP, Nam PT, Porta M, Schlein B, Seiringer R. 2020. Optimal upper bound for the correlation energy of a Fermi gas in the mean-field regime. Communications in Mathematical Physics. 374, 2097–2150.","mla":"Benedikter, Niels P., et al. “Optimal Upper Bound for the Correlation Energy of a Fermi Gas in the Mean-Field Regime.” <i>Communications in Mathematical Physics</i>, vol. 374, Springer Nature, 2020, pp. 2097–2150, doi:<a href=\"https://doi.org/10.1007/s00220-019-03505-5\">10.1007/s00220-019-03505-5</a>.","ama":"Benedikter NP, Nam PT, Porta M, Schlein B, Seiringer R. Optimal upper bound for the correlation energy of a Fermi gas in the mean-field regime. <i>Communications in Mathematical Physics</i>. 2020;374:2097–2150. doi:<a href=\"https://doi.org/10.1007/s00220-019-03505-5\">10.1007/s00220-019-03505-5</a>","ieee":"N. P. Benedikter, P. T. Nam, M. Porta, B. Schlein, and R. Seiringer, “Optimal upper bound for the correlation energy of a Fermi gas in the mean-field regime,” <i>Communications in Mathematical Physics</i>, vol. 374. Springer Nature, pp. 2097–2150, 2020.","short":"N.P. Benedikter, P.T. Nam, M. Porta, B. Schlein, R. Seiringer, Communications in Mathematical Physics 374 (2020) 2097–2150."},"date_created":"2019-07-18T13:30:04Z","doi":"10.1007/s00220-019-03505-5","file_date_updated":"2020-07-14T12:47:35Z","article_type":"original","_id":"6649","ddc":["530"],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"external_id":{"arxiv":["1809.01902"],"isi":["000527910700019"]},"abstract":[{"text":"While Hartree–Fock theory is well established as a fundamental approximation for interacting fermions, it has been unclear how to describe corrections to it due to many-body correlations. In this paper we start from the Hartree–Fock state given by plane waves and introduce collective particle–hole pair excitations. These pairs can be approximately described by a bosonic quadratic Hamiltonian. We use Bogoliubov theory to construct a trial state yielding a rigorous Gell-Mann–Brueckner–type upper bound to the ground state energy. Our result justifies the random-phase approximation in the mean-field scaling regime, for repulsive, regular interaction potentials.\r\n","lang":"eng"}],"publication_status":"published","publisher":"Springer Nature","date_published":"2020-03-01T00:00:00Z","page":"2097–2150","department":[{"_id":"RoSe"}],"quality_controlled":"1","volume":374,"project":[{"name":"FWF Open Access Fund","call_identifier":"FWF","_id":"3AC91DDA-15DF-11EA-824D-93A3E7B544D1"},{"call_identifier":"FWF","name":"Structure of the Excitation Spectrum for Many-Body Quantum Systems","grant_number":"P27533_N27","_id":"25C878CE-B435-11E9-9278-68D0E5697425"},{"call_identifier":"H2020","name":"Analysis of quantum many-body systems","grant_number":"694227","_id":"25C6DC12-B435-11E9-9278-68D0E5697425"}],"isi":1,"intvolume":"       374","has_accepted_license":"1","scopus_import":"1","date_updated":"2023-08-17T13:51:50Z","oa":1,"article_processing_charge":"No","title":"Optimal upper bound for the correlation energy of a Fermi gas in the mean-field regime","publication":"Communications in Mathematical Physics","language":[{"iso":"eng"}],"month":"03","publication_identifier":{"eissn":["1432-0916"],"issn":["0010-3616"]},"oa_version":"Published Version","day":"01","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","file":[{"checksum":"f9dd6dd615a698f1d3636c4a092fed23","file_id":"6668","file_name":"2019_CommMathPhysics_Benedikter.pdf","date_created":"2019-07-24T07:19:10Z","date_updated":"2020-07-14T12:47:35Z","creator":"dernst","access_level":"open_access","content_type":"application/pdf","file_size":853289,"relation":"main_file"}],"author":[{"orcid":"0000-0002-1071-6091","id":"3DE6C32A-F248-11E8-B48F-1D18A9856A87","last_name":"Benedikter","full_name":"Benedikter, Niels P","first_name":"Niels P"},{"last_name":"Nam","first_name":"Phan Thành","full_name":"Nam, Phan Thành"},{"last_name":"Porta","full_name":"Porta, Marcello","first_name":"Marcello"},{"last_name":"Schlein","full_name":"Schlein, Benjamin","first_name":"Benjamin"},{"last_name":"Seiringer","id":"4AFD0470-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6781-0521","first_name":"Robert","full_name":"Seiringer, Robert"}],"arxiv":1,"status":"public","type":"journal_article"},{"issue":"6","article_processing_charge":"No","oa":1,"date_updated":"2024-03-06T08:28:50Z","title":"Analysis of a two-layer neural network via displacement convexity","isi":1,"intvolume":"        48","language":[{"iso":"eng"}],"publication":"Annals of Statistics","arxiv":1,"author":[{"last_name":"Javanmard","first_name":"Adel","full_name":"Javanmard, Adel"},{"first_name":"Marco","full_name":"Mondelli, Marco","orcid":"0000-0002-3242-7020","id":"27EB676C-8706-11E9-9510-7717E6697425","last_name":"Mondelli"},{"last_name":"Montanari","first_name":"Andrea","full_name":"Montanari, Andrea"}],"publication_identifier":{"issn":["1932-6157"],"eissn":["1941-7330"]},"month":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"https://arxiv.org/abs/1901.01375","open_access":"1"}],"day":"11","oa_version":"Preprint","status":"public","type":"journal_article","year":"2020","citation":{"ista":"Javanmard A, Mondelli M, Montanari A. 2020. Analysis of a two-layer neural network via displacement convexity. Annals of Statistics. 48(6), 3619–3642.","chicago":"Javanmard, Adel, Marco Mondelli, and Andrea Montanari. “Analysis of a Two-Layer Neural Network via Displacement Convexity.” <i>Annals of Statistics</i>. Institute of Mathematical Statistics, 2020. <a href=\"https://doi.org/10.1214/20-AOS1945\">https://doi.org/10.1214/20-AOS1945</a>.","mla":"Javanmard, Adel, et al. “Analysis of a Two-Layer Neural Network via Displacement Convexity.” <i>Annals of Statistics</i>, vol. 48, no. 6, Institute of Mathematical Statistics, 2020, pp. 3619–42, doi:<a href=\"https://doi.org/10.1214/20-AOS1945\">10.1214/20-AOS1945</a>.","ieee":"A. Javanmard, M. Mondelli, and A. Montanari, “Analysis of a two-layer neural network via displacement convexity,” <i>Annals of Statistics</i>, vol. 48, no. 6. Institute of Mathematical Statistics, pp. 3619–3642, 2020.","ama":"Javanmard A, Mondelli M, Montanari A. Analysis of a two-layer neural network via displacement convexity. <i>Annals of Statistics</i>. 2020;48(6):3619-3642. doi:<a href=\"https://doi.org/10.1214/20-AOS1945\">10.1214/20-AOS1945</a>","short":"A. Javanmard, M. Mondelli, A. Montanari, Annals of Statistics 48 (2020) 3619–3642.","apa":"Javanmard, A., Mondelli, M., &#38; Montanari, A. (2020). Analysis of a two-layer neural network via displacement convexity. <i>Annals of Statistics</i>. Institute of Mathematical Statistics. <a href=\"https://doi.org/10.1214/20-AOS1945\">https://doi.org/10.1214/20-AOS1945</a>"},"date_created":"2019-07-31T09:39:42Z","_id":"6748","article_type":"original","doi":"10.1214/20-AOS1945","publication_status":"published","abstract":[{"text":"Fitting a function by using linear combinations of a large number N of `simple' components is one of the most fruitful ideas in statistical learning. This idea lies at the core of a variety of methods, from two-layer neural networks to kernel regression, to boosting. In general, the resulting risk minimization problem is non-convex and is solved by gradient descent or its variants. Unfortunately, little is known about global convergence properties of these approaches.\r\nHere we consider the problem of learning a concave function f on a compact convex domain Ω⊆ℝd, using linear combinations of `bump-like' components (neurons). The parameters to be fitted are the centers of N bumps, and the resulting empirical risk minimization problem is highly non-convex. We prove that, in the limit in which the number of neurons diverges, the evolution of gradient descent converges to a Wasserstein gradient flow in the space of probability distributions over Ω. Further, when the bump width δ tends to 0, this gradient flow has a limit which is a viscous porous medium equation. Remarkably, the cost function optimized by this gradient flow exhibits a special property known as displacement convexity, which implies exponential convergence rates for N→∞, δ→0. Surprisingly, this asymptotic theory appears to capture well the behavior for moderate values of δ,N. Explaining this phenomenon, and understanding the dependence on δ,N in a quantitative manner remains an outstanding challenge.","lang":"eng"}],"external_id":{"arxiv":["1901.01375"],"isi":["000598369200021"]},"date_published":"2020-12-11T00:00:00Z","publisher":"Institute of Mathematical Statistics","quality_controlled":"1","volume":48,"department":[{"_id":"MaMo"}],"page":"3619-3642"},{"doi":"10.1016/j.tcs.2019.06.031","article_type":"original","_id":"6761","file_date_updated":"2020-10-09T06:31:22Z","related_material":{"record":[{"status":"public","relation":"earlier_version","id":"1341"}]},"year":"2020","citation":{"short":"G. Avni, T.A. Henzinger, O. Kupferman, Theoretical Computer Science 807 (2020) 42–55.","ieee":"G. Avni, T. A. Henzinger, and O. Kupferman, “Dynamic resource allocation games,” <i>Theoretical Computer Science</i>, vol. 807. Elsevier, pp. 42–55, 2020.","ama":"Avni G, Henzinger TA, Kupferman O. Dynamic resource allocation games. <i>Theoretical Computer Science</i>. 2020;807:42-55. doi:<a href=\"https://doi.org/10.1016/j.tcs.2019.06.031\">10.1016/j.tcs.2019.06.031</a>","mla":"Avni, Guy, et al. “Dynamic Resource Allocation Games.” <i>Theoretical Computer Science</i>, vol. 807, Elsevier, 2020, pp. 42–55, doi:<a href=\"https://doi.org/10.1016/j.tcs.2019.06.031\">10.1016/j.tcs.2019.06.031</a>.","chicago":"Avni, Guy, Thomas A Henzinger, and Orna Kupferman. “Dynamic Resource Allocation Games.” <i>Theoretical Computer Science</i>. Elsevier, 2020. <a href=\"https://doi.org/10.1016/j.tcs.2019.06.031\">https://doi.org/10.1016/j.tcs.2019.06.031</a>.","ista":"Avni G, Henzinger TA, Kupferman O. 2020. Dynamic resource allocation games. Theoretical Computer Science. 807, 42–55.","apa":"Avni, G., Henzinger, T. A., &#38; Kupferman, O. (2020). Dynamic resource allocation games. <i>Theoretical Computer Science</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.tcs.2019.06.031\">https://doi.org/10.1016/j.tcs.2019.06.031</a>"},"date_created":"2019-08-04T21:59:20Z","date_published":"2020-02-06T00:00:00Z","publisher":"Elsevier","quality_controlled":"1","volume":807,"page":"42-55","department":[{"_id":"ToHe"}],"project":[{"grant_number":"S11402-N23","_id":"25F2ACDE-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Rigorous Systems Engineering"},{"grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"The Wittgenstein Prize"},{"grant_number":"M02369","_id":"264B3912-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Formal Methods meets Algorithmic Game Theory"}],"ddc":["000"],"abstract":[{"lang":"eng","text":"In resource allocation games, selfish players share resources that are needed in order to fulfill their objectives. The cost of using a resource depends on the load on it. In the traditional setting, the players make their choices concurrently and in one-shot. That is, a strategy for a player is a subset of the resources. We introduce and study dynamic resource allocation games. In this setting, the game proceeds in phases. In each phase each player chooses one resource. A scheduler dictates the order in which the players proceed in a phase, possibly scheduling several players to proceed concurrently. The game ends when each player has collected a set of resources that fulfills his objective. The cost for each player then depends on this set as well as on the load on the resources in it – we consider both congestion and cost-sharing games. We argue that the dynamic setting is the suitable setting for many applications in practice. We study the stability of dynamic resource allocation games, where the appropriate notion of stability is that of subgame perfect equilibrium, study the inefficiency incurred due to selfish behavior, and also study problems that are particular to the dynamic setting, like constraints on the order in which resources can be chosen or the problem of finding a scheduler that achieves stability."}],"external_id":{"isi":["000512219400004"]},"publication_status":"published","publication":"Theoretical Computer Science","language":[{"iso":"eng"}],"isi":1,"has_accepted_license":"1","scopus_import":"1","intvolume":"       807","article_processing_charge":"No","oa":1,"date_updated":"2023-08-17T13:52:49Z","title":"Dynamic resource allocation games","status":"public","type":"journal_article","month":"02","publication_identifier":{"issn":["03043975"]},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","file":[{"date_created":"2020-10-09T06:31:22Z","date_updated":"2020-10-09T06:31:22Z","success":1,"file_name":"2020_TheoreticalCS_Avni.pdf","file_id":"8639","checksum":"e86635417f45eb2cd75778f91382f737","content_type":"application/pdf","access_level":"open_access","file_size":1413001,"relation":"main_file","creator":"dernst"}],"oa_version":"Submitted Version","day":"06","author":[{"first_name":"Guy","full_name":"Avni, Guy","orcid":"0000-0001-5588-8287","id":"463C8BC2-F248-11E8-B48F-1D18A9856A87","last_name":"Avni"},{"last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000−0002−2985−7724","full_name":"Henzinger, Thomas A","first_name":"Thomas A"},{"last_name":"Kupferman","first_name":"Orna","full_name":"Kupferman, Orna"}]},{"date_created":"2019-08-11T21:59:24Z","citation":{"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>","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.","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>","short":"F. Stella, E. Urdapilleta, Y. Luo, A. Treves, Hippocampus 30 (2020) 302–313.","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>.","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>."},"year":"2020","article_type":"original","_id":"6796","file_date_updated":"2020-07-14T12:47:40Z","doi":"10.1002/hipo.23144","publication_status":"published","abstract":[{"lang":"eng","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."}],"external_id":{"isi":["000477299600001"],"pmid":["31339190"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["570"],"volume":30,"quality_controlled":"1","page":"302-313","department":[{"_id":"JoCs"}],"date_published":"2020-04-01T00:00:00Z","publisher":"Wiley","title":"Partial coherence and frustration in self-organizing spherical grids","article_processing_charge":"No","issue":"4","date_updated":"2023-08-17T13:53:14Z","oa":1,"intvolume":"        30","has_accepted_license":"1","scopus_import":"1","isi":1,"language":[{"iso":"eng"}],"publication":"Hippocampus","author":[{"first_name":"Federico","full_name":"Stella, Federico","orcid":"0000-0001-9439-3148","id":"39AF1E74-F248-11E8-B48F-1D18A9856A87","last_name":"Stella"},{"last_name":"Urdapilleta","first_name":"Eugenio","full_name":"Urdapilleta, Eugenio"},{"last_name":"Luo","first_name":"Yifan","full_name":"Luo, Yifan"},{"first_name":"Alessandro","full_name":"Treves, Alessandro","last_name":"Treves"}],"file":[{"relation":"main_file","file_size":2370658,"content_type":"application/pdf","access_level":"open_access","creator":"dernst","date_created":"2019-08-12T07:53:33Z","date_updated":"2020-07-14T12:47:40Z","checksum":"7b54d22bfbfc0d1188a9ea24d985bfb2","file_name":"2019_Hippocampus_Stella.pdf","file_id":"6800"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","day":"01","oa_version":"Published Version","month":"04","publication_identifier":{"eissn":["10981063"],"issn":["10509631"]},"status":"public","type":"journal_article","pmid":1},{"pmid":1,"type":"journal_article","status":"public","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100895/","open_access":"1"}],"oa_version":"Submitted Version","day":"01","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication_identifier":{"issn":["1046-2023"]},"month":"03","author":[{"id":"425C1CE8-F248-11E8-B48F-1D18A9856A87","last_name":"Jahr","full_name":"Jahr, Wiebke","first_name":"Wiebke"},{"id":"39BDC62C-F248-11E8-B48F-1D18A9856A87","last_name":"Velicky","orcid":"0000-0002-2340-7431","first_name":"Philipp","full_name":"Velicky, Philipp"},{"first_name":"Johann G","full_name":"Danzl, Johann G","orcid":"0000-0001-8559-3973","id":"42EFD3B6-F248-11E8-B48F-1D18A9856A87","last_name":"Danzl"}],"publication":"Methods","language":[{"iso":"eng"}],"intvolume":"       174","scopus_import":"1","isi":1,"title":"Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens","oa":1,"date_updated":"2023-08-17T13:59:57Z","article_processing_charge":"No","issue":"3","page":"27-41","department":[{"_id":"JoDa"}],"volume":174,"quality_controlled":"1","publisher":"Elsevier","date_published":"2020-03-01T00:00:00Z","project":[{"_id":"265CB4D0-B435-11E9-9278-68D0E5697425","grant_number":"I03600","name":"Optical control of synaptic function via adhesion molecules","call_identifier":"FWF"},{"grant_number":"LT00057","_id":"2668BFA0-B435-11E9-9278-68D0E5697425","name":"High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration"}],"external_id":{"pmid":["31344404"],"isi":["000525860400005"]},"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."}],"publication_status":"published","doi":"10.1016/j.ymeth.2019.07.019","article_type":"original","_id":"6808","citation":{"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>","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.","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>.","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>.","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>","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.","short":"W. Jahr, P. Velicky, J.G. Danzl, Methods 174 (2020) 27–41."},"date_created":"2019-08-12T16:36:32Z","year":"2020"},{"project":[{"grant_number":"694227","_id":"25C6DC12-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Analysis of quantum many-body systems"}],"date_published":"2020-06-01T00:00:00Z","publisher":"Springer","volume":376,"quality_controlled":"1","department":[{"_id":"RoSe"}],"page":"1311-1395","publication_status":"published","abstract":[{"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.","lang":"eng"}],"external_id":{"arxiv":["1812.03086"],"isi":["000536053300012"]},"_id":"6906","article_type":"original","doi":"10.1007/s00220-019-03555-9","year":"2020","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”.","citation":{"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>","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.","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.","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>.","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>.","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>"},"date_created":"2019-09-24T17:30:59Z","ec_funded":1,"type":"journal_article","status":"public","arxiv":1,"author":[{"last_name":"Boccato","id":"342E7E22-F248-11E8-B48F-1D18A9856A87","first_name":"Chiara","full_name":"Boccato, Chiara"},{"full_name":"Brennecke, Christian","first_name":"Christian","last_name":"Brennecke"},{"full_name":"Cenatiempo, Serena","first_name":"Serena","last_name":"Cenatiempo"},{"first_name":"Benjamin","full_name":"Schlein, Benjamin","last_name":"Schlein"}],"month":"06","publication_identifier":{"issn":["0010-3616"],"eissn":["1432-0916"]},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1812.03086"}],"day":"01","language":[{"iso":"eng"}],"publication":"Communications in Mathematical Physics","article_processing_charge":"No","oa":1,"date_updated":"2024-02-22T13:33:02Z","title":"Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime","isi":1,"intvolume":"       376","scopus_import":"1"},{"publication":"arXiv","_id":"10012","language":[{"iso":"eng"}],"article_number":"2003.05478","related_material":{"record":[{"status":"public","id":"10007","relation":"dissertation_contains"}]},"ec_funded":1,"year":"2020","article_processing_charge":"No","date_updated":"2023-09-07T13:30:45Z","acknowledgement":"Parts of the paper were written during the visit of the authors to the Hausdorff Research Institute for Mathematics (HIM), University of Bonn, in the framework of the trimester program “Evolution of Interfaces”. The support and the hospitality of HIM are gratefully acknowledged. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 665385.","oa":1,"citation":{"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.","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.","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>.","short":"J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.).","apa":"Fischer, J. L., Hensel, S., Laux, T., &#38; Simon, T. (n.d.). The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. <i>arXiv</i>."},"date_created":"2021-09-13T12:17:11Z","title":"The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions","date_published":"2020-03-11T00:00:00Z","type":"preprint","status":"public","department":[{"_id":"JuFi"}],"project":[{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385","name":"International IST Doctoral Program","call_identifier":"H2020"}],"month":"03","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","abstract":[{"text":"We prove that in the absence of topological changes, the notion of BV solutions to planar multiphase mean curvature flow does not allow for a mechanism for (unphysical) non-uniqueness. Our approach is based on the local structure of the energy landscape near a classical evolution by mean curvature. Mean curvature flow being the gradient flow of the surface energy functional, we develop a gradient-flow analogue of the notion of calibrations. Just like the existence of a calibration guarantees that one has reached a global minimum in the energy landscape, the existence of a \"gradient flow calibration\" ensures that the route of steepest descent in the energy landscape is unique and stable.","lang":"eng"}],"external_id":{"arxiv":["2003.05478"]},"main_file_link":[{"url":"https://arxiv.org/abs/2003.05478","open_access":"1"}],"day":"11","oa_version":"Preprint","arxiv":1,"publication_status":"submitted","author":[{"last_name":"Fischer","id":"2C12A0B0-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0479-558X","first_name":"Julian L","full_name":"Fischer, Julian L"},{"first_name":"Sebastian","full_name":"Hensel, Sebastian","last_name":"Hensel","id":"4D23B7DA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-7252-8072"},{"full_name":"Laux, Tim","first_name":"Tim","last_name":"Laux"},{"last_name":"Simon","full_name":"Simon, Thilo","first_name":"Thilo"}]},{"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2008.10962"}],"oa_version":"Preprint","external_id":{"arxiv":["2008.10962"]},"day":"25","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","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"}],"month":"08","author":[{"first_name":"Dominik L","full_name":"Forkert, Dominik L","id":"35C79D68-F248-11E8-B48F-1D18A9856A87","last_name":"Forkert"},{"full_name":"Maas, Jan","first_name":"Jan","orcid":"0000-0002-0845-1338","last_name":"Maas","id":"4C5696CE-F248-11E8-B48F-1D18A9856A87"},{"id":"30AD2CBC-F248-11E8-B48F-1D18A9856A87","last_name":"Portinale","first_name":"Lorenzo","full_name":"Portinale, Lorenzo"}],"publication_status":"submitted","arxiv":1,"department":[{"_id":"JaMa"}],"page":"33","type":"preprint","date_published":"2020-08-25T00:00:00Z","status":"public","project":[{"call_identifier":"H2020","name":"Optimal Transport and Stochastic Dynamics","grant_number":"716117","_id":"256E75B8-B435-11E9-9278-68D0E5697425"},{"grant_number":"F6504","_id":"fc31cba2-9c52-11eb-aca3-ff467d239cd2","name":"Taming Complexity in Partial Differential Systems"}],"ec_funded":1,"related_material":{"record":[{"id":"11739","relation":"later_version","status":"public"},{"status":"public","id":"10030","relation":"dissertation_contains"}]},"article_number":"2008.10962","date_created":"2021-09-17T10:57:27Z","citation":{"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>.","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.","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.","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.","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>.","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>. .","short":"D.L. Forkert, J. Maas, L. Portinale, ArXiv (n.d.)."},"title":"Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions","date_updated":"2023-09-07T13:31:05Z","acknowledgement":"This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 716117) and by the Austrian Science Fund (FWF), grants No F65 and W1245.","oa":1,"year":"2020","article_processing_charge":"No","publication":"arXiv","language":[{"iso":"eng"}],"_id":"10022"},{"article_number":"QTu8A.1","scopus_import":"1","article_processing_charge":"No","year":"2020","date_updated":"2023-10-18T08:32:34Z","date_created":"2021-11-21T23:01:31Z","title":"New designs and noise channels in electro-optic microwave to optical up-conversion","citation":{"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>","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>.","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>.","short":"N.J. Lambert, S. Mobassem, A.R. Rueda Sanchez, H.G.L. Schwefel, in:, OSA Quantum 2.0 Conference, Optica Publishing Group, 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>","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."},"publication":"OSA Quantum 2.0 Conference","doi":"10.1364/QUANTUM.2020.QTu8A.1","_id":"10328","language":[{"iso":"eng"}],"publication_identifier":{"isbn":["9-781-5575-2820-9"]},"month":"01","conference":{"start_date":"2020-09-14","name":"OSA: Optical Society of America","end_date":"2020-09-17","location":"Washington, DC, United States"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","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."}],"oa_version":"None","day":"01","publication_status":"published","author":[{"first_name":"Nicholas J.","full_name":"Lambert, Nicholas J.","last_name":"Lambert"},{"last_name":"Mobassem","first_name":"Sonia","full_name":"Mobassem, Sonia"},{"orcid":"0000-0001-6249-5860","last_name":"Rueda Sanchez","id":"3B82B0F8-F248-11E8-B48F-1D18A9856A87","full_name":"Rueda Sanchez, Alfredo R","first_name":"Alfredo R"},{"first_name":"Harald G.L.","full_name":"Schwefel, Harald G.L.","last_name":"Schwefel"}],"type":"conference","date_published":"2020-01-01T00:00:00Z","status":"public","alternative_title":["OSA Technical Digest"],"publisher":"Optica Publishing Group","quality_controlled":"1","department":[{"_id":"JoFi"}]},{"date_updated":"2024-02-22T13:10:45Z","oa":1,"article_processing_charge":"No","title":"Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures","isi":1,"scopus_import":"1","language":[{"iso":"eng"}],"publication":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","author":[{"full_name":"Kokoris Kogias, Eleftherios","first_name":"Eleftherios","last_name":"Kokoris Kogias","id":"f5983044-d7ef-11ea-ac6d-fd1430a26d30"},{"full_name":"Malkhi, Dahlia","first_name":"Dahlia","last_name":"Malkhi"},{"last_name":"Spiegelman","first_name":"Alexander","full_name":"Spiegelman, Alexander"}],"conference":{"start_date":"2020-11-09","name":"CCS: Computer and Communications Security","end_date":"2020-11-13","location":"Virtual, United States"},"publication_identifier":{"isbn":["978-1-4503-7089-9"]},"month":"10","oa_version":"Preprint","day":"30","main_file_link":[{"url":"https://eprint.iacr.org/2019/1015","open_access":"1"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","type":"conference","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.","year":"2020","date_created":"2021-12-16T13:23:27Z","citation":{"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>","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.","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.","ama":"Kokoris Kogias E, Malkhi D, Spiegelman A. Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. In: <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>. Association for Computing Machinery; 2020:1751–1767. doi:<a href=\"https://doi.org/10.1145/3372297.3423364\">10.1145/3372297.3423364</a>","chicago":"Kokoris Kogias, Eleftherios, Dahlia Malkhi, and Alexander Spiegelman. “Asynchronous Distributed Key Generation for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” In <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>, 1751–1767. Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3372297.3423364\">https://doi.org/10.1145/3372297.3423364</a>.","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.","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>."},"_id":"10556","doi":"10.1145/3372297.3423364","publication_status":"published","external_id":{"isi":["000768470400104"]},"abstract":[{"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.","lang":"eng"}],"publisher":"Association for Computing Machinery","date_published":"2020-10-30T00:00:00Z","department":[{"_id":"ElKo"}],"page":"1751–1767","quality_controlled":"1"},{"extern":"1","department":[{"_id":"ElKo"}],"type":"patent","status":"public","date_published":"2020-03-03T00:00:00Z","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."}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","main_file_link":[{"open_access":"1","url":"https://patents.google.com/patent/US10581613B2/en"}],"oa_version":"Published Version","day":"03","applicant":["Ecole Polytechnique Federale de Lausanne"],"month":"03","ipc":" H04L9/3247 ; G06Q20/29 ; G06Q20/382 ; H04L9/3236","author":[{"full_name":"Ford, Bryan","first_name":"Bryan","last_name":"Ford"},{"last_name":"Gasse","first_name":"Linus","full_name":"Gasse, Linus"},{"last_name":"Kokoris Kogias","id":"f5983044-d7ef-11ea-ac6d-fd1430a26d30","first_name":"Eleftherios","full_name":"Kokoris Kogias, Eleftherios"},{"first_name":"Philipp","full_name":"Jovanovic, Philipp","last_name":"Jovanovic"}],"ipn":"10581613","publication_date":"2020-03-03","_id":"10557","related_material":{"link":[{"relation":"earlier_version","url":"https://patents.google.com/patent/US20180359096A1/en"}]},"date_created":"2021-12-16T13:28:59Z","citation":{"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.","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.","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.","short":"B. Ford, L. Gasse, E. Kokoris Kogias, P. Jovanovic, (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.","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."},"title":"Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods","year":"2020","article_processing_charge":"No","application_date":"2017-06-09","date_updated":"2021-12-21T10:04:50Z","oa":1},{"publication":"8th International Conference on Learning Representations","language":[{"iso":"eng"}],"has_accepted_license":"1","scopus_import":"1","title":"Learning representations for binary-classification without backpropagation","article_processing_charge":"No","oa":1,"date_updated":"2023-04-03T07:33:40Z","status":"public","type":"conference","file":[{"content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_size":249431,"creator":"mlechner","date_updated":"2022-01-26T07:35:17Z","date_created":"2022-01-26T07:35:17Z","file_name":"iclr_2020.pdf","file_id":"10677","checksum":"ea13d42dd4541ddb239b6a75821fd6c9","success":1}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=Bke61krFvS"}],"day":"11","oa_version":"Published Version","month":"03","conference":{"name":"ICLR: International Conference on Learning Representations","start_date":"2020-04-26","location":"Virtual ; Addis Ababa, Ethiopia","end_date":"2020-05-01"},"author":[{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","last_name":"Lechner","first_name":"Mathias","full_name":"Lechner, Mathias"}],"_id":"10672","file_date_updated":"2022-01-26T07:35:17Z","date_created":"2022-01-25T15:50:00Z","citation":{"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.","short":"M. Lechner, in:, 8th International Conference on Learning Representations, ICLR, 2020.","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.","ista":"Lechner M. 2020. Learning representations for binary-classification without backpropagation. 8th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","mla":"Lechner, Mathias. “Learning Representations for Binary-Classification without Backpropagation.” <i>8th International Conference on Learning Representations</i>, ICLR, 2020."},"year":"2020","acknowledgement":"This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23\r\n(Wittgenstein Award).\r\n","quality_controlled":"1","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"date_published":"2020-03-11T00:00:00Z","license":"https://creativecommons.org/licenses/by-nc-nd/3.0/","publisher":"ICLR","project":[{"call_identifier":"FWF","name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211"}],"abstract":[{"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.","lang":"eng"}],"tmp":{"short":"CC BY-NC-ND (3.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (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"},"ddc":["000"],"publication_status":"published"},{"article_processing_charge":"No","date_updated":"2022-01-26T11:14:27Z","oa":1,"title":"A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits","scopus_import":"1","has_accepted_license":"1","language":[{"iso":"eng"}],"publication":"Proceedings of the 37th International Conference on Machine Learning","author":[{"last_name":"Hasani","full_name":"Hasani, Ramin","first_name":"Ramin"},{"first_name":"Mathias","full_name":"Lechner, Mathias","last_name":"Lechner","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Alexander","full_name":"Amini, Alexander","last_name":"Amini"},{"first_name":"Daniela","full_name":"Rus, Daniela","last_name":"Rus"},{"full_name":"Grosu, Radu","first_name":"Radu","last_name":"Grosu"}],"publication_identifier":{"issn":["2640-3498"]},"conference":{"start_date":"2020-07-12","name":"ML: Machine Learning","end_date":"2020-07-18","location":"Virtual"},"file":[{"success":1,"file_name":"2020_PMLR_Hasani.pdf","checksum":"c9a4a29161777fc1a89ef451c040e3b1","file_id":"10691","date_created":"2022-01-26T11:08:51Z","date_updated":"2022-01-26T11:08:51Z","creator":"cchlebak","file_size":2329798,"content_type":"application/pdf","access_level":"open_access","relation":"main_file"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","oa_version":"Published Version","main_file_link":[{"url":"http://proceedings.mlr.press/v119/hasani20a.html","open_access":"1"}],"status":"public","type":"conference","alternative_title":["PMLR"],"year":"2020","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","citation":{"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.","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.","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.","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.","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.","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."},"date_created":"2022-01-25T15:50:34Z","_id":"10673","file_date_updated":"2022-01-26T11:08:51Z","publication_status":"published","tmp":{"short":"CC BY-NC-ND (3.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (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"},"ddc":["000"],"series_title":"PMLR","abstract":[{"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.","lang":"eng"}],"project":[{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","call_identifier":"FWF","name":"The Wittgenstein Prize"}],"date_published":"2020-01-01T00:00:00Z","quality_controlled":"1","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"page":"4082-4093"},{"_id":"9415","file_date_updated":"2021-05-25T09:51:36Z","year":"2020","date_created":"2021-05-23T22:01:45Z","citation":{"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.","ama":"Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: <i>37th International Conference on Machine Learning, ICML 2020</i>. Vol 119. ; 2020:5533-5543.","short":"M. Kurtz, J. Kopinsky, R. Gelashvili, A. Matveev, J. Carr, M. Goin, W. Leiserson, S. Moore, B. Nell, N. Shavit, D.-A. Alistarh, in:, 37th International Conference on Machine Learning, ICML 2020, 2020, pp. 5533–5543.","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.","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.","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.","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."},"date_published":"2020-07-12T00:00:00Z","volume":119,"quality_controlled":"1","page":"5533-5543","department":[{"_id":"DaAl"}],"ddc":["000"],"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"}],"language":[{"iso":"eng"}],"publication":"37th International Conference on Machine Learning, ICML 2020","article_processing_charge":"No","oa":1,"date_updated":"2023-02-23T13:57:24Z","title":"Inducing and exploiting activation sparsity for fast neural network inference","scopus_import":"1","has_accepted_license":"1","intvolume":"       119","type":"conference","status":"public","author":[{"last_name":"Kurtz","first_name":"Mark","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","full_name":"Matveev, Alexander","first_name":"Alexander"},{"first_name":"John","full_name":"Carr, John","last_name":"Carr"},{"full_name":"Goin, Michael","first_name":"Michael","last_name":"Goin"},{"first_name":"William","full_name":"Leiserson, William","last_name":"Leiserson"},{"first_name":"Sage","full_name":"Moore, Sage","last_name":"Moore"},{"full_name":"Nell, Bill","first_name":"Bill","last_name":"Nell"},{"last_name":"Shavit","full_name":"Shavit, Nir","first_name":"Nir"},{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian"}],"month":"07","publication_identifier":{"issn":["2640-3498"]},"conference":{"location":"Online","end_date":"2020-07-18","name":"ICML: International Conference on Machine Learning","start_date":"2020-07-12"},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","file":[{"success":1,"checksum":"2aaaa7d7226e49161311d91627cf783b","file_name":"2020_PMLR_Kurtz.pdf","file_id":"9421","date_created":"2021-05-25T09:51:36Z","date_updated":"2021-05-25T09:51:36Z","creator":"kschuh","content_type":"application/pdf","relation":"main_file","file_size":741899,"access_level":"open_access"}],"day":"12","oa_version":"Published Version"},{"year":"2020","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>","short":"J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77 (2020) 310–323.e7.","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>.","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>"},"date_created":"2021-06-08T06:37:09Z","doi":"10.1016/j.molcel.2019.10.011","article_type":"original","_id":"9526","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."}],"external_id":{"pmid":["31732458"]},"publication_status":"published","date_published":"2020-01-16T00:00:00Z","publisher":"Elsevier","quality_controlled":"1","volume":77,"department":[{"_id":"DaZi"}],"page":"310-323.e7","extern":"1","scopus_import":"1","intvolume":"        77","issue":"2","article_processing_charge":"No","oa":1,"date_updated":"2021-12-14T07:51:15Z","title":"DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts","publication":"Molecular Cell","language":[{"iso":"eng"}],"month":"01","publication_identifier":{"issn":["1097-2765"],"eissn":["1097-4164"]},"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.molcel.2019.10.011"}],"oa_version":"Published Version","day":"16","author":[{"full_name":"Choi, Jaemyung","first_name":"Jaemyung","last_name":"Choi"},{"full_name":"Lyons, David B.","first_name":"David B.","last_name":"Lyons"},{"last_name":"Kim","full_name":"Kim, M. Yvonne","first_name":"M. Yvonne"},{"first_name":"Jonathan D.","full_name":"Moore, Jonathan D.","last_name":"Moore"},{"full_name":"Zilberman, Daniel","first_name":"Daniel","orcid":"0000-0002-0123-8649","id":"6973db13-dd5f-11ea-814e-b3e5455e9ed1","last_name":"Zilberman"}],"status":"public","type":"journal_article","pmid":1},{"project":[{"grant_number":"I4887","_id":"0aa4bc98-070f-11eb-9043-e6fff9c6a316","name":"Discretization in Geometry and Dynamics"}],"volume":11,"quality_controlled":"1","department":[{"_id":"HeEd"}],"page":"162-182","license":"https://creativecommons.org/licenses/by/3.0/","date_published":"2020-12-14T00:00:00Z","publisher":"Carleton University","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"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","image":"/images/cc_by.png","short":"CC BY (3.0)","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)"},"ddc":["510","000"],"_id":"9630","article_type":"original","file_date_updated":"2021-08-11T11:55:11Z","doi":"10.20382/jocg.v11i2a7","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>","ama":"Edelsbrunner H, Virk Z, Wagner H. Topological data analysis in information space. <i>Journal of Computational Geometry</i>. 2020;11(2):162-182. doi:<a href=\"https://doi.org/10.20382/jocg.v11i2a7\">10.20382/jocg.v11i2a7</a>","ieee":"H. Edelsbrunner, Z. Virk, and H. Wagner, “Topological data analysis in information space,” <i>Journal of Computational Geometry</i>, vol. 11, no. 2. Carleton University, pp. 162–182, 2020.","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.","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>."},"date_created":"2021-07-04T22:01:26Z","year":"2020","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).","status":"public","type":"journal_article","author":[{"first_name":"Herbert","full_name":"Edelsbrunner, Herbert","orcid":"0000-0002-9823-6833","last_name":"Edelsbrunner","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Ziga","full_name":"Virk, Ziga","id":"2E36B656-F248-11E8-B48F-1D18A9856A87","last_name":"Virk"},{"last_name":"Wagner","id":"379CA8B8-F248-11E8-B48F-1D18A9856A87","full_name":"Wagner, Hubert","first_name":"Hubert"}],"file":[{"date_created":"2021-08-11T11:55:11Z","date_updated":"2021-08-11T11:55:11Z","success":1,"file_id":"9882","checksum":"f02d0b2b3838e7891a6c417fc34ffdcd","file_name":"2020_JournalOfComputationalGeometry_Edelsbrunner.pdf","relation":"main_file","file_size":1449234,"access_level":"open_access","content_type":"application/pdf","creator":"asandaue"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","day":"14","oa_version":"Published Version","publication_identifier":{"eissn":["1920180X"]},"month":"12","language":[{"iso":"eng"}],"publication":"Journal of Computational Geometry","title":"Topological data analysis in information space","issue":"2","article_processing_charge":"Yes","oa":1,"date_updated":"2021-08-11T12:26:34Z","has_accepted_license":"1","intvolume":"        11","scopus_import":"1"},{"project":[{"name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223"}],"department":[{"_id":"DaAl"}],"page":"22361-22372","volume":33,"quality_controlled":"1","publisher":"Curran Associates","date_published":"2020-12-06T00:00:00Z","publication_status":"published","external_id":{"arxiv":["2002.11505"]},"abstract":[{"lang":"eng","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."}],"_id":"9631","date_created":"2021-07-04T22:01:26Z","citation":{"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.","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.","mla":"Aksenov, Vitaly, et al. “Scalable Belief Propagation via Relaxed Scheduling.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 22361–72.","ista":"Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via relaxed scheduling. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 22361–22372.","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."},"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).","year":"2020","ec_funded":1,"type":"conference","status":"public","author":[{"full_name":"Aksenov, Vitaly","first_name":"Vitaly","last_name":"Aksenov"},{"last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian"},{"first_name":"Janne","full_name":"Korhonen, Janne","id":"C5402D42-15BC-11E9-A202-CA2BE6697425","last_name":"Korhonen"}],"arxiv":1,"main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html","open_access":"1"}],"day":"06","oa_version":"Published Version","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","conference":{"start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems","end_date":"2020-12-12","location":"Vancouver, Canada"},"publication_identifier":{"isbn":["9781713829546"],"issn":["10495258"]},"month":"12","language":[{"iso":"eng"}],"publication":"Advances in Neural Information Processing Systems","title":"Scalable belief propagation via relaxed scheduling","oa":1,"date_updated":"2023-02-23T14:03:03Z","article_processing_charge":"No","intvolume":"        33","scopus_import":"1"}]