[{"publication":"Proceedings of the AAAI Conference on Artificial Intelligence","article_processing_charge":"No","year":"2020","language":[{"iso":"eng"}],"acknowledgement":"This research was supported by the Austrian Science Fund (FWF) under grants S11402-N23 (RiSE/SHiNE), Z211-N23 (Wittgenstein Award), and M 2369-N33 (Meitner fellowship).","intvolume":"        34","title":"All-pay bidding games on graphs","article_type":"original","conference":{"start_date":"2020-02-07","name":"AAAI: Conference on Artificial Intelligence","end_date":"2020-02-12","location":"New York, NY, United States"},"doi":"10.1609/aaai.v34i02.5546","oa_version":"Preprint","citation":{"ista":"Avni G, Ibsen-Jensen R, Tkadlec J. 2020. All-pay bidding games on graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 34(02), 1798–1805.","chicago":"Avni, Guy, Rasmus Ibsen-Jensen, and Josef Tkadlec. “All-Pay Bidding Games on Graphs.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. Association for the Advancement of Artificial Intelligence, 2020. <a href=\"https://doi.org/10.1609/aaai.v34i02.5546\">https://doi.org/10.1609/aaai.v34i02.5546</a>.","apa":"Avni, G., Ibsen-Jensen, R., &#38; Tkadlec, J. (2020). All-pay bidding games on graphs. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. New York, NY, United States: Association for the Advancement of Artificial Intelligence. <a href=\"https://doi.org/10.1609/aaai.v34i02.5546\">https://doi.org/10.1609/aaai.v34i02.5546</a>","short":"G. Avni, R. Ibsen-Jensen, J. Tkadlec, Proceedings of the AAAI Conference on Artificial Intelligence 34 (2020) 1798–1805.","ieee":"G. Avni, R. Ibsen-Jensen, and J. Tkadlec, “All-pay bidding games on graphs,” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 34, no. 02. Association for the Advancement of Artificial Intelligence, pp. 1798–1805, 2020.","mla":"Avni, Guy, et al. “All-Pay Bidding Games on Graphs.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 34, no. 02, Association for the Advancement of Artificial Intelligence, 2020, pp. 1798–805, doi:<a href=\"https://doi.org/10.1609/aaai.v34i02.5546\">10.1609/aaai.v34i02.5546</a>.","ama":"Avni G, Ibsen-Jensen R, Tkadlec J. All-pay bidding games on graphs. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. 2020;34(02):1798-1805. doi:<a href=\"https://doi.org/10.1609/aaai.v34i02.5546\">10.1609/aaai.v34i02.5546</a>"},"date_updated":"2023-09-05T12:40:00Z","type":"journal_article","scopus_import":"1","month":"04","date_published":"2020-04-03T00:00:00Z","page":"1798-1805","publisher":"Association for the Advancement of Artificial Intelligence","status":"public","date_created":"2021-02-25T09:05:18Z","department":[{"_id":"ToHe"},{"_id":"KrCh"}],"day":"03","arxiv":1,"issue":"02","quality_controlled":"1","external_id":{"arxiv":["1911.08360"]},"author":[{"full_name":"Avni, Guy","id":"463C8BC2-F248-11E8-B48F-1D18A9856A87","first_name":"Guy","orcid":"0000-0001-5588-8287","last_name":"Avni"},{"full_name":"Ibsen-Jensen, Rasmus","id":"3B699956-F248-11E8-B48F-1D18A9856A87","last_name":"Ibsen-Jensen","orcid":"0000-0003-4783-0389","first_name":"Rasmus"},{"id":"3F24CCC8-F248-11E8-B48F-1D18A9856A87","first_name":"Josef","last_name":"Tkadlec","orcid":"0000-0002-1097-9684","full_name":"Tkadlec, Josef"}],"volume":34,"publication_identifier":{"isbn":["9781577358350"],"eissn":["2374-3468"],"issn":["2159-5399"]},"project":[{"name":"Rigorous Systems Engineering","_id":"25F2ACDE-B435-11E9-9278-68D0E5697425","grant_number":"S11402-N23","call_identifier":"FWF"},{"call_identifier":"FWF","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize"},{"call_identifier":"FWF","grant_number":"M02369","_id":"264B3912-B435-11E9-9278-68D0E5697425","name":"Formal Methods meets Algorithmic Game Theory"}],"_id":"9197","abstract":[{"text":"In this paper we introduce and study all-pay bidding games, a class of two player, zero-sum games on graphs. The game proceeds as follows. We place a token on some vertex in the graph and assign budgets to the two players. Each turn, each player submits a sealed legal bid (non-negative and below their remaining budget), which is deducted from their budget and the highest bidder moves the token onto an adjacent vertex. The game ends once a sink is reached, and Player 1 pays Player 2 the outcome that is associated with the sink. The players attempt to maximize their expected outcome. Our games model settings where effort (of no inherent value) needs to be invested in an ongoing and stateful manner. On the negative side, we show that even in simple games on DAGs, optimal strategies may require a distribution over bids with infinite support. A central quantity in bidding games is the ratio of the players budgets. On the positive side, we show a simple FPTAS for DAGs, that, for each budget ratio, outputs an approximation for the optimal strategy for that ratio. We also implement it, show that it performs well, and suggests interesting properties of these games. Then, given an outcome c, we show an algorithm for finding the necessary and sufficient initial ratio for guaranteeing outcome c with probability 1 and a strategy ensuring such. Finally, while the general case has not previously been studied, solving the specific game in which Player 1 wins iff he wins the first two auctions, has been long stated as an open question, which we solve.","lang":"eng"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publication_status":"published"},{"day":"13","department":[{"_id":"MaMo"}],"quality_controlled":"1","arxiv":1,"oa":1,"file":[{"success":1,"date_created":"2021-03-02T15:38:14Z","checksum":"f042c8d4316bd87c6361aa76f1fbdbbe","access_level":"open_access","file_name":"2020_PMLR_Shevchenko.pdf","file_id":"9217","relation":"main_file","content_type":"application/pdf","file_size":5336380,"creator":"dernst","date_updated":"2021-03-02T15:38:14Z"}],"publisher":"ML Research Press","page":"8773-8784","status":"public","date_created":"2021-02-25T09:36:22Z","abstract":[{"lang":"eng","text":"The optimization of multilayer neural networks typically leads to a solution\r\nwith zero training error, yet the landscape can exhibit spurious local minima\r\nand the minima can be disconnected. In this paper, we shed light on this\r\nphenomenon: we show that the combination of stochastic gradient descent (SGD)\r\nand over-parameterization makes the landscape of multilayer neural networks\r\napproximately connected and thus more favorable to optimization. More\r\nspecifically, we prove that SGD solutions are connected via a piecewise linear\r\npath, and the increase in loss along this path vanishes as the number of\r\nneurons grows large. This result is a consequence of the fact that the\r\nparameters found by SGD are increasingly dropout stable as the network becomes\r\nwider. We show that, if we remove part of the neurons (and suitably rescale the\r\nremaining ones), the change in loss is independent of the total number of\r\nneurons, and it depends only on how many neurons are left. Our results exhibit\r\na mild dependence on the input dimension: they are dimension-free for two-layer\r\nnetworks and depend linearly on the dimension for multilayer networks. We\r\nvalidate our theoretical findings with numerical experiments for different\r\narchitectures and classification tasks."}],"_id":"9198","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","author":[{"last_name":"Shevchenko","first_name":"Alexander","full_name":"Shevchenko, Alexander"},{"id":"27EB676C-8706-11E9-9510-7717E6697425","last_name":"Mondelli","orcid":"0000-0002-3242-7020","first_name":"Marco","full_name":"Mondelli, Marco"}],"external_id":{"arxiv":["1912.10095"]},"project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"volume":119,"acknowledgement":"M. Mondelli was partially supported by the 2019 LopezLoreta Prize. The authors thank Phan-Minh Nguyen for helpful discussions and the IST Distributed Algorithms and Systems Lab for providing computational resources.","intvolume":"       119","title":"Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks","article_processing_charge":"No","publication":"Proceedings of the 37th International Conference on Machine Learning","year":"2020","language":[{"iso":"eng"}],"citation":{"mla":"Shevchenko, Alexander, and Marco Mondelli. “Landscape Connectivity and Dropout Stability of SGD Solutions for Over-Parameterized Neural Networks.” <i>Proceedings of the 37th International Conference on Machine Learning</i>, vol. 119, ML Research Press, 2020, pp. 8773–84.","ieee":"A. Shevchenko and M. Mondelli, “Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks,” in <i>Proceedings of the 37th International Conference on Machine Learning</i>, 2020, vol. 119, pp. 8773–8784.","ama":"Shevchenko A, Mondelli M. Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In: <i>Proceedings of the 37th International Conference on Machine Learning</i>. Vol 119. ML Research Press; 2020:8773-8784.","chicago":"Shevchenko, Alexander, and Marco Mondelli. “Landscape Connectivity and Dropout Stability of SGD Solutions for Over-Parameterized Neural Networks.” In <i>Proceedings of the 37th International Conference on Machine Learning</i>, 119:8773–84. ML Research Press, 2020.","ista":"Shevchenko A, Mondelli M. 2020. Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. Proceedings of the 37th International Conference on Machine Learning. vol. 119, 8773–8784.","short":"A. Shevchenko, M. Mondelli, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 8773–8784.","apa":"Shevchenko, A., &#38; Mondelli, M. (2020). Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In <i>Proceedings of the 37th International Conference on Machine Learning</i> (Vol. 119, pp. 8773–8784). ML Research Press."},"date_updated":"2024-09-10T13:03:19Z","has_accepted_license":"1","type":"conference","date_published":"2020-07-13T00:00:00Z","month":"07","ddc":["000"],"oa_version":"Published Version","file_date_updated":"2021-03-02T15:38:14Z"},{"publisher":"IEEE","page":"244-256","file":[{"date_updated":"2021-02-26T16:38:14Z","creator":"mgarcias","content_type":"application/pdf","file_size":1125794,"file_id":"9203","relation":"main_file","access_level":"open_access","file_name":"main.pdf","date_created":"2021-02-26T16:38:14Z","checksum":"8f97f229316c3b3a6f0cf99297aa0941"}],"status":"public","date_created":"2021-02-26T16:38:24Z","department":[{"_id":"ToHe"}],"isi":1,"day":"01","oa":1,"quality_controlled":"1","external_id":{"isi":["000680435100021"]},"author":[{"id":"4B3207F6-F248-11E8-B48F-1D18A9856A87","first_name":"Miriam","last_name":"Garcia Soto","orcid":"0000-0003-2936-5719","full_name":"Garcia Soto, Miriam"},{"full_name":"Prabhakar, Pavithra","last_name":"Prabhakar","first_name":"Pavithra"}],"publication_identifier":{"eissn":["2576-3172"],"eisbn":["9781728183244"]},"project":[{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","call_identifier":"FWF","grant_number":"Z211"}],"_id":"9202","abstract":[{"lang":"eng","text":"We propose a novel hybridization method for stability analysis that over-approximates nonlinear dynamical systems by switched systems with linear inclusion dynamics. We observe that existing hybridization techniques for safety analysis that over-approximate nonlinear dynamical systems by switched affine inclusion dynamics and provide fixed approximation error, do not suffice for stability analysis. Hence, we propose a hybridization method that provides a state-dependent error which converges to zero as the state tends to the equilibrium point. The crux of our hybridization computation is an elegant recursive algorithm that uses partial derivatives of a given function to obtain upper and lower bound matrices for the over-approximating linear inclusion. We illustrate our method on some examples to demonstrate the application of the theory for stability analysis. In particular, our method is able to establish stability of a nonlinear system which does not admit a polynomial Lyapunov function."}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publication_status":"published","publication":"2020 IEEE Real-Time Systems Symposium","article_processing_charge":"No","year":"2020","language":[{"iso":"eng"}],"acknowledgement":"Miriam Garc´ıa Soto was partially supported by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). Pavithra Prabhakar was partially supported by NSF CAREER Award No. 1552668, NSF Award No. 2008957 and ONR YIP Award No. N000141712577.","title":"Hybridization for stability verification of nonlinear switched systems","conference":{"location":"Houston, TX, USA ","end_date":"2020-12-04","name":"RTTS: Real-Time Systems Symposium","start_date":"2020-12-01"},"doi":"10.1109/RTSS49844.2020.00031","file_date_updated":"2021-02-26T16:38:14Z","oa_version":"Submitted Version","date_updated":"2024-02-22T13:25:19Z","has_accepted_license":"1","citation":{"apa":"Garcia Soto, M., &#38; Prabhakar, P. (2020). Hybridization for stability verification of nonlinear switched systems. In <i>2020 IEEE Real-Time Systems Symposium</i> (pp. 244–256). Houston, TX, USA : IEEE. <a href=\"https://doi.org/10.1109/RTSS49844.2020.00031\">https://doi.org/10.1109/RTSS49844.2020.00031</a>","short":"M. Garcia Soto, P. Prabhakar, in:, 2020 IEEE Real-Time Systems Symposium, IEEE, 2020, pp. 244–256.","ista":"Garcia Soto M, Prabhakar P. 2020. Hybridization for stability verification of nonlinear switched systems. 2020 IEEE Real-Time Systems Symposium. RTTS: Real-Time Systems Symposium, 244–256.","chicago":"Garcia Soto, Miriam, and Pavithra Prabhakar. “Hybridization for Stability Verification of Nonlinear Switched Systems.” In <i>2020 IEEE Real-Time Systems Symposium</i>, 244–56. IEEE, 2020. <a href=\"https://doi.org/10.1109/RTSS49844.2020.00031\">https://doi.org/10.1109/RTSS49844.2020.00031</a>.","ama":"Garcia Soto M, Prabhakar P. Hybridization for stability verification of nonlinear switched systems. In: <i>2020 IEEE Real-Time Systems Symposium</i>. IEEE; 2020:244-256. doi:<a href=\"https://doi.org/10.1109/RTSS49844.2020.00031\">10.1109/RTSS49844.2020.00031</a>","ieee":"M. Garcia Soto and P. Prabhakar, “Hybridization for stability verification of nonlinear switched systems,” in <i>2020 IEEE Real-Time Systems Symposium</i>, Houston, TX, USA , 2020, pp. 244–256.","mla":"Garcia Soto, Miriam, and Pavithra Prabhakar. “Hybridization for Stability Verification of Nonlinear Switched Systems.” <i>2020 IEEE Real-Time Systems Symposium</i>, IEEE, 2020, pp. 244–56, doi:<a href=\"https://doi.org/10.1109/RTSS49844.2020.00031\">10.1109/RTSS49844.2020.00031</a>."},"type":"conference","date_published":"2020-12-01T00:00:00Z","month":"12","ddc":["000"]},{"acknowledgement":"The FlexMaps Pavilion has been awarded First Prize at the “Competition and Exhibition of innovative lightweight structures” organized by the IASS Working Group 21 within the FORM and FORCE, joint international conference of IASS Symposium 2019 and Structural Membranes 2019 (Barcelona, 7-11 October 2019) with the following motivation: “for its structural innovation of bending-twisting system, connection constructability and exquisite craftmanship”[20]. The authors would like to acknowledge the Visual Computing Lab Staff of ISTI - CNR, in particular Thomas Alderighi, Marco Callieri, Paolo Pingi; Antonio Rizzo of IPCF - CNR; and the Administrative Staff of ISTI - CNR. This research was partially funded by the EU H2020 Programme EVOCATION: Advanced Visual and Geometric Computing for 3D Capture, Display, and Fabrication (grant no. 813170).","title":"A bending-active twisted-arch plywood structure: Computational design and fabrication of the FlexMaps Pavilion","intvolume":"         2","publication":"SN Applied Sciences","article_processing_charge":"No","year":"2020","language":[{"iso":"eng"}],"type":"journal_article","citation":{"ieee":"F. Laccone <i>et al.</i>, “A bending-active twisted-arch plywood structure: Computational design and fabrication of the FlexMaps Pavilion,” <i>SN Applied Sciences</i>, vol. 2, no. 9. Springer Nature, 2020.","mla":"Laccone, Francesco, et al. “A Bending-Active Twisted-Arch Plywood Structure: Computational Design and Fabrication of the FlexMaps Pavilion.” <i>SN Applied Sciences</i>, vol. 2, no. 9, 1505, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1007/s42452-020-03305-w\">10.1007/s42452-020-03305-w</a>.","ama":"Laccone F, Malomo L, Perez Rodriguez J, et al. A bending-active twisted-arch plywood structure: Computational design and fabrication of the FlexMaps Pavilion. <i>SN Applied Sciences</i>. 2020;2(9). doi:<a href=\"https://doi.org/10.1007/s42452-020-03305-w\">10.1007/s42452-020-03305-w</a>","ista":"Laccone F, Malomo L, Perez Rodriguez J, Pietroni N, Ponchio F, Bickel B, Cignoni P. 2020. A bending-active twisted-arch plywood structure: Computational design and fabrication of the FlexMaps Pavilion. SN Applied Sciences. 2(9), 1505.","chicago":"Laccone, Francesco, Luigi Malomo, Jesus Perez Rodriguez, Nico Pietroni, Federico Ponchio, Bernd Bickel, and Paolo Cignoni. “A Bending-Active Twisted-Arch Plywood Structure: Computational Design and Fabrication of the FlexMaps Pavilion.” <i>SN Applied Sciences</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1007/s42452-020-03305-w\">https://doi.org/10.1007/s42452-020-03305-w</a>.","apa":"Laccone, F., Malomo, L., Perez Rodriguez, J., Pietroni, N., Ponchio, F., Bickel, B., &#38; Cignoni, P. (2020). A bending-active twisted-arch plywood structure: Computational design and fabrication of the FlexMaps Pavilion. <i>SN Applied Sciences</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s42452-020-03305-w\">https://doi.org/10.1007/s42452-020-03305-w</a>","short":"F. Laccone, L. Malomo, J. Perez Rodriguez, N. Pietroni, F. Ponchio, B. Bickel, P. Cignoni, SN Applied Sciences 2 (2020)."},"date_updated":"2021-03-03T09:43:14Z","article_number":"1505","date_published":"2020-09-01T00:00:00Z","month":"09","scopus_import":"1","doi":"10.1007/s42452-020-03305-w","article_type":"original","oa_version":"None","day":"01","department":[{"_id":"BeBi"}],"quality_controlled":"1","issue":"9","publisher":"Springer Nature","date_created":"2021-02-28T23:01:25Z","status":"public","abstract":[{"lang":"eng","text":"Bending-active structures are able to efficiently produce complex curved shapes from flat panels. The desired deformation of the panels derives from the proper selection of their elastic properties. Optimized panels, called FlexMaps, are designed such that, once they are bent and assembled, the resulting static equilibrium configuration matches a desired input 3D shape. The FlexMaps elastic properties are controlled by locally varying spiraling geometric mesostructures, which are optimized in size and shape to match specific bending requests, namely the global curvature of the target shape. The design pipeline starts from a quad mesh representing the input 3D shape, which defines the edge size and the total amount of spirals: every quad will embed one spiral. Then, an optimization algorithm tunes the geometry of the spirals by using a simplified pre-computed rod model. This rod model is derived from a non-linear regression algorithm which approximates the non-linear behavior of solid FEM spiral models subject to hundreds of load combinations. This innovative pipeline has been applied to the project of a lightweight plywood pavilion named FlexMaps Pavilion, which is a single-layer piecewise twisted arch that fits a bounding box of 3.90x3.96x3.25 meters. This case study serves to test the applicability of this methodology at the architectural scale. The structure is validated via FE analyses and the fabrication of the full scale prototype."}],"_id":"9208","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Laccone, Francesco","last_name":"Laccone","first_name":"Francesco"},{"full_name":"Malomo, Luigi","last_name":"Malomo","first_name":"Luigi"},{"full_name":"Perez Rodriguez, Jesus","first_name":"Jesus","last_name":"Perez Rodriguez","id":"2DC83906-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Pietroni, Nico","last_name":"Pietroni","first_name":"Nico"},{"full_name":"Ponchio, Federico","first_name":"Federico","last_name":"Ponchio"},{"last_name":"Bickel","orcid":"0000-0001-6511-9385","first_name":"Bernd","id":"49876194-F248-11E8-B48F-1D18A9856A87","full_name":"Bickel, Bernd"},{"first_name":"Paolo","last_name":"Cignoni","full_name":"Cignoni, Paolo"}],"publication_identifier":{"eissn":["25233971"]},"volume":2},{"author":[{"last_name":"Nguyen","first_name":"Quynh","full_name":"Nguyen, Quynh"},{"full_name":"Mondelli, Marco","first_name":"Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425"}],"external_id":{"arxiv":["2002.07867"]},"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"}],"volume":33,"abstract":[{"text":"Recent works have shown that gradient descent can find a global minimum for over-parameterized neural networks where the widths of all the hidden layers scale polynomially with N (N being the number of training samples). In this paper, we prove that, for deep networks, a single layer of width N following the input layer suffices to ensure a similar guarantee. In particular, all the remaining layers are allowed to have constant widths, and form a pyramidal topology. We show an application of our result to the widely used LeCun’s initialization and obtain an over-parameterization requirement for the single wide layer of order N2.\r\n","lang":"eng"}],"_id":"9221","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","publication_status":"published","publisher":"Curran Associates","page":"11961–11972","status":"public","date_created":"2021-03-03T12:06:02Z","day":"07","department":[{"_id":"MaMo"}],"quality_controlled":"1","arxiv":1,"oa":1,"conference":{"end_date":"2020-12-12","start_date":"2020-12-06","name":"NeurIPS: Neural Information Processing Systems","location":"Vancouver, Canada"},"oa_version":"Preprint","date_updated":"2024-09-10T13:03:17Z","citation":{"short":"Q. Nguyen, M. Mondelli, in:, 34th Conference on Neural Information Processing Systems, Curran Associates, 2020, pp. 11961–11972.","apa":"Nguyen, Q., &#38; Mondelli, M. (2020). Global convergence of deep networks with one wide layer followed by pyramidal topology. In <i>34th Conference on Neural Information Processing Systems</i> (Vol. 33, pp. 11961–11972). Vancouver, Canada: Curran Associates.","chicago":"Nguyen, Quynh, and Marco Mondelli. “Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology.” In <i>34th Conference on Neural Information Processing Systems</i>, 33:11961–11972. Curran Associates, 2020.","ista":"Nguyen Q, Mondelli M. 2020. Global convergence of deep networks with one wide layer followed by pyramidal topology. 34th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 33, 11961–11972.","ama":"Nguyen Q, Mondelli M. Global convergence of deep networks with one wide layer followed by pyramidal topology. In: <i>34th Conference on Neural Information Processing Systems</i>. Vol 33. Curran Associates; 2020:11961–11972.","mla":"Nguyen, Quynh, and Marco Mondelli. “Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology.” <i>34th Conference on Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 11961–11972.","ieee":"Q. Nguyen and M. Mondelli, “Global convergence of deep networks with one wide layer followed by pyramidal topology,” in <i>34th Conference on Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 11961–11972."},"type":"conference","month":"07","date_published":"2020-07-07T00:00:00Z","publication":"34th Conference on Neural Information Processing Systems","article_processing_charge":"No","year":"2020","language":[{"iso":"eng"}],"acknowledgement":"The authors would like to thank Jan Maas, Mahdi Soltanolkotabi, and Daniel Soudry for the helpful discussions, Marius Kloft, Matthias Hein and Quoc Dinh Tran for proofreading portions of a prior version of this paper, and James Martens for a clarification concerning LeCun’s initialization. M. Mondelli was partially supported by the 2019 Lopez-Loreta Prize. Q. Nguyen was partially supported by the German Research Foundation (DFG) award KL 2698/2-1.","intvolume":"        33","title":"Global convergence of deep networks with one wide layer followed by pyramidal topology","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2002.07867"}]},{"date_created":"2021-03-05T18:00:47Z","status":"public","year":"2020","article_processing_charge":"No","publisher":"Institute of Science and Technology Austria","file":[{"file_size":13317557,"content_type":"application/x-zip-compressed","creator":"gkatsaro","date_updated":"2021-03-05T17:50:45Z","relation":"main_file","file_id":"9223","file_name":"DOI_SiteControlledHWs.zip","access_level":"open_access","checksum":"41b66e195ed3dbd73077feee77b05652","date_created":"2021-03-05T17:50:45Z"},{"creator":"dernst","date_updated":"2021-03-10T07:31:50Z","file_size":3515,"content_type":"text/plain","relation":"main_file","file_id":"9233","file_name":"Readme.txt","access_level":"open_access","checksum":"a1dc5f710ba4b3bb7f248195ba754ab2","date_created":"2021-03-10T07:31:50Z","success":1}],"title":"Transport data for: Site‐controlled uniform Ge/Si Hut wires with electrically tunable spin–orbit coupling","oa":1,"day":"16","department":[{"_id":"GeKa"}],"oa_version":"Published Version","file_date_updated":"2021-03-10T07:31:50Z","doi":"10.15479/AT:ISTA:9222","contributor":[{"id":"38DB5788-F248-11E8-B48F-1D18A9856A87","last_name":"Katsaros","first_name":"Georgios","contributor_type":"research_group"}],"author":[{"full_name":"Katsaros, Georgios","orcid":"0000-0001-8342-202X","last_name":"Katsaros","first_name":"Georgios","id":"38DB5788-F248-11E8-B48F-1D18A9856A87"}],"related_material":{"record":[{"id":"7541","relation":"used_in_publication","status":"public"}]},"ddc":["530"],"date_published":"2020-03-16T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"03","tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","short":"CC0 (1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"type":"research_data","citation":{"ama":"Katsaros G. Transport data for: Site‐controlled uniform Ge/Si Hut wires with electrically tunable spin–orbit coupling. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:9222\">10.15479/AT:ISTA:9222</a>","ieee":"G. Katsaros, “Transport data for: Site‐controlled uniform Ge/Si Hut wires with electrically tunable spin–orbit coupling.” Institute of Science and Technology Austria, 2020.","mla":"Katsaros, Georgios. <i>Transport Data for: Site‐controlled Uniform Ge/Si Hut Wires with Electrically Tunable Spin–Orbit Coupling</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:9222\">10.15479/AT:ISTA:9222</a>.","apa":"Katsaros, G. (2020). Transport data for: Site‐controlled uniform Ge/Si Hut wires with electrically tunable spin–orbit coupling. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:9222\">https://doi.org/10.15479/AT:ISTA:9222</a>","short":"G. Katsaros, (2020).","ista":"Katsaros G. 2020. Transport data for: Site‐controlled uniform Ge/Si Hut wires with electrically tunable spin–orbit coupling, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:9222\">10.15479/AT:ISTA:9222</a>.","chicago":"Katsaros, Georgios. “Transport Data for: Site‐controlled Uniform Ge/Si Hut Wires with Electrically Tunable Spin–Orbit Coupling.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:9222\">https://doi.org/10.15479/AT:ISTA:9222</a>."},"has_accepted_license":"1","date_updated":"2024-02-21T12:42:13Z","_id":"9222"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","_id":"9249","abstract":[{"text":"Rhombic dodecahedron is a space filling polyhedron which represents the close packing of spheres in 3D space and the Voronoi structures of the face centered cubic (FCC) lattice. In this paper, we describe a new coordinate system where every 3-integer coordinates grid point corresponds to a rhombic dodecahedron centroid. In order to illustrate the interest of the new coordinate system, we propose the characterization of 3D digital plane with its topological features, such as the interrelation between the thickness of the digital plane and the separability constraint we aim to obtain. We also present the characterization of 3D digital lines and study it as the intersection of multiple digital planes. Characterization of 3D digital sphere with relevant topological features is proposed as well along with the 48-symmetry appearing in the new coordinate system.","lang":"eng"}],"volume":4,"publication_identifier":{"issn":["2353-3390"]},"project":[{"grant_number":"788183","call_identifier":"H2020","_id":"266A2E9E-B435-11E9-9278-68D0E5697425","name":"Alpha Shape Theory Extended"},{"name":"Persistence and stability of geometric complexes","_id":"2561EBF4-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"I02979-N35"}],"author":[{"id":"3C2B033E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5372-7890","last_name":"Biswas","first_name":"Ranita","full_name":"Biswas, Ranita"},{"full_name":"Largeteau-Skapin, Gaëlle","last_name":"Largeteau-Skapin","first_name":"Gaëlle"},{"last_name":"Zrour","first_name":"Rita","full_name":"Zrour, Rita"},{"first_name":"Eric","last_name":"Andres","full_name":"Andres, Eric"}],"oa":1,"issue":"1","quality_controlled":"1","department":[{"_id":"HeEd"}],"day":"17","status":"public","date_created":"2021-03-16T08:55:19Z","page":"143-158","file":[{"success":1,"date_created":"2021-03-22T08:56:37Z","checksum":"4a1043fa0548a725d464017fe2483ce0","access_level":"open_access","file_name":"2020_MathMorpholTheoryAppl_Biswas.pdf","file_id":"9272","relation":"main_file","content_type":"application/pdf","file_size":3668725,"date_updated":"2021-03-22T08:56:37Z","creator":"dernst"}],"publisher":"De Gruyter","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"month":"11","date_published":"2020-11-17T00:00:00Z","ddc":["510"],"has_accepted_license":"1","citation":{"short":"R. Biswas, G. Largeteau-Skapin, R. Zrour, E. Andres, Mathematical Morphology - Theory and Applications 4 (2020) 143–158.","apa":"Biswas, R., Largeteau-Skapin, G., Zrour, R., &#38; Andres, E. (2020). Digital objects in rhombic dodecahedron grid. <i>Mathematical Morphology - Theory and Applications</i>. De Gruyter. <a href=\"https://doi.org/10.1515/mathm-2020-0106\">https://doi.org/10.1515/mathm-2020-0106</a>","chicago":"Biswas, Ranita, Gaëlle Largeteau-Skapin, Rita Zrour, and Eric Andres. “Digital Objects in Rhombic Dodecahedron Grid.” <i>Mathematical Morphology - Theory and Applications</i>. De Gruyter, 2020. <a href=\"https://doi.org/10.1515/mathm-2020-0106\">https://doi.org/10.1515/mathm-2020-0106</a>.","ista":"Biswas R, Largeteau-Skapin G, Zrour R, Andres E. 2020. Digital objects in rhombic dodecahedron grid. Mathematical Morphology - Theory and Applications. 4(1), 143–158.","ama":"Biswas R, Largeteau-Skapin G, Zrour R, Andres E. Digital objects in rhombic dodecahedron grid. <i>Mathematical Morphology - Theory and Applications</i>. 2020;4(1):143-158. doi:<a href=\"https://doi.org/10.1515/mathm-2020-0106\">10.1515/mathm-2020-0106</a>","mla":"Biswas, Ranita, et al. “Digital Objects in Rhombic Dodecahedron Grid.” <i>Mathematical Morphology - Theory and Applications</i>, vol. 4, no. 1, De Gruyter, 2020, pp. 143–58, doi:<a href=\"https://doi.org/10.1515/mathm-2020-0106\">10.1515/mathm-2020-0106</a>.","ieee":"R. Biswas, G. Largeteau-Skapin, R. Zrour, and E. Andres, “Digital objects in rhombic dodecahedron grid,” <i>Mathematical Morphology - Theory and Applications</i>, vol. 4, no. 1. De Gruyter, pp. 143–158, 2020."},"date_updated":"2021-03-22T09:01:50Z","type":"journal_article","file_date_updated":"2021-03-22T08:56:37Z","oa_version":"Published Version","ec_funded":1,"article_type":"original","doi":"10.1515/mathm-2020-0106","intvolume":"         4","title":"Digital objects in rhombic dodecahedron grid","acknowledgement":"This work has been partially supported by the European Research Council (ERC) under\r\nthe European Union’s Horizon 2020 research and innovation programme, grant no. 788183, and the DFG Collaborative Research Center TRR 109, ‘Discretization in Geometry and Dynamics’, Austrian Science Fund (FWF), grant no. I 02979-N35. ","language":[{"iso":"eng"}],"year":"2020","article_processing_charge":"No","publication":"Mathematical Morphology - Theory and Applications"},{"publication_identifier":{"issn":["0302-9743"],"isbn":["9783030687656"],"eissn":["1611-3349"]},"volume":12590,"project":[{"_id":"268116B8-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","grant_number":"Z00342","call_identifier":"FWF"}],"author":[{"last_name":"Pach","first_name":"János","id":"E62E3130-B088-11EA-B919-BF823C25FEA4","full_name":"Pach, János"},{"full_name":"Tardos, Gábor","last_name":"Tardos","first_name":"Gábor"},{"full_name":"Tóth, Géza","first_name":"Géza","last_name":"Tóth"}],"external_id":{"arxiv":["2006.14908"]},"publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"9299","abstract":[{"lang":"eng","text":"We call a multigraph non-homotopic if it can be drawn in the plane in such a way that no two edges connecting the same pair of vertices can be continuously transformed into each other without passing through a vertex, and no loop can be shrunk to its end-vertex in the same way. It is easy to see that a non-homotopic multigraph on   n>1  vertices can have arbitrarily many edges. We prove that the number of crossings between the edges of a non-homotopic multigraph with n vertices and   m>4n  edges is larger than   cm2n  for some constant   c>0 , and that this bound is tight up to a polylogarithmic factor. We also show that the lower bound is not asymptotically sharp as n is fixed and   m⟶∞ ."}],"date_created":"2021-03-28T22:01:44Z","status":"public","page":"359-371","publisher":"Springer Nature","oa":1,"arxiv":1,"quality_controlled":"1","department":[{"_id":"HeEd"}],"day":"20","series_title":"LNCS","oa_version":"Preprint","conference":{"location":"Virtual, Online","start_date":"2020-09-16","name":"GD: Graph Drawing and Network Visualization","end_date":"2020-09-18"},"doi":"10.1007/978-3-030-68766-3_28","scopus_import":"1","date_published":"2020-09-20T00:00:00Z","month":"09","type":"conference","citation":{"mla":"Pach, János, et al. “Crossings between Non-Homotopic Edges.” <i>28th International Symposium on Graph Drawing and Network Visualization</i>, vol. 12590, Springer Nature, 2020, pp. 359–71, doi:<a href=\"https://doi.org/10.1007/978-3-030-68766-3_28\">10.1007/978-3-030-68766-3_28</a>.","ieee":"J. Pach, G. Tardos, and G. Tóth, “Crossings between non-homotopic edges,” in <i>28th International Symposium on Graph Drawing and Network Visualization</i>, Virtual, Online, 2020, vol. 12590, pp. 359–371.","ama":"Pach J, Tardos G, Tóth G. Crossings between non-homotopic edges. In: <i>28th International Symposium on Graph Drawing and Network Visualization</i>. Vol 12590. LNCS. Springer Nature; 2020:359-371. doi:<a href=\"https://doi.org/10.1007/978-3-030-68766-3_28\">10.1007/978-3-030-68766-3_28</a>","chicago":"Pach, János, Gábor Tardos, and Géza Tóth. “Crossings between Non-Homotopic Edges.” In <i>28th International Symposium on Graph Drawing and Network Visualization</i>, 12590:359–71. LNCS. Springer Nature, 2020. <a href=\"https://doi.org/10.1007/978-3-030-68766-3_28\">https://doi.org/10.1007/978-3-030-68766-3_28</a>.","ista":"Pach J, Tardos G, Tóth G. 2020. Crossings between non-homotopic edges. 28th International Symposium on Graph Drawing and Network Visualization. GD: Graph Drawing and Network VisualizationLNCS vol. 12590, 359–371.","short":"J. Pach, G. Tardos, G. Tóth, in:, 28th International Symposium on Graph Drawing and Network Visualization, Springer Nature, 2020, pp. 359–371.","apa":"Pach, J., Tardos, G., &#38; Tóth, G. (2020). Crossings between non-homotopic edges. In <i>28th International Symposium on Graph Drawing and Network Visualization</i> (Vol. 12590, pp. 359–371). Virtual, Online: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-68766-3_28\">https://doi.org/10.1007/978-3-030-68766-3_28</a>"},"date_updated":"2021-04-06T11:32:32Z","year":"2020","language":[{"iso":"eng"}],"publication":"28th International Symposium on Graph Drawing and Network Visualization","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2006.14908"}],"title":"Crossings between non-homotopic edges","intvolume":"     12590","acknowledgement":"Supported by the National Research, Development and Innovation Office, NKFIH, KKP-133864, K-131529, K-116769, K-132696, by the Higher Educational Institutional Excellence Program 2019 NKFIH-1158-6/2019, the Austrian Science Fund (FWF), grant Z 342-N31, by the Ministry of Education and Science of the Russian Federation MegaGrant No. 075-15-2019-1926, and by the ERC Synergy Grant “Dynasnet” No. 810115. A full version can be found at https://arxiv.org/abs/2006.14908."},{"author":[{"full_name":"Avvakumov, Sergey","first_name":"Sergey","last_name":"Avvakumov","id":"3827DAC8-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Wagner, Uli","last_name":"Wagner","orcid":"0000-0002-1494-0568","first_name":"Uli","id":"36690CA2-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Isaac","last_name":"Mabillard","id":"32BF9DAA-F248-11E8-B48F-1D18A9856A87","full_name":"Mabillard, Isaac"},{"last_name":"Skopenkov","first_name":"A. B.","full_name":"Skopenkov, A. B."}],"external_id":{"arxiv":["1511.03501"],"isi":["000625983100001"]},"volume":75,"publication_identifier":{"issn":["0036-0279"]},"_id":"9308","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication_status":"published","page":"1156-1158","publisher":"IOP Publishing","status":"public","date_created":"2021-04-04T22:01:22Z","isi":1,"day":"01","department":[{"_id":"UlWa"}],"quality_controlled":"1","arxiv":1,"issue":"6","oa":1,"related_material":{"record":[{"status":"public","relation":"earlier_version","id":"8183"},{"id":"10220","relation":"later_version","status":"public"}]},"article_type":"original","doi":"10.1070/RM9943","oa_version":"Preprint","citation":{"ista":"Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. 2020. Eliminating higher-multiplicity intersections, III. Codimension 2. Russian Mathematical Surveys. 75(6), 1156–1158.","chicago":"Avvakumov, Sergey, Uli Wagner, Isaac Mabillard, and A. B. Skopenkov. “Eliminating Higher-Multiplicity Intersections, III. Codimension 2.” <i>Russian Mathematical Surveys</i>. IOP Publishing, 2020. <a href=\"https://doi.org/10.1070/RM9943\">https://doi.org/10.1070/RM9943</a>.","apa":"Avvakumov, S., Wagner, U., Mabillard, I., &#38; Skopenkov, A. B. (2020). Eliminating higher-multiplicity intersections, III. Codimension 2. <i>Russian Mathematical Surveys</i>. IOP Publishing. <a href=\"https://doi.org/10.1070/RM9943\">https://doi.org/10.1070/RM9943</a>","short":"S. Avvakumov, U. Wagner, I. Mabillard, A.B. Skopenkov, Russian Mathematical Surveys 75 (2020) 1156–1158.","ieee":"S. Avvakumov, U. Wagner, I. Mabillard, and A. B. Skopenkov, “Eliminating higher-multiplicity intersections, III. Codimension 2,” <i>Russian Mathematical Surveys</i>, vol. 75, no. 6. IOP Publishing, pp. 1156–1158, 2020.","mla":"Avvakumov, Sergey, et al. “Eliminating Higher-Multiplicity Intersections, III. Codimension 2.” <i>Russian Mathematical Surveys</i>, vol. 75, no. 6, IOP Publishing, 2020, pp. 1156–58, doi:<a href=\"https://doi.org/10.1070/RM9943\">10.1070/RM9943</a>.","ama":"Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. Eliminating higher-multiplicity intersections, III. Codimension 2. <i>Russian Mathematical Surveys</i>. 2020;75(6):1156-1158. doi:<a href=\"https://doi.org/10.1070/RM9943\">10.1070/RM9943</a>"},"date_updated":"2023-08-14T11:43:54Z","type":"journal_article","date_published":"2020-12-01T00:00:00Z","month":"12","scopus_import":"1","publication":"Russian Mathematical Surveys","article_processing_charge":"No","year":"2020","language":[{"iso":"eng"}],"acknowledgement":"This research was carried out with the support of the Russian Foundation for Basic Research(grant no. 19-01-00169)","intvolume":"        75","title":"Eliminating higher-multiplicity intersections, III. Codimension 2","main_file_link":[{"url":"https://arxiv.org/abs/1511.03501","open_access":"1"}]},{"article_processing_charge":"No","publisher":"American Chemical Society","year":"2020","date_created":"2021-04-14T12:05:20Z","status":"public","department":[{"_id":"LeSa"}],"day":"20","oa":1,"main_file_link":[{"open_access":"1"}],"title":"Charge transfer and chemo-mechanical coupling in respiratory complex I","doi":"10.1021/jacs.9b13450.s002","related_material":{"record":[{"id":"8040","relation":"used_in_publication","status":"public"}]},"author":[{"full_name":"Gupta, Chitrak","first_name":"Chitrak","last_name":"Gupta"},{"last_name":"Khaniya","first_name":"Umesh","full_name":"Khaniya, Umesh"},{"last_name":"Chan","first_name":"Chun","full_name":"Chan, Chun"},{"full_name":"Dehez, Francois","first_name":"Francois","last_name":"Dehez"},{"last_name":"Shekhar","first_name":"Mrinal","full_name":"Shekhar, Mrinal"},{"full_name":"Gunner, M. R.","last_name":"Gunner","first_name":"M. R."},{"first_name":"Leonid A","orcid":"0000-0002-0977-7989","last_name":"Sazanov","id":"338D39FE-F248-11E8-B48F-1D18A9856A87","full_name":"Sazanov, Leonid A"},{"first_name":"Christophe","last_name":"Chipot","full_name":"Chipot, Christophe"},{"last_name":"Singharoy","first_name":"Abhishek","full_name":"Singharoy, Abhishek"}],"oa_version":"Published Version","_id":"9326","type":"research_data_reference","abstract":[{"text":"The mitochondrial respiratory chain, formed by five protein complexes, utilizes energy from catabolic processes to synthesize ATP. Complex I, the first and the largest protein complex of the chain, harvests electrons from NADH to reduce quinone, while pumping protons across the mitochondrial membrane. Detailed knowledge of the working principle of such coupled charge-transfer processes remains, however, fragmentary due to bottlenecks in understanding redox-driven conformational transitions and their interplay with the hydrated proton pathways. Complex I from Thermus thermophilus encases 16 subunits with nine iron–sulfur clusters, reduced by electrons from NADH. Here, employing the latest crystal structure of T. thermophilus complex I, we have used microsecond-scale molecular dynamics simulations to study the chemo-mechanical coupling between redox changes of the iron–sulfur clusters and conformational transitions across complex I. First, we identify the redox switches within complex I, which allosterically couple the dynamics of the quinone binding pocket to the site of NADH reduction. Second, our free-energy calculations reveal that the affinity of the quinone, specifically menaquinone, for the binding-site is higher than that of its reduced, menaquinol forma design essential for menaquinol release. Remarkably, the barriers to diffusive menaquinone dynamics are lesser than that of the more ubiquitous ubiquinone, and the naphthoquinone headgroup of the former furnishes stronger binding interactions with the pocket, favoring menaquinone for charge transport in T. thermophilus. Our computations are consistent with experimentally validated mutations and hierarchize the key residues into three functional classes, identifying new mutation targets. Third, long-range hydrogen-bond networks connecting the quinone-binding site to the transmembrane subunits are found to be responsible for proton pumping. Put together, the simulations reveal the molecular design principles linking redox reactions to quinone turnover to proton translocation in complex I.","lang":"eng"}],"date_updated":"2023-08-22T07:49:37Z","citation":{"ista":"Gupta C, Khaniya U, Chan C, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot C, Singharoy A. 2020. Charge transfer and chemo-mechanical coupling in respiratory complex I, American Chemical Society, <a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">10.1021/jacs.9b13450.s002</a>.","chicago":"Gupta, Chitrak, Umesh Khaniya, Chun Chan, Francois Dehez, Mrinal Shekhar, M. R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Charge Transfer and Chemo-Mechanical Coupling in Respiratory Complex I.” American Chemical Society, 2020. <a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">https://doi.org/10.1021/jacs.9b13450.s002</a>.","apa":"Gupta, C., Khaniya, U., Chan, C., Dehez, F., Shekhar, M., Gunner, M. R., … Singharoy, A. (2020). Charge transfer and chemo-mechanical coupling in respiratory complex I. American Chemical Society. <a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">https://doi.org/10.1021/jacs.9b13450.s002</a>","short":"C. Gupta, U. Khaniya, C. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (2020).","ieee":"C. Gupta <i>et al.</i>, “Charge transfer and chemo-mechanical coupling in respiratory complex I.” American Chemical Society, 2020.","mla":"Gupta, Chitrak, et al. <i>Charge Transfer and Chemo-Mechanical Coupling in Respiratory Complex I</i>. American Chemical Society, 2020, doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">10.1021/jacs.9b13450.s002</a>.","ama":"Gupta C, Khaniya U, Chan C, et al. Charge transfer and chemo-mechanical coupling in respiratory complex I. 2020. doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">10.1021/jacs.9b13450.s002</a>"},"tmp":{"name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","image":"/images/cc_by_nc.png","short":"CC BY-NC (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode"},"date_published":"2020-05-20T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"05"},{"intvolume":"       119","title":"Inducing and exploiting activation sparsity for fast neural network inference","year":"2020","language":[{"iso":"eng"}],"publication":"37th International Conference on Machine Learning, ICML 2020","article_processing_charge":"No","date_published":"2020-07-12T00:00:00Z","month":"07","ddc":["000"],"scopus_import":"1","has_accepted_license":"1","citation":{"chicago":"Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In <i>37th International Conference on Machine Learning, ICML 2020</i>, 119:5533–43, 2020.","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.","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.","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."},"date_updated":"2023-02-23T13:57:24Z","type":"conference","oa_version":"Published Version","file_date_updated":"2021-05-25T09:51:36Z","conference":{"location":"Online","end_date":"2020-07-18","name":"ICML: International Conference on Machine Learning","start_date":"2020-07-12"},"quality_controlled":"1","oa":1,"day":"12","department":[{"_id":"DaAl"}],"status":"public","date_created":"2021-05-23T22:01:45Z","file":[{"relation":"main_file","file_id":"9421","date_updated":"2021-05-25T09:51:36Z","creator":"kschuh","file_size":741899,"content_type":"application/pdf","checksum":"2aaaa7d7226e49161311d91627cf783b","date_created":"2021-05-25T09:51:36Z","success":1,"file_name":"2020_PMLR_Kurtz.pdf","access_level":"open_access"}],"page":"5533-5543","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","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","volume":119,"publication_identifier":{"issn":["2640-3498"]},"author":[{"last_name":"Kurtz","first_name":"Mark","full_name":"Kurtz, Mark"},{"full_name":"Kopinsky, Justin","first_name":"Justin","last_name":"Kopinsky"},{"first_name":"Rati","last_name":"Gelashvili","full_name":"Gelashvili, Rati"},{"full_name":"Matveev, Alexander","first_name":"Alexander","last_name":"Matveev"},{"full_name":"Carr, John","last_name":"Carr","first_name":"John"},{"full_name":"Goin, Michael","first_name":"Michael","last_name":"Goin"},{"full_name":"Leiserson, William","last_name":"Leiserson","first_name":"William"},{"full_name":"Moore, Sage","last_name":"Moore","first_name":"Sage"},{"full_name":"Nell, Bill","last_name":"Nell","first_name":"Bill"},{"full_name":"Shavit, Nir","last_name":"Shavit","first_name":"Nir"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian"}]},{"article_processing_charge":"No","publication":"Molecular Cell","extern":"1","language":[{"iso":"eng"}],"year":"2020","intvolume":"        77","title":"DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.molcel.2019.10.011"}],"doi":"10.1016/j.molcel.2019.10.011","article_type":"original","oa_version":"Published Version","citation":{"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>","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>.","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.","short":"J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77 (2020) 310–323.e7.","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>","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."},"date_updated":"2021-12-14T07:51:15Z","pmid":1,"type":"journal_article","scopus_import":"1","month":"01","date_published":"2020-01-16T00:00:00Z","publisher":"Elsevier","page":"310-323.e7","status":"public","date_created":"2021-06-08T06:37:09Z","department":[{"_id":"DaZi"}],"day":"16","issue":"2","oa":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."},{"first_name":"M. Yvonne","last_name":"Kim","full_name":"Kim, M. Yvonne"},{"full_name":"Moore, Jonathan D.","last_name":"Moore","first_name":"Jonathan D."},{"full_name":"Zilberman, Daniel","id":"6973db13-dd5f-11ea-814e-b3e5455e9ed1","last_name":"Zilberman","orcid":"0000-0002-0123-8649","first_name":"Daniel"}],"external_id":{"pmid":["31732458"]},"volume":77,"publication_identifier":{"eissn":["1097-4164"],"issn":["1097-2765"]},"_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."}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","publication_status":"published"},{"status":"public","date_created":"2021-07-04T22:01:26Z","page":"162-182","publisher":"Carleton University","file":[{"file_id":"9882","relation":"main_file","date_updated":"2021-08-11T11:55:11Z","creator":"asandaue","content_type":"application/pdf","file_size":1449234,"date_created":"2021-08-11T11:55:11Z","checksum":"f02d0b2b3838e7891a6c417fc34ffdcd","success":1,"access_level":"open_access","file_name":"2020_JournalOfComputationalGeometry_Edelsbrunner.pdf"}],"quality_controlled":"1","issue":"2","oa":1,"day":"14","department":[{"_id":"HeEd"}],"project":[{"grant_number":"I4887","name":"Discretization in Geometry and Dynamics","_id":"0aa4bc98-070f-11eb-9043-e6fff9c6a316"}],"volume":11,"publication_identifier":{"eissn":["1920180X"]},"author":[{"last_name":"Edelsbrunner","orcid":"0000-0002-9823-6833","first_name":"Herbert","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","full_name":"Edelsbrunner, Herbert"},{"full_name":"Virk, Ziga","first_name":"Ziga","last_name":"Virk","id":"2E36B656-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Wagner, Hubert","last_name":"Wagner","first_name":"Hubert","id":"379CA8B8-F248-11E8-B48F-1D18A9856A87"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","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"}],"_id":"9630","language":[{"iso":"eng"}],"year":"2020","article_processing_charge":"Yes","publication":"Journal of Computational Geometry","intvolume":"        11","title":"Topological data analysis in information space","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).","oa_version":"Published Version","file_date_updated":"2021-08-11T11:55:11Z","doi":"10.20382/jocg.v11i2a7","article_type":"original","date_published":"2020-12-14T00:00:00Z","month":"12","ddc":["510","000"],"tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","short":"CC BY (3.0)"},"scopus_import":"1","date_updated":"2021-08-11T12:26:34Z","has_accepted_license":"1","citation":{"short":"H. Edelsbrunner, Z. Virk, H. Wagner, Journal of Computational Geometry 11 (2020) 162–182.","apa":"Edelsbrunner, H., Virk, Z., &#38; Wagner, H. (2020). Topological data analysis in information space. <i>Journal of Computational Geometry</i>. Carleton University. <a href=\"https://doi.org/10.20382/jocg.v11i2a7\">https://doi.org/10.20382/jocg.v11i2a7</a>","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>.","ista":"Edelsbrunner H, Virk Z, Wagner H. 2020. Topological data analysis in information space. Journal of Computational Geometry. 11(2), 162–182.","ama":"Edelsbrunner H, Virk Z, Wagner H. Topological data analysis in information space. <i>Journal of Computational Geometry</i>. 2020;11(2):162-182. doi:<a href=\"https://doi.org/10.20382/jocg.v11i2a7\">10.20382/jocg.v11i2a7</a>","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>.","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."},"type":"journal_article"},{"language":[{"iso":"eng"}],"year":"2020","article_processing_charge":"No","publication":"Advances in Neural Information Processing Systems","title":"Scalable belief propagation via relaxed scheduling","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html"}],"intvolume":"        33","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).","oa_version":"Published Version","conference":{"location":"Vancouver, Canada","end_date":"2020-12-12","start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems"},"ec_funded":1,"month":"12","date_published":"2020-12-06T00:00:00Z","scopus_import":"1","type":"conference","citation":{"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.","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.","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.","short":"V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 22361–22372.","apa":"Aksenov, V., Alistarh, D.-A., &#38; Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 22361–22372). Vancouver, Canada: Curran Associates.","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.","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."},"date_updated":"2023-02-23T14:03:03Z","date_created":"2021-07-04T22:01:26Z","status":"public","page":"22361-22372","publisher":"Curran Associates","quality_controlled":"1","oa":1,"arxiv":1,"day":"06","department":[{"_id":"DaAl"}],"project":[{"call_identifier":"H2020","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"publication_identifier":{"issn":["10495258"],"isbn":["9781713829546"]},"volume":33,"external_id":{"arxiv":["2002.11505"]},"author":[{"full_name":"Aksenov, Vitaly","last_name":"Aksenov","first_name":"Vitaly"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","last_name":"Alistarh","first_name":"Dan-Adrian"},{"full_name":"Korhonen, Janne","first_name":"Janne","last_name":"Korhonen","id":"C5402D42-15BC-11E9-A202-CA2BE6697425"}],"publication_status":"published","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","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"},{"_id":"9632","abstract":[{"text":"Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep\r\nneural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for oneshot pruning. Further, even when iterative, gradual pruning is allowed, our method results in a gain in test accuracy over the state-of-the-art approaches, for standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as\r\nillustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher.","lang":"eng"}],"publication_status":"published","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","author":[{"id":"DD138E24-D89D-11E9-9DC0-DEF6E5697425","last_name":"Singh","first_name":"Sidak Pal","full_name":"Singh, Sidak Pal"},{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"}],"external_id":{"arxiv":["2004.14340"]},"publication_identifier":{"isbn":["9781713829546"],"issn":["10495258"]},"volume":33,"project":[{"name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","call_identifier":"H2020"}],"department":[{"_id":"DaAl"},{"_id":"ToHe"}],"day":"06","oa":1,"arxiv":1,"quality_controlled":"1","page":"18098-18109","publisher":"Curran Associates","date_created":"2021-07-04T22:01:26Z","status":"public","type":"conference","date_updated":"2023-02-23T14:03:06Z","citation":{"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.","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.","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.","short":"S.P. Singh, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 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.","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.","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."},"scopus_import":"1","month":"12","date_published":"2020-12-06T00:00:00Z","ec_funded":1,"conference":{"location":"Vancouver, Canada","end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems","start_date":"2020-12-06"},"oa_version":"Published Version","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.","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html","open_access":"1"}],"title":"WoodFisher: Efficient second-order approximation for neural network compression","intvolume":"        33","publication":"Advances in Neural Information Processing Systems","article_processing_charge":"No","year":"2020","language":[{"iso":"eng"}]},{"quality_controlled":"1","oa":1,"day":"06","department":[{"_id":"TiVo"}],"status":"public","date_created":"2021-07-04T22:01:27Z","page":"16398-16408","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","publication_status":"published","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"}],"_id":"9633","project":[{"name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","grant_number":"214316/Z/18/Z"},{"grant_number":"819603","call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"volume":33,"publication_identifier":{"issn":["1049-5258"]},"author":[{"full_name":"Confavreux, Basile J","id":"C7610134-B532-11EA-BD9F-F5753DDC885E","last_name":"Confavreux","first_name":"Basile J"},{"full_name":"Zenke, Friedemann","first_name":"Friedemann","last_name":"Zenke"},{"first_name":"Everton J.","last_name":"Agnes","full_name":"Agnes, Everton J."},{"last_name":"Lillicrap","first_name":"Timothy","full_name":"Lillicrap, Timothy"},{"full_name":"Vogels, Tim P","first_name":"Tim P","last_name":"Vogels","orcid":"0000-0003-3295-6181","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425"}],"intvolume":"        33","title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html","open_access":"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.","language":[{"iso":"eng"}],"year":"2020","article_processing_charge":"No","publication":"Advances in Neural Information Processing Systems","month":"12","date_published":"2020-12-06T00:00:00Z","scopus_import":"1","date_updated":"2023-10-18T09:20:55Z","citation":{"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.","chicago":"Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap, and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” In <i>Advances in Neural Information Processing Systems</i>, 33:16398–408, 2020.","apa":"Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., &#38; Vogels, T. P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 16398–16408). Vancouver, Canada.","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.","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.","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.","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."},"type":"conference","oa_version":"Published Version","related_material":{"record":[{"id":"14422","relation":"dissertation_contains","status":"public"}],"link":[{"url":"https://doi.org/10.1101/2020.10.24.353409","relation":"is_continued_by"}]},"conference":{"end_date":"2020-12-12","start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems","location":"Vancouver, Canada"},"ec_funded":1},{"article_processing_charge":"No","publisher":"Springer Nature","date_created":"2021-07-23T08:59:15Z","status":"public","year":"2020","day":"09","department":[{"_id":"MaRo"}],"main_file_link":[{"url":"https://doi.org/10.6084/m9.figshare.12629697.v1","open_access":"1"}],"title":"Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","oa":1,"doi":"10.6084/m9.figshare.12629697.v1","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"8133"}]},"author":[{"full_name":"Hillary, Robert F.","last_name":"Hillary","first_name":"Robert F."},{"first_name":"Daniel","last_name":"Trejo-Banos","full_name":"Trejo-Banos, Daniel"},{"last_name":"Kousathanas","first_name":"Athanasios","full_name":"Kousathanas, Athanasios"},{"last_name":"McCartney","first_name":"Daniel L.","full_name":"McCartney, Daniel L."},{"first_name":"Sarah E.","last_name":"Harris","full_name":"Harris, Sarah E."},{"first_name":"Anna J.","last_name":"Stevenson","full_name":"Stevenson, Anna J."},{"last_name":"Patxot","first_name":"Marion","full_name":"Patxot, Marion"},{"full_name":"Ojavee, Sven Erik","first_name":"Sven Erik","last_name":"Ojavee"},{"full_name":"Zhang, Qian","first_name":"Qian","last_name":"Zhang"},{"last_name":"Liewald","first_name":"David C.","full_name":"Liewald, David C."},{"full_name":"Ritchie, Craig W.","last_name":"Ritchie","first_name":"Craig W."},{"first_name":"Kathryn L.","last_name":"Evans","full_name":"Evans, Kathryn L."},{"full_name":"Tucker-Drob, Elliot M.","first_name":"Elliot M.","last_name":"Tucker-Drob"},{"full_name":"Wray, Naomi R.","first_name":"Naomi R.","last_name":"Wray"},{"last_name":"McRae","first_name":"Allan F. ","full_name":"McRae, Allan F. "},{"full_name":"Visscher, Peter M.","last_name":"Visscher","first_name":"Peter M."},{"full_name":"Deary, Ian J.","first_name":"Ian J.","last_name":"Deary"},{"full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson"},{"full_name":"Marioni, Riccardo E. ","first_name":"Riccardo E. ","last_name":"Marioni"}],"oa_version":"Published Version","other_data_license":"CC0 + CC BY (4.0)","type":"research_data_reference","abstract":[{"lang":"eng","text":"Additional file 2: Supplementary Tables. The association of pre-adjusted protein levels with biological and technical covariates. Protein levels were adjusted for age, sex, array plate and four genetic principal components (population structure) prior to analyses. Significant associations are emboldened. (Table S1). pQTLs associated with inflammatory biomarker levels from Bayesian penalised regression model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary least squares GWAS and EWAS performed on inflammatory protein levels (n = 70) in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary least squares and Bayesian penalised regression models for concordantly identified SNPs. (Table S6). Estimate of heritability for blood protein levels as well as proportion of variance explained attributable to different prior mixtures. (Table S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood) and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant SNPs identified by linear model and Bayesian penalised regression and whether they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression are putatively associated with circulating inflammatory proteins that harbour pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table S12). CpGs associated with inflammatory protein biomarkers as identified by linear model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10. (Table S14). Estimate of variance explained for blood protein levels by DNA methylation as well as proportion of explained attributable to different prior mixtures - BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker levels explained by common genetic and methylation data (joint and conditional estimates from BayesR+). Ordered by combined variance explained by genetic and epigenetic data - smallest to largest. Significant results from t-tests comparing distributions for variance explained by methylation or genetics alone versus combined estimate are emboldened. (Table S17). Genetic and epigenetic factors identified by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian Randomisation analyses to assess whether proteins with concordantly identified genetic signals are causally associated with Alzheimer’s disease risk. (Table S19)."}],"date_updated":"2023-08-22T07:55:36Z","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>","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).","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>.","chicago":"Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” Springer Nature, 2020. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>.","ama":"Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. 2020. doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>","ieee":"R. F. Hillary <i>et al.</i>, “Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults.” Springer Nature, 2020.","mla":"Hillary, Robert F., et al. <i>Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults</i>. Springer Nature, 2020, doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>."},"has_accepted_license":"1","_id":"9706","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_published":"2020-07-09T00:00:00Z","month":"07","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"}},{"oa":1,"title":"Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'","main_file_link":[{"url":"https://doi.org/10.17863/CAM.50169","open_access":"1"}],"department":[{"_id":"KiMo"}],"day":"29","year":"2020","date_created":"2021-07-23T10:00:35Z","status":"public","article_processing_charge":"No","publisher":"Apollo - University of Cambridge","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2020-05-29T00:00:00Z","month":"05","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","_id":"9708","type":"research_data_reference","date_updated":"2023-08-21T07:06:48Z","abstract":[{"lang":"eng","text":"This research data supports 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'. A Readme file for plotting each figure is provided."}],"citation":{"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>.","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).","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>.","ieee":"M. Hartstein <i>et al.</i>, “Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.’” Apollo - University of Cambridge, 2020.","ama":"Hartstein M, Hsu Y-T, Modic KA, et al. Accompanying dataset for “Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.” 2020. doi:<a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>"},"has_accepted_license":"1","oa_version":"Published Version","doi":"10.17863/cam.50169","related_material":{"record":[{"relation":"used_in_publication","id":"7942","status":"public"}]},"author":[{"full_name":"Hartstein, Mate","last_name":"Hartstein","first_name":"Mate"},{"last_name":"Hsu","first_name":"Yu-Te","full_name":"Hsu, Yu-Te"},{"full_name":"Modic, Kimberly A","id":"13C26AC0-EB69-11E9-87C6-5F3BE6697425","orcid":"0000-0001-9760-3147","last_name":"Modic","first_name":"Kimberly A"},{"last_name":"Porras","first_name":"Juan","full_name":"Porras, Juan"},{"first_name":"Toshinao","last_name":"Loew","full_name":"Loew, Toshinao"},{"first_name":"Matthieu","last_name":"Le Tacon","full_name":"Le Tacon, Matthieu"},{"last_name":"Zuo","first_name":"Huakun","full_name":"Zuo, Huakun"},{"full_name":"Wang, Jinhua","last_name":"Wang","first_name":"Jinhua"},{"full_name":"Zhu, Zengwei","first_name":"Zengwei","last_name":"Zhu"},{"full_name":"Chan, Mun","first_name":"Mun","last_name":"Chan"},{"first_name":"Ross","last_name":"McDonald","full_name":"McDonald, Ross"},{"full_name":"Lonzarich, Gilbert","first_name":"Gilbert","last_name":"Lonzarich"},{"full_name":"Keimer, Bernhard","first_name":"Bernhard","last_name":"Keimer"},{"full_name":"Sebastian, Suchitra","last_name":"Sebastian","first_name":"Suchitra"},{"first_name":"Neil","last_name":"Harrison","full_name":"Harrison, Neil"}]},{"title":"Supporting information","day":"20","department":[{"_id":"LeSa"}],"status":"public","date_created":"2021-07-23T12:02:39Z","year":"2020","publisher":"American Chemical Society ","article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","month":"05","date_published":"2020-05-20T00:00:00Z","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.","mla":"Gupta, Chitrak, et al. <i>Supporting Information</i>. American Chemical Society , 2020, doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>.","apa":"Gupta, C., Khaniya, U., Chan, C. K., Dehez, F., Shekhar, M., Gunner, M. R., … Singharoy, A. (2020). Supporting information. American Chemical Society . <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">https://doi.org/10.1021/jacs.9b13450.s001</a>","short":"C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (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>.","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>."},"abstract":[{"text":"Additional analyses of the trajectories","lang":"eng"}],"date_updated":"2023-08-22T07:49:38Z","type":"research_data_reference","_id":"9713","oa_version":"Published Version","author":[{"full_name":"Gupta, Chitrak","first_name":"Chitrak","last_name":"Gupta"},{"last_name":"Khaniya","first_name":"Umesh","full_name":"Khaniya, Umesh"},{"last_name":"Chan","first_name":"Chun Kit","full_name":"Chan, Chun Kit"},{"full_name":"Dehez, Francois","last_name":"Dehez","first_name":"Francois"},{"full_name":"Shekhar, Mrinal","first_name":"Mrinal","last_name":"Shekhar"},{"full_name":"Gunner, M.R.","last_name":"Gunner","first_name":"M.R."},{"full_name":"Sazanov, Leonid A","id":"338D39FE-F248-11E8-B48F-1D18A9856A87","first_name":"Leonid A","orcid":"0000-0002-0977-7989","last_name":"Sazanov"},{"full_name":"Chipot, Christophe","first_name":"Christophe","last_name":"Chipot"},{"last_name":"Singharoy","first_name":"Abhishek","full_name":"Singharoy, Abhishek"}],"related_material":{"record":[{"relation":"used_in_publication","id":"8040","status":"public"}]},"doi":"10.1021/jacs.9b13450.s001"},{"year":"2020","language":[{"iso":"eng"}],"acknowledged_ssus":[{"_id":"Bio"},{"_id":"EM-Fac"},{"_id":"SSU"}],"status":"public","date_created":"2021-07-29T11:29:50Z","page":"41","publisher":"Cold Spring Harbor Laboratory","article_processing_charge":"No","publication":"bioRxiv","oa":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2020.11.20.391284"}],"title":"Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion","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.","department":[{"_id":"CaHe"},{"_id":"EM-Fac"},{"_id":"Bio"}],"day":"20","oa_version":"Preprint","project":[{"call_identifier":"FP7","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme"},{"_id":"260F1432-B435-11E9-9278-68D0E5697425","name":"Interaction and feedback between cell mechanics and fate specification in vertebrate gastrulation","call_identifier":"H2020","grant_number":"742573"},{"_id":"2521E28E-B435-11E9-9278-68D0E5697425","name":"Modulation of adhesion function in cell-cell contact formation by cortical tension","grant_number":"187-2013"}],"ec_funded":1,"author":[{"full_name":"Slovakova, Jana","id":"30F3F2F0-F248-11E8-B48F-1D18A9856A87","last_name":"Slovakova","first_name":"Jana"},{"full_name":"Sikora, Mateusz K","last_name":"Sikora","first_name":"Mateusz K","id":"2F74BCDE-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Caballero Mancebo, Silvia","id":"2F1E1758-F248-11E8-B48F-1D18A9856A87","last_name":"Caballero Mancebo","orcid":"0000-0002-5223-3346","first_name":"Silvia"},{"full_name":"Krens, Gabriel","first_name":"Gabriel","orcid":"0000-0003-4761-5996","last_name":"Krens","id":"2B819732-F248-11E8-B48F-1D18A9856A87"},{"id":"3F99E422-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9735-5315","last_name":"Kaufmann","first_name":"Walter","full_name":"Kaufmann, Walter"},{"last_name":"Huljev","first_name":"Karla","id":"44C6F6A6-F248-11E8-B48F-1D18A9856A87","full_name":"Huljev, Karla"},{"full_name":"Heisenberg, Carl-Philipp J","id":"39427864-F248-11E8-B48F-1D18A9856A87","first_name":"Carl-Philipp J","last_name":"Heisenberg","orcid":"0000-0002-0912-4566"}],"related_material":{"record":[{"relation":"later_version","id":"10766","status":"public"},{"status":"public","id":"9623","relation":"dissertation_contains"}]},"doi":"10.1101/2020.11.20.391284","month":"11","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_published":"2020-11-20T00:00:00Z","publication_status":"published","_id":"9750","citation":{"ama":"Slovakova J, Sikora MK, Caballero Mancebo S, et al. Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. 2020. doi:<a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>","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>.","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.","short":"J. Slovakova, M.K. Sikora, S. Caballero Mancebo, G. Krens, W. Kaufmann, K. Huljev, C.-P.J. Heisenberg, BioRxiv (2020).","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>","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>.","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>."},"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."}],"date_updated":"2024-03-25T23:30:10Z","type":"preprint"}]