--- _id: '9198' 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." 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. article_processing_charge: No arxiv: 1 author: - first_name: Alexander full_name: Shevchenko, Alexander last_name: Shevchenko - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: ama: 'Shevchenko A, Mondelli M. Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:8773-8784.' apa: Shevchenko, A., & Mondelli, M. (2020). Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 8773–8784). ML Research Press. chicago: Shevchenko, Alexander, and Marco Mondelli. “Landscape Connectivity and Dropout Stability of SGD Solutions for Over-Parameterized Neural Networks.” In Proceedings of the 37th International Conference on Machine Learning, 119:8773–84. ML Research Press, 2020. ieee: A. Shevchenko and M. Mondelli, “Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks,” in Proceedings of the 37th International Conference on Machine Learning, 2020, vol. 119, pp. 8773–8784. 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. mla: Shevchenko, Alexander, and Marco Mondelli. “Landscape Connectivity and Dropout Stability of SGD Solutions for Over-Parameterized Neural Networks.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 8773–84. short: A. Shevchenko, M. Mondelli, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 8773–8784. date_created: 2021-02-25T09:36:22Z date_published: 2020-07-13T00:00:00Z date_updated: 2024-09-10T13:03:19Z day: '13' ddc: - '000' department: - _id: MaMo external_id: arxiv: - '1912.10095' file: - access_level: open_access checksum: f042c8d4316bd87c6361aa76f1fbdbbe content_type: application/pdf creator: dernst date_created: 2021-03-02T15:38:14Z date_updated: 2021-03-02T15:38:14Z file_id: '9217' file_name: 2020_PMLR_Shevchenko.pdf file_size: 5336380 relation: main_file success: 1 file_date_updated: 2021-03-02T15:38:14Z has_accepted_license: '1' intvolume: ' 119' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 8773-8784 project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: Proceedings of the 37th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 119 year: '2020' ... --- _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. 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. article_processing_charge: No author: - first_name: Miriam full_name: Garcia Soto, Miriam id: 4B3207F6-F248-11E8-B48F-1D18A9856A87 last_name: Garcia Soto orcid: 0000-0003-2936-5719 - first_name: Pavithra full_name: Prabhakar, Pavithra last_name: Prabhakar citation: ama: 'Garcia Soto M, Prabhakar P. Hybridization for stability verification of nonlinear switched systems. In: 2020 IEEE Real-Time Systems Symposium. IEEE; 2020:244-256. doi:10.1109/RTSS49844.2020.00031' apa: 'Garcia Soto, M., & Prabhakar, P. (2020). Hybridization for stability verification of nonlinear switched systems. In 2020 IEEE Real-Time Systems Symposium (pp. 244–256). Houston, TX, USA : IEEE. https://doi.org/10.1109/RTSS49844.2020.00031' chicago: Garcia Soto, Miriam, and Pavithra Prabhakar. “Hybridization for Stability Verification of Nonlinear Switched Systems.” In 2020 IEEE Real-Time Systems Symposium, 244–56. IEEE, 2020. https://doi.org/10.1109/RTSS49844.2020.00031. ieee: M. Garcia Soto and P. Prabhakar, “Hybridization for stability verification of nonlinear switched systems,” in 2020 IEEE Real-Time Systems Symposium, Houston, TX, USA , 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.' mla: Garcia Soto, Miriam, and Pavithra Prabhakar. “Hybridization for Stability Verification of Nonlinear Switched Systems.” 2020 IEEE Real-Time Systems Symposium, IEEE, 2020, pp. 244–56, doi:10.1109/RTSS49844.2020.00031. short: M. Garcia Soto, P. Prabhakar, in:, 2020 IEEE Real-Time Systems Symposium, IEEE, 2020, pp. 244–256. conference: end_date: 2020-12-04 location: 'Houston, TX, USA ' name: 'RTTS: Real-Time Systems Symposium' start_date: 2020-12-01 date_created: 2021-02-26T16:38:24Z date_published: 2020-12-01T00:00:00Z date_updated: 2024-02-22T13:25:19Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.1109/RTSS49844.2020.00031 external_id: isi: - '000680435100021' file: - access_level: open_access checksum: 8f97f229316c3b3a6f0cf99297aa0941 content_type: application/pdf creator: mgarcias date_created: 2021-02-26T16:38:14Z date_updated: 2021-02-26T16:38:14Z file_id: '9203' file_name: main.pdf file_size: 1125794 relation: main_file file_date_updated: 2021-02-26T16:38:14Z has_accepted_license: '1' isi: 1 language: - iso: eng month: '12' oa: 1 oa_version: Submitted Version page: 244-256 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: 2020 IEEE Real-Time Systems Symposium publication_identifier: eisbn: - '9781728183244' eissn: - 2576-3172 publication_status: published publisher: IEEE quality_controlled: '1' status: public title: Hybridization for stability verification of nonlinear switched systems type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '9208' 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.' 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).' article_number: '1505' article_processing_charge: No article_type: original author: - first_name: Francesco full_name: Laccone, Francesco last_name: Laccone - first_name: Luigi full_name: Malomo, Luigi last_name: Malomo - first_name: Jesus full_name: Perez Rodriguez, Jesus id: 2DC83906-F248-11E8-B48F-1D18A9856A87 last_name: Perez Rodriguez - first_name: Nico full_name: Pietroni, Nico last_name: Pietroni - first_name: Federico full_name: Ponchio, Federico last_name: Ponchio - first_name: Bernd full_name: Bickel, Bernd id: 49876194-F248-11E8-B48F-1D18A9856A87 last_name: Bickel orcid: 0000-0001-6511-9385 - first_name: Paolo full_name: Cignoni, Paolo last_name: Cignoni citation: 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. SN Applied Sciences. 2020;2(9). doi:10.1007/s42452-020-03305-w' apa: '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. Springer Nature. https://doi.org/10.1007/s42452-020-03305-w' 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.” SN Applied Sciences. Springer Nature, 2020. https://doi.org/10.1007/s42452-020-03305-w.' ieee: 'F. Laccone et al., “A bending-active twisted-arch plywood structure: Computational design and fabrication of the FlexMaps Pavilion,” SN Applied Sciences, vol. 2, no. 9. Springer Nature, 2020.' 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.' mla: 'Laccone, Francesco, et al. “A Bending-Active Twisted-Arch Plywood Structure: Computational Design and Fabrication of the FlexMaps Pavilion.” SN Applied Sciences, vol. 2, no. 9, 1505, Springer Nature, 2020, doi:10.1007/s42452-020-03305-w.' short: F. Laccone, L. Malomo, J. Perez Rodriguez, N. Pietroni, F. Ponchio, B. Bickel, P. Cignoni, SN Applied Sciences 2 (2020). date_created: 2021-02-28T23:01:25Z date_published: 2020-09-01T00:00:00Z date_updated: 2021-03-03T09:43:14Z day: '01' department: - _id: BeBi doi: 10.1007/s42452-020-03305-w intvolume: ' 2' issue: '9' language: - iso: eng month: '09' oa_version: None publication: SN Applied Sciences publication_identifier: eissn: - '25233971' publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: 'A bending-active twisted-arch plywood structure: Computational design and fabrication of the FlexMaps Pavilion' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2 year: '2020' ... --- _id: '9221' abstract: - lang: eng 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" 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. article_processing_charge: No arxiv: 1 author: - first_name: Quynh full_name: Nguyen, Quynh last_name: Nguyen - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: ama: 'Nguyen Q, Mondelli M. Global convergence of deep networks with one wide layer followed by pyramidal topology. In: 34th Conference on Neural Information Processing Systems. Vol 33. Curran Associates; 2020:11961–11972.' apa: 'Nguyen, Q., & Mondelli, M. (2020). Global convergence of deep networks with one wide layer followed by pyramidal topology. In 34th Conference on Neural Information Processing Systems (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 34th Conference on Neural Information Processing Systems, 33:11961–11972. Curran Associates, 2020. ieee: Q. Nguyen and M. Mondelli, “Global convergence of deep networks with one wide layer followed by pyramidal topology,” in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 11961–11972. 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.' mla: Nguyen, Quynh, and Marco Mondelli. “Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology.” 34th Conference on Neural Information Processing Systems, vol. 33, Curran Associates, 2020, pp. 11961–11972. short: Q. Nguyen, M. Mondelli, in:, 34th Conference on Neural Information Processing Systems, Curran Associates, 2020, pp. 11961–11972. conference: end_date: 2020-12-12 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2020-12-06 date_created: 2021-03-03T12:06:02Z date_published: 2020-07-07T00:00:00Z date_updated: 2024-09-10T13:03:17Z day: '07' department: - _id: MaMo external_id: arxiv: - '2002.07867' intvolume: ' 33' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2002.07867 month: '07' oa: 1 oa_version: Preprint page: 11961–11972 project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: 34th Conference on Neural Information Processing Systems publication_status: published publisher: Curran Associates quality_controlled: '1' status: public title: Global convergence of deep networks with one wide layer followed by pyramidal topology type: conference user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 volume: 33 year: '2020' ... --- _id: '9222' article_processing_charge: No author: - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X citation: ama: 'Katsaros G. Transport data for: Site‐controlled uniform Ge/Si Hut wires with electrically tunable spin–orbit coupling. 2020. doi:10.15479/AT:ISTA:9222' 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. https://doi.org/10.15479/AT:ISTA:9222' 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. https://doi.org/10.15479/AT:ISTA:9222.' 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.' 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, 10.15479/AT:ISTA:9222.' mla: '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, doi:10.15479/AT:ISTA:9222.' short: G. Katsaros, (2020). contributor: - contributor_type: research_group first_name: Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros date_created: 2021-03-05T18:00:47Z date_published: 2020-03-16T00:00:00Z date_updated: 2024-02-21T12:42:13Z day: '16' ddc: - '530' department: - _id: GeKa doi: 10.15479/AT:ISTA:9222 file: - access_level: open_access checksum: 41b66e195ed3dbd73077feee77b05652 content_type: application/x-zip-compressed creator: gkatsaro date_created: 2021-03-05T17:50:45Z date_updated: 2021-03-05T17:50:45Z file_id: '9223' file_name: DOI_SiteControlledHWs.zip file_size: 13317557 relation: main_file - access_level: open_access checksum: a1dc5f710ba4b3bb7f248195ba754ab2 content_type: text/plain creator: dernst date_created: 2021-03-10T07:31:50Z date_updated: 2021-03-10T07:31:50Z file_id: '9233' file_name: Readme.txt file_size: 3515 relation: main_file success: 1 file_date_updated: 2021-03-10T07:31:50Z has_accepted_license: '1' license: https://creativecommons.org/publicdomain/zero/1.0/ month: '03' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria related_material: record: - id: '7541' relation: used_in_publication status: public status: public title: 'Transport data for: Site‐controlled uniform Ge/Si Hut wires with electrically tunable spin–orbit coupling' tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '9249' abstract: - lang: eng 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. 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. " article_processing_charge: No article_type: original author: - first_name: Ranita full_name: Biswas, Ranita id: 3C2B033E-F248-11E8-B48F-1D18A9856A87 last_name: Biswas orcid: 0000-0002-5372-7890 - first_name: Gaëlle full_name: Largeteau-Skapin, Gaëlle last_name: Largeteau-Skapin - first_name: Rita full_name: Zrour, Rita last_name: Zrour - first_name: Eric full_name: Andres, Eric last_name: Andres citation: ama: Biswas R, Largeteau-Skapin G, Zrour R, Andres E. Digital objects in rhombic dodecahedron grid. Mathematical Morphology - Theory and Applications. 2020;4(1):143-158. doi:10.1515/mathm-2020-0106 apa: Biswas, R., Largeteau-Skapin, G., Zrour, R., & Andres, E. (2020). Digital objects in rhombic dodecahedron grid. Mathematical Morphology - Theory and Applications. De Gruyter. https://doi.org/10.1515/mathm-2020-0106 chicago: Biswas, Ranita, Gaëlle Largeteau-Skapin, Rita Zrour, and Eric Andres. “Digital Objects in Rhombic Dodecahedron Grid.” Mathematical Morphology - Theory and Applications. De Gruyter, 2020. https://doi.org/10.1515/mathm-2020-0106. ieee: R. Biswas, G. Largeteau-Skapin, R. Zrour, and E. Andres, “Digital objects in rhombic dodecahedron grid,” Mathematical Morphology - Theory and Applications, vol. 4, no. 1. De Gruyter, pp. 143–158, 2020. 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. mla: Biswas, Ranita, et al. “Digital Objects in Rhombic Dodecahedron Grid.” Mathematical Morphology - Theory and Applications, vol. 4, no. 1, De Gruyter, 2020, pp. 143–58, doi:10.1515/mathm-2020-0106. short: R. Biswas, G. Largeteau-Skapin, R. Zrour, E. Andres, Mathematical Morphology - Theory and Applications 4 (2020) 143–158. date_created: 2021-03-16T08:55:19Z date_published: 2020-11-17T00:00:00Z date_updated: 2021-03-22T09:01:50Z day: '17' ddc: - '510' department: - _id: HeEd doi: 10.1515/mathm-2020-0106 ec_funded: 1 file: - access_level: open_access checksum: 4a1043fa0548a725d464017fe2483ce0 content_type: application/pdf creator: dernst date_created: 2021-03-22T08:56:37Z date_updated: 2021-03-22T08:56:37Z file_id: '9272' file_name: 2020_MathMorpholTheoryAppl_Biswas.pdf file_size: 3668725 relation: main_file success: 1 file_date_updated: 2021-03-22T08:56:37Z has_accepted_license: '1' intvolume: ' 4' issue: '1' language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 143-158 project: - _id: 266A2E9E-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '788183' name: Alpha Shape Theory Extended - _id: 2561EBF4-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: I02979-N35 name: Persistence and stability of geometric complexes publication: Mathematical Morphology - Theory and Applications publication_identifier: issn: - 2353-3390 publication_status: published publisher: De Gruyter quality_controlled: '1' status: public title: Digital objects in rhombic dodecahedron grid tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 4 year: '2020' ... --- _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⟶∞ . 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. article_processing_charge: No arxiv: 1 author: - first_name: János full_name: Pach, János id: E62E3130-B088-11EA-B919-BF823C25FEA4 last_name: Pach - first_name: Gábor full_name: Tardos, Gábor last_name: Tardos - first_name: Géza full_name: Tóth, Géza last_name: Tóth citation: ama: 'Pach J, Tardos G, Tóth G. Crossings between non-homotopic edges. In: 28th International Symposium on Graph Drawing and Network Visualization. Vol 12590. LNCS. Springer Nature; 2020:359-371. doi:10.1007/978-3-030-68766-3_28' apa: 'Pach, J., Tardos, G., & Tóth, G. (2020). Crossings between non-homotopic edges. In 28th International Symposium on Graph Drawing and Network Visualization (Vol. 12590, pp. 359–371). Virtual, Online: Springer Nature. https://doi.org/10.1007/978-3-030-68766-3_28' chicago: Pach, János, Gábor Tardos, and Géza Tóth. “Crossings between Non-Homotopic Edges.” In 28th International Symposium on Graph Drawing and Network Visualization, 12590:359–71. LNCS. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-68766-3_28. ieee: J. Pach, G. Tardos, and G. Tóth, “Crossings between non-homotopic edges,” in 28th International Symposium on Graph Drawing and Network Visualization, Virtual, Online, 2020, vol. 12590, pp. 359–371. 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.' mla: Pach, János, et al. “Crossings between Non-Homotopic Edges.” 28th International Symposium on Graph Drawing and Network Visualization, vol. 12590, Springer Nature, 2020, pp. 359–71, doi:10.1007/978-3-030-68766-3_28. short: J. Pach, G. Tardos, G. Tóth, in:, 28th International Symposium on Graph Drawing and Network Visualization, Springer Nature, 2020, pp. 359–371. conference: end_date: 2020-09-18 location: Virtual, Online name: 'GD: Graph Drawing and Network Visualization' start_date: 2020-09-16 date_created: 2021-03-28T22:01:44Z date_published: 2020-09-20T00:00:00Z date_updated: 2021-04-06T11:32:32Z day: '20' department: - _id: HeEd doi: 10.1007/978-3-030-68766-3_28 external_id: arxiv: - '2006.14908' intvolume: ' 12590' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2006.14908 month: '09' oa: 1 oa_version: Preprint page: 359-371 project: - _id: 268116B8-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z00342 name: The Wittgenstein Prize publication: 28th International Symposium on Graph Drawing and Network Visualization publication_identifier: eissn: - 1611-3349 isbn: - '9783030687656' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' series_title: LNCS status: public title: Crossings between non-homotopic edges type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 12590 year: '2020' ... --- _id: '9308' acknowledgement: This research was carried out with the support of the Russian Foundation for Basic Research(grant no. 19-01-00169) article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Sergey full_name: Avvakumov, Sergey id: 3827DAC8-F248-11E8-B48F-1D18A9856A87 last_name: Avvakumov - first_name: Uli full_name: Wagner, Uli id: 36690CA2-F248-11E8-B48F-1D18A9856A87 last_name: Wagner orcid: 0000-0002-1494-0568 - first_name: Isaac full_name: Mabillard, Isaac id: 32BF9DAA-F248-11E8-B48F-1D18A9856A87 last_name: Mabillard - first_name: A. B. full_name: Skopenkov, A. B. last_name: Skopenkov citation: ama: Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. Eliminating higher-multiplicity intersections, III. Codimension 2. Russian Mathematical Surveys. 2020;75(6):1156-1158. doi:10.1070/RM9943 apa: Avvakumov, S., Wagner, U., Mabillard, I., & Skopenkov, A. B. (2020). Eliminating higher-multiplicity intersections, III. Codimension 2. Russian Mathematical Surveys. IOP Publishing. https://doi.org/10.1070/RM9943 chicago: Avvakumov, Sergey, Uli Wagner, Isaac Mabillard, and A. B. Skopenkov. “Eliminating Higher-Multiplicity Intersections, III. Codimension 2.” Russian Mathematical Surveys. IOP Publishing, 2020. https://doi.org/10.1070/RM9943. ieee: S. Avvakumov, U. Wagner, I. Mabillard, and A. B. Skopenkov, “Eliminating higher-multiplicity intersections, III. Codimension 2,” Russian Mathematical Surveys, vol. 75, no. 6. IOP Publishing, pp. 1156–1158, 2020. ista: Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. 2020. Eliminating higher-multiplicity intersections, III. Codimension 2. Russian Mathematical Surveys. 75(6), 1156–1158. mla: Avvakumov, Sergey, et al. “Eliminating Higher-Multiplicity Intersections, III. Codimension 2.” Russian Mathematical Surveys, vol. 75, no. 6, IOP Publishing, 2020, pp. 1156–58, doi:10.1070/RM9943. short: S. Avvakumov, U. Wagner, I. Mabillard, A.B. Skopenkov, Russian Mathematical Surveys 75 (2020) 1156–1158. date_created: 2021-04-04T22:01:22Z date_published: 2020-12-01T00:00:00Z date_updated: 2023-08-14T11:43:54Z day: '01' department: - _id: UlWa doi: 10.1070/RM9943 external_id: arxiv: - '1511.03501' isi: - '000625983100001' intvolume: ' 75' isi: 1 issue: '6' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1511.03501 month: '12' oa: 1 oa_version: Preprint page: 1156-1158 publication: Russian Mathematical Surveys publication_identifier: issn: - 0036-0279 publication_status: published publisher: IOP Publishing quality_controlled: '1' related_material: record: - id: '8183' relation: earlier_version status: public - id: '10220' relation: later_version status: public scopus_import: '1' status: public title: Eliminating higher-multiplicity intersections, III. Codimension 2 type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 75 year: '2020' ... --- _id: '9326' abstract: - lang: eng 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. article_processing_charge: No author: - first_name: Chitrak full_name: Gupta, Chitrak last_name: Gupta - first_name: Umesh full_name: Khaniya, Umesh last_name: Khaniya - first_name: Chun full_name: Chan, Chun last_name: Chan - first_name: Francois full_name: Dehez, Francois last_name: Dehez - first_name: Mrinal full_name: Shekhar, Mrinal last_name: Shekhar - first_name: M. R. full_name: Gunner, M. R. last_name: Gunner - first_name: Leonid A full_name: Sazanov, Leonid A id: 338D39FE-F248-11E8-B48F-1D18A9856A87 last_name: Sazanov orcid: 0000-0002-0977-7989 - first_name: Christophe full_name: Chipot, Christophe last_name: Chipot - first_name: Abhishek full_name: Singharoy, Abhishek last_name: Singharoy citation: ama: Gupta C, Khaniya U, Chan C, et al. Charge transfer and chemo-mechanical coupling in respiratory complex I. 2020. doi:10.1021/jacs.9b13450.s002 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. https://doi.org/10.1021/jacs.9b13450.s002 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. https://doi.org/10.1021/jacs.9b13450.s002. ieee: C. Gupta et al., “Charge transfer and chemo-mechanical coupling in respiratory complex I.” American Chemical Society, 2020. 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, 10.1021/jacs.9b13450.s002. mla: Gupta, Chitrak, et al. Charge Transfer and Chemo-Mechanical Coupling in Respiratory Complex I. American Chemical Society, 2020, doi:10.1021/jacs.9b13450.s002. short: C. Gupta, U. Khaniya, C. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (2020). date_created: 2021-04-14T12:05:20Z date_published: 2020-05-20T00:00:00Z date_updated: 2023-08-22T07:49:37Z day: '20' department: - _id: LeSa doi: 10.1021/jacs.9b13450.s002 license: https://creativecommons.org/licenses/by-nc/4.0/ main_file_link: - open_access: '1' month: '05' oa: 1 oa_version: Published Version publisher: American Chemical Society related_material: record: - id: '8040' relation: used_in_publication status: public status: public title: Charge transfer and chemo-mechanical coupling in respiratory complex I tmp: image: /images/cc_by_nc.png legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) short: CC BY-NC (4.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '9415' abstract: - lang: eng 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. ' article_processing_charge: No author: - first_name: Mark full_name: Kurtz, Mark last_name: Kurtz - first_name: Justin full_name: Kopinsky, Justin last_name: Kopinsky - first_name: Rati full_name: Gelashvili, Rati last_name: Gelashvili - first_name: Alexander full_name: Matveev, Alexander last_name: Matveev - first_name: John full_name: Carr, John last_name: Carr - first_name: Michael full_name: Goin, Michael last_name: Goin - first_name: William full_name: Leiserson, William last_name: Leiserson - first_name: Sage full_name: Moore, Sage last_name: Moore - first_name: Bill full_name: Nell, Bill last_name: Nell - first_name: Nir full_name: Shavit, Nir last_name: Shavit - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: 37th International Conference on Machine Learning, ICML 2020. Vol 119. ; 2020:5533-5543.' 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 37th International Conference on Machine Learning, ICML 2020 (Vol. 119, pp. 5533–5543). Online. 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 37th International Conference on Machine Learning, ICML 2020, 119:5533–43, 2020. ieee: M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543. ista: 'Kurtz M, Kopinsky J, Gelashvili R, Matveev A, Carr J, Goin M, Leiserson W, Moore S, Nell B, Shavit N, Alistarh D-A. 2020. Inducing and exploiting activation sparsity for fast neural network inference. 37th International Conference on Machine Learning, ICML 2020. ICML: International Conference on Machine Learning vol. 119, 5533–5543.' mla: Kurtz, Mark, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” 37th International Conference on Machine Learning, ICML 2020, vol. 119, 2020, pp. 5533–43. 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. conference: end_date: 2020-07-18 location: Online name: 'ICML: International Conference on Machine Learning' start_date: 2020-07-12 date_created: 2021-05-23T22:01:45Z date_published: 2020-07-12T00:00:00Z date_updated: 2023-02-23T13:57:24Z day: '12' ddc: - '000' department: - _id: DaAl file: - access_level: open_access checksum: 2aaaa7d7226e49161311d91627cf783b content_type: application/pdf creator: kschuh date_created: 2021-05-25T09:51:36Z date_updated: 2021-05-25T09:51:36Z file_id: '9421' file_name: 2020_PMLR_Kurtz.pdf file_size: 741899 relation: main_file success: 1 file_date_updated: 2021-05-25T09:51:36Z has_accepted_license: '1' intvolume: ' 119' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 5533-5543 publication: 37th International Conference on Machine Learning, ICML 2020 publication_identifier: issn: - 2640-3498 quality_controlled: '1' scopus_import: '1' status: public title: Inducing and exploiting activation sparsity for fast neural network inference type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 119 year: '2020' ... --- _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. article_processing_charge: No article_type: original author: - first_name: Jaemyung full_name: Choi, Jaemyung last_name: Choi - first_name: David B. full_name: Lyons, David B. last_name: Lyons - first_name: M. Yvonne full_name: Kim, M. Yvonne last_name: Kim - first_name: Jonathan D. full_name: Moore, Jonathan D. last_name: Moore - first_name: Daniel full_name: Zilberman, Daniel id: 6973db13-dd5f-11ea-814e-b3e5455e9ed1 last_name: Zilberman orcid: 0000-0002-0123-8649 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. Molecular Cell. 2020;77(2):310-323.e7. doi:10.1016/j.molcel.2019.10.011 apa: Choi, J., Lyons, D. B., Kim, M. Y., Moore, J. D., & Zilberman, D. (2020). DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. Molecular Cell. Elsevier. https://doi.org/10.1016/j.molcel.2019.10.011 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.” Molecular Cell. Elsevier, 2020. https://doi.org/10.1016/j.molcel.2019.10.011. 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,” Molecular Cell, vol. 77, no. 2. Elsevier, p. 310–323.e7, 2020. ista: Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. 2020. DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. Molecular Cell. 77(2), 310–323.e7. mla: Choi, Jaemyung, et al. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements and Aberrant Intragenic Transcripts.” Molecular Cell, vol. 77, no. 2, Elsevier, 2020, p. 310–323.e7, doi:10.1016/j.molcel.2019.10.011. short: J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77 (2020) 310–323.e7. date_created: 2021-06-08T06:37:09Z date_published: 2020-01-16T00:00:00Z date_updated: 2021-12-14T07:51:15Z day: '16' department: - _id: DaZi doi: 10.1016/j.molcel.2019.10.011 extern: '1' external_id: pmid: - '31732458' intvolume: ' 77' issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1016/j.molcel.2019.10.011 month: '01' oa: 1 oa_version: Published Version page: 310-323.e7 pmid: 1 publication: Molecular Cell publication_identifier: eissn: - 1097-4164 issn: - 1097-2765 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts type: journal_article user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 volume: 77 year: '2020' ... --- _id: '9630' abstract: - lang: eng text: Various kinds of data are routinely represented as discrete probability distributions. Examples include text documents summarized by histograms of word occurrences and images represented as histograms of oriented gradients. Viewing a discrete probability distribution as a point in the standard simplex of the appropriate dimension, we can understand collections of such objects in geometric and topological terms. Importantly, instead of using the standard Euclidean distance, we look into dissimilarity measures with information-theoretic justification, and we develop the theory needed for applying topological data analysis in this setting. In doing so, we emphasize constructions that enable the usage of existing computational topology software in this context. 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). article_processing_charge: Yes article_type: original author: - first_name: Herbert full_name: Edelsbrunner, Herbert id: 3FB178DA-F248-11E8-B48F-1D18A9856A87 last_name: Edelsbrunner orcid: 0000-0002-9823-6833 - first_name: Ziga full_name: Virk, Ziga id: 2E36B656-F248-11E8-B48F-1D18A9856A87 last_name: Virk - first_name: Hubert full_name: Wagner, Hubert id: 379CA8B8-F248-11E8-B48F-1D18A9856A87 last_name: Wagner citation: ama: Edelsbrunner H, Virk Z, Wagner H. Topological data analysis in information space. Journal of Computational Geometry. 2020;11(2):162-182. doi:10.20382/jocg.v11i2a7 apa: Edelsbrunner, H., Virk, Z., & Wagner, H. (2020). Topological data analysis in information space. Journal of Computational Geometry. Carleton University. https://doi.org/10.20382/jocg.v11i2a7 chicago: Edelsbrunner, Herbert, Ziga Virk, and Hubert Wagner. “Topological Data Analysis in Information Space.” Journal of Computational Geometry. Carleton University, 2020. https://doi.org/10.20382/jocg.v11i2a7. ieee: H. Edelsbrunner, Z. Virk, and H. Wagner, “Topological data analysis in information space,” Journal of Computational Geometry, vol. 11, no. 2. Carleton University, pp. 162–182, 2020. ista: Edelsbrunner H, Virk Z, Wagner H. 2020. Topological data analysis in information space. Journal of Computational Geometry. 11(2), 162–182. mla: Edelsbrunner, Herbert, et al. “Topological Data Analysis in Information Space.” Journal of Computational Geometry, vol. 11, no. 2, Carleton University, 2020, pp. 162–82, doi:10.20382/jocg.v11i2a7. short: H. Edelsbrunner, Z. Virk, H. Wagner, Journal of Computational Geometry 11 (2020) 162–182. date_created: 2021-07-04T22:01:26Z date_published: 2020-12-14T00:00:00Z date_updated: 2021-08-11T12:26:34Z day: '14' ddc: - '510' - '000' department: - _id: HeEd doi: 10.20382/jocg.v11i2a7 file: - access_level: open_access checksum: f02d0b2b3838e7891a6c417fc34ffdcd content_type: application/pdf creator: asandaue date_created: 2021-08-11T11:55:11Z date_updated: 2021-08-11T11:55:11Z file_id: '9882' file_name: 2020_JournalOfComputationalGeometry_Edelsbrunner.pdf file_size: 1449234 relation: main_file success: 1 file_date_updated: 2021-08-11T11:55:11Z has_accepted_license: '1' intvolume: ' 11' issue: '2' language: - iso: eng license: https://creativecommons.org/licenses/by/3.0/ month: '12' oa: 1 oa_version: Published Version page: 162-182 project: - _id: 0aa4bc98-070f-11eb-9043-e6fff9c6a316 grant_number: I4887 name: Discretization in Geometry and Dynamics publication: Journal of Computational Geometry publication_identifier: eissn: - 1920180X publication_status: published publisher: Carleton University quality_controlled: '1' scopus_import: '1' status: public title: Topological data analysis in information space tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode name: Creative Commons Attribution 3.0 Unported (CC BY 3.0) short: CC BY (3.0) type: journal_article user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf volume: 11 year: '2020' ... --- _id: '9631' 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. acknowledgement: "We thank Marco Mondelli for discussions related to LDPC decoding, and Giorgi Nadiradze for discussions on analysis of relaxed schedulers. This project has received funding from the European Research Council (ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML)." article_processing_charge: No arxiv: 1 author: - first_name: Vitaly full_name: Aksenov, Vitaly last_name: Aksenov - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X - first_name: Janne full_name: Korhonen, Janne id: C5402D42-15BC-11E9-A202-CA2BE6697425 last_name: Korhonen citation: ama: 'Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: Advances in Neural Information Processing Systems. Vol 33. Curran Associates; 2020:22361-22372.' apa: 'Aksenov, V., Alistarh, D.-A., & Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling. In Advances in Neural Information Processing Systems (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 Advances in Neural Information Processing Systems, 33:22361–72. Curran Associates, 2020. ieee: V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372. ista: 'Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via relaxed scheduling. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 22361–22372.' mla: Aksenov, Vitaly, et al. “Scalable Belief Propagation via Relaxed Scheduling.” Advances in Neural Information Processing Systems, vol. 33, Curran Associates, 2020, pp. 22361–72. short: V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 22361–22372. conference: end_date: 2020-12-12 location: Vancouver, Canada name: 'NeurIPS: Conference on Neural Information Processing Systems' start_date: 2020-12-06 date_created: 2021-07-04T22:01:26Z date_published: 2020-12-06T00:00:00Z date_updated: 2023-02-23T14:03:03Z day: '06' department: - _id: DaAl ec_funded: 1 external_id: arxiv: - '2002.11505' intvolume: ' 33' language: - iso: eng main_file_link: - open_access: '1' url: https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html month: '12' oa: 1 oa_version: Published Version page: 22361-22372 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: Advances in Neural Information Processing Systems publication_identifier: isbn: - '9781713829546' issn: - '10495258' publication_status: published publisher: Curran Associates quality_controlled: '1' scopus_import: '1' status: public title: Scalable belief propagation via relaxed scheduling type: conference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf volume: 33 year: '2020' ... --- _id: '9632' abstract: - lang: eng text: "Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep\r\nneural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for oneshot pruning. Further, even when iterative, gradual pruning is allowed, our method results in a gain in test accuracy over the state-of-the-art approaches, for standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as\r\nillustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher." 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. article_processing_charge: No arxiv: 1 author: - first_name: Sidak Pal full_name: Singh, Sidak Pal id: DD138E24-D89D-11E9-9DC0-DEF6E5697425 last_name: Singh - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: Advances in Neural Information Processing Systems. Vol 33. Curran Associates; 2020:18098-18109.' apa: 'Singh, S. P., & Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression. In Advances in Neural Information Processing Systems (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 Advances in Neural Information Processing Systems, 33:18098–109. Curran Associates, 2020.' ieee: 'S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.' ista: 'Singh SP, Alistarh D-A. 2020. WoodFisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 18098–18109.' mla: 'Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” Advances in Neural Information Processing Systems, vol. 33, Curran Associates, 2020, pp. 18098–109.' short: S.P. Singh, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 18098–18109. conference: end_date: 2020-12-12 location: Vancouver, Canada name: 'NeurIPS: Conference on Neural Information Processing Systems' start_date: 2020-12-06 date_created: 2021-07-04T22:01:26Z date_published: 2020-12-06T00:00:00Z date_updated: 2023-02-23T14:03:06Z day: '06' department: - _id: DaAl - _id: ToHe ec_funded: 1 external_id: arxiv: - '2004.14340' intvolume: ' 33' language: - iso: eng main_file_link: - open_access: '1' url: https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html month: '12' oa: 1 oa_version: Published Version page: 18098-18109 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: Advances in Neural Information Processing Systems publication_identifier: isbn: - '9781713829546' issn: - '10495258' publication_status: published publisher: Curran Associates quality_controlled: '1' scopus_import: '1' status: public title: 'WoodFisher: Efficient second-order approximation for neural network compression' type: conference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf volume: 33 year: '2020' ... --- _id: '9633' abstract: - lang: eng text: The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by – and fitted to – experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Oja’s rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions. 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. article_processing_charge: No author: - first_name: Basile J full_name: Confavreux, Basile J id: C7610134-B532-11EA-BD9F-F5753DDC885E last_name: Confavreux - first_name: Friedemann full_name: Zenke, Friedemann last_name: Zenke - first_name: Everton J. full_name: Agnes, Everton J. last_name: Agnes - first_name: Timothy full_name: Lillicrap, Timothy last_name: Lillicrap - first_name: Tim P full_name: Vogels, Tim P id: CB6FF8D2-008F-11EA-8E08-2637E6697425 last_name: Vogels orcid: 0000-0003-3295-6181 citation: 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: Advances in Neural Information Processing Systems. Vol 33. ; 2020:16398-16408.' apa: Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., & Vogels, T. P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In Advances in Neural Information Processing Systems (Vol. 33, pp. 16398–16408). Vancouver, Canada. 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 Advances in Neural Information Processing Systems, 33:16398–408, 2020. 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 Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 16398–16408. ista: 'Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 16398–16408.' mla: Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 16398–408. 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. conference: end_date: 2020-12-12 location: Vancouver, Canada name: 'NeurIPS: Conference on Neural Information Processing Systems' start_date: 2020-12-06 date_created: 2021-07-04T22:01:27Z date_published: 2020-12-06T00:00:00Z date_updated: 2023-10-18T09:20:55Z day: '06' department: - _id: TiVo ec_funded: 1 intvolume: ' 33' language: - iso: eng main_file_link: - open_access: '1' url: https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html month: '12' oa: 1 oa_version: Published Version page: 16398-16408 project: - _id: c084a126-5a5b-11eb-8a69-d75314a70a87 grant_number: 214316/Z/18/Z name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks. - _id: 0aacfa84-070f-11eb-9043-d7eb2c709234 call_identifier: H2020 grant_number: '819603' name: Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning. publication: Advances in Neural Information Processing Systems publication_identifier: issn: - 1049-5258 publication_status: published quality_controlled: '1' related_material: link: - relation: is_continued_by url: https://doi.org/10.1101/2020.10.24.353409 record: - id: '14422' relation: dissertation_contains status: public scopus_import: '1' status: public title: A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network type: conference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf volume: 33 year: '2020' ... --- _id: '10012' abstract: - lang: eng text: We prove that in the absence of topological changes, the notion of BV solutions to planar multiphase mean curvature flow does not allow for a mechanism for (unphysical) non-uniqueness. Our approach is based on the local structure of the energy landscape near a classical evolution by mean curvature. Mean curvature flow being the gradient flow of the surface energy functional, we develop a gradient-flow analogue of the notion of calibrations. Just like the existence of a calibration guarantees that one has reached a global minimum in the energy landscape, the existence of a "gradient flow calibration" ensures that the route of steepest descent in the energy landscape is unique and stable. acknowledgement: Parts of the paper were written during the visit of the authors to the Hausdorff Research Institute for Mathematics (HIM), University of Bonn, in the framework of the trimester program “Evolution of Interfaces”. The support and the hospitality of HIM are gratefully acknowledged. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 665385. article_number: '2003.05478' article_processing_charge: No arxiv: 1 author: - first_name: Julian L full_name: Fischer, Julian L id: 2C12A0B0-F248-11E8-B48F-1D18A9856A87 last_name: Fischer orcid: 0000-0002-0479-558X - first_name: Sebastian full_name: Hensel, Sebastian id: 4D23B7DA-F248-11E8-B48F-1D18A9856A87 last_name: Hensel orcid: 0000-0001-7252-8072 - first_name: Tim full_name: Laux, Tim last_name: Laux - first_name: Thilo full_name: Simon, Thilo last_name: Simon citation: ama: 'Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. arXiv.' apa: 'Fischer, J. L., Hensel, S., Laux, T., & Simon, T. (n.d.). The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. arXiv.' chicago: 'Fischer, Julian L, Sebastian Hensel, Tim Laux, and Thilo Simon. “The Local Structure of the Energy Landscape in Multiphase Mean Curvature Flow: Weak-Strong Uniqueness and Stability of Evolutions.” ArXiv, n.d.' ieee: 'J. L. Fischer, S. Hensel, T. Laux, and T. Simon, “The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions,” arXiv. .' ista: 'Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. arXiv, 2003.05478.' mla: 'Fischer, Julian L., et al. “The Local Structure of the Energy Landscape in Multiphase Mean Curvature Flow: Weak-Strong Uniqueness and Stability of Evolutions.” ArXiv, 2003.05478.' short: J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.). date_created: 2021-09-13T12:17:11Z date_published: 2020-03-11T00:00:00Z date_updated: 2023-09-07T13:30:45Z day: '11' department: - _id: JuFi ec_funded: 1 external_id: arxiv: - '2003.05478' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2003.05478 month: '03' oa: 1 oa_version: Preprint project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: arXiv publication_status: submitted related_material: record: - id: '10007' relation: dissertation_contains status: public status: public title: 'The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions' type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2020' ... --- _id: '10022' abstract: - lang: eng text: We consider finite-volume approximations of Fokker-Planck equations on bounded convex domains in R^d and study the corresponding gradient flow structures. We reprove the convergence of the discrete to continuous Fokker-Planck equation via the method of Evolutionary Γ-convergence, i.e., we pass to the limit at the level of the gradient flow structures, generalising the one-dimensional result obtained by Disser and Liero. The proof is of variational nature and relies on a Mosco convergence result for functionals in the discrete-to-continuum limit that is of independent interest. Our results apply to arbitrary regular meshes, even though the associated discrete transport distances may fail to converge to the Wasserstein distance in this generality. acknowledgement: This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 716117) and by the Austrian Science Fund (FWF), grants No F65 and W1245. article_number: '2008.10962' article_processing_charge: No arxiv: 1 author: - first_name: Dominik L full_name: Forkert, Dominik L id: 35C79D68-F248-11E8-B48F-1D18A9856A87 last_name: Forkert - first_name: Jan full_name: Maas, Jan id: 4C5696CE-F248-11E8-B48F-1D18A9856A87 last_name: Maas orcid: 0000-0002-0845-1338 - first_name: Lorenzo full_name: Portinale, Lorenzo id: 30AD2CBC-F248-11E8-B48F-1D18A9856A87 last_name: Portinale citation: ama: Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. arXiv. apa: Forkert, D. L., Maas, J., & Portinale, L. (n.d.). Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. arXiv. chicago: Forkert, Dominik L, Jan Maas, and Lorenzo Portinale. “Evolutionary Γ-Convergence of Entropic Gradient Flow Structures for Fokker-Planck Equations in Multiple Dimensions.” ArXiv, n.d. ieee: D. L. Forkert, J. Maas, and L. Portinale, “Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions,” arXiv. . ista: Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. arXiv, 2008.10962. mla: Forkert, Dominik L., et al. “Evolutionary Γ-Convergence of Entropic Gradient Flow Structures for Fokker-Planck Equations in Multiple Dimensions.” ArXiv, 2008.10962. short: D.L. Forkert, J. Maas, L. Portinale, ArXiv (n.d.). date_created: 2021-09-17T10:57:27Z date_published: 2020-08-25T00:00:00Z date_updated: 2023-09-07T13:31:05Z day: '25' department: - _id: JaMa ec_funded: 1 external_id: arxiv: - '2008.10962' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2008.10962 month: '08' oa: 1 oa_version: Preprint page: '33' project: - _id: 256E75B8-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '716117' name: Optimal Transport and Stochastic Dynamics - _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2 grant_number: F6504 name: Taming Complexity in Partial Differential Systems publication: arXiv publication_status: submitted related_material: record: - id: '11739' relation: later_version status: public - id: '10030' relation: dissertation_contains status: public status: public title: Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2020' ... --- _id: '10328' abstract: - lang: eng text: We discus noise channels in coherent electro-optic up-conversion between microwave and optical fields, in particular due to optical heating. We also report on a novel configuration, which promises to be flexible and highly efficient. alternative_title: - OSA Technical Digest article_number: QTu8A.1 article_processing_charge: No author: - first_name: Nicholas J. full_name: Lambert, Nicholas J. last_name: Lambert - first_name: Sonia full_name: Mobassem, Sonia last_name: Mobassem - first_name: Alfredo R full_name: Rueda Sanchez, Alfredo R id: 3B82B0F8-F248-11E8-B48F-1D18A9856A87 last_name: Rueda Sanchez orcid: 0000-0001-6249-5860 - first_name: Harald G.L. full_name: Schwefel, Harald G.L. last_name: Schwefel citation: ama: 'Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. New designs and noise channels in electro-optic microwave to optical up-conversion. In: OSA Quantum 2.0 Conference. Optica Publishing Group; 2020. doi:10.1364/QUANTUM.2020.QTu8A.1' apa: 'Lambert, N. J., Mobassem, S., Rueda Sanchez, A. R., & Schwefel, H. G. L. (2020). New designs and noise channels in electro-optic microwave to optical up-conversion. In OSA Quantum 2.0 Conference. Washington, DC, United States: Optica Publishing Group. https://doi.org/10.1364/QUANTUM.2020.QTu8A.1' chicago: Lambert, Nicholas J., Sonia Mobassem, Alfredo R Rueda Sanchez, and Harald G.L. Schwefel. “New Designs and Noise Channels in Electro-Optic Microwave to Optical up-Conversion.” In OSA Quantum 2.0 Conference. Optica Publishing Group, 2020. https://doi.org/10.1364/QUANTUM.2020.QTu8A.1. ieee: N. J. Lambert, S. Mobassem, A. R. Rueda Sanchez, and H. G. L. Schwefel, “New designs and noise channels in electro-optic microwave to optical up-conversion,” in OSA Quantum 2.0 Conference, Washington, DC, United States, 2020. ista: 'Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. 2020. New designs and noise channels in electro-optic microwave to optical up-conversion. OSA Quantum 2.0 Conference. OSA: Optical Society of America, OSA Technical Digest, , QTu8A.1.' mla: Lambert, Nicholas J., et al. “New Designs and Noise Channels in Electro-Optic Microwave to Optical up-Conversion.” OSA Quantum 2.0 Conference, QTu8A.1, Optica Publishing Group, 2020, doi:10.1364/QUANTUM.2020.QTu8A.1. short: N.J. Lambert, S. Mobassem, A.R. Rueda Sanchez, H.G.L. Schwefel, in:, OSA Quantum 2.0 Conference, Optica Publishing Group, 2020. conference: end_date: 2020-09-17 location: Washington, DC, United States name: 'OSA: Optical Society of America' start_date: 2020-09-14 date_created: 2021-11-21T23:01:31Z date_published: 2020-01-01T00:00:00Z date_updated: 2023-10-18T08:32:34Z day: '01' department: - _id: JoFi doi: 10.1364/QUANTUM.2020.QTu8A.1 language: - iso: eng month: '01' oa_version: None publication: OSA Quantum 2.0 Conference publication_identifier: isbn: - 9-781-5575-2820-9 publication_status: published publisher: Optica Publishing Group quality_controlled: '1' scopus_import: '1' status: public title: New designs and noise channels in electro-optic microwave to optical up-conversion type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '10556' abstract: - lang: eng text: In this paper, we present the first Asynchronous Distributed Key Generation (ADKG) algorithm which is also the first distributed key generation algorithm that can generate cryptographic keys with a dual (f,2f+1)-threshold (where f is the number of faulty parties). As a result, using our ADKG we remove the trusted setup assumption that the most scalable consensus algorithms make. In order to create a DKG with a dual (f,2f+1)- threshold we first answer in the affirmative the open question posed by Cachin et al. [7] on how to create an Asynchronous Verifiable Secret Sharing (AVSS) protocol with a reconstruction threshold of f+1Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery; 2020:1751–1767. doi:10.1145/3372297.3423364' apa: 'Kokoris Kogias, E., Malkhi, D., & Spiegelman, A. (2020). Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (pp. 1751–1767). Virtual, United States: Association for Computing Machinery. https://doi.org/10.1145/3372297.3423364' chicago: Kokoris Kogias, Eleftherios, Dahlia Malkhi, and Alexander Spiegelman. “Asynchronous Distributed Key Generation for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 1751–1767. Association for Computing Machinery, 2020. https://doi.org/10.1145/3372297.3423364. ieee: E. Kokoris Kogias, D. Malkhi, and A. Spiegelman, “Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, Virtual, United States, 2020, pp. 1751–1767. ista: 'Kokoris Kogias E, Malkhi D, Spiegelman A. 2020. Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. CCS: Computer and Communications Security, 1751–1767.' mla: Kokoris Kogias, Eleftherios, et al. “Asynchronous Distributed Key Generation for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery, 2020, pp. 1751–1767, doi:10.1145/3372297.3423364. short: E. Kokoris Kogias, D. Malkhi, A. Spiegelman, in:, Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery, 2020, pp. 1751–1767. conference: end_date: 2020-11-13 location: Virtual, United States name: 'CCS: Computer and Communications Security' start_date: 2020-11-09 date_created: 2021-12-16T13:23:27Z date_published: 2020-10-30T00:00:00Z date_updated: 2024-02-22T13:10:45Z day: '30' department: - _id: ElKo doi: 10.1145/3372297.3423364 external_id: isi: - '000768470400104' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://eprint.iacr.org/2019/1015 month: '10' oa: 1 oa_version: Preprint page: 1751–1767 publication: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security publication_identifier: isbn: - 978-1-4503-7089-9 publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' scopus_import: '1' status: public title: Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '10557' abstract: - lang: eng text: Data storage and retrieval systems, methods, and computer-readable media utilize a cryptographically verifiable data structure that facilitates verification of a transaction in a decentralized peer-to-peer environment using multi-hop backwards and forwards links. Backward links are cryptographic hashes of past records. Forward links are cryptographic signatures of future records that are added retroactively to records once the target block has been appended to the data structure. applicant: - Ecole Polytechnique Federale de Lausanne application_date: 2017-06-09 article_processing_charge: No author: - first_name: Bryan full_name: Ford, Bryan last_name: Ford - first_name: Linus full_name: Gasse, Linus last_name: Gasse - first_name: Eleftherios full_name: Kokoris Kogias, Eleftherios id: f5983044-d7ef-11ea-ac6d-fd1430a26d30 last_name: Kokoris Kogias - first_name: Philipp full_name: Jovanovic, Philipp last_name: Jovanovic citation: ama: Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods. 2020. apa: Ford, B., Gasse, L., Kokoris Kogias, E., & Jovanovic, P. (2020). Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods. chicago: Ford, Bryan, Linus Gasse, Eleftherios Kokoris Kogias, and Philipp Jovanovic. “Cryptographically Verifiable Data Structure Having Multi-Hop Forward and Backwards Links and Associated Systems and Methods,” 2020. ieee: B. Ford, L. Gasse, E. Kokoris Kogias, and P. Jovanovic, “Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods.” 2020. ista: Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. 2020. Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods. mla: Ford, Bryan, et al. Cryptographically Verifiable Data Structure Having Multi-Hop Forward and Backwards Links and Associated Systems and Methods. 2020. short: B. Ford, L. Gasse, E. Kokoris Kogias, P. Jovanovic, (2020). date_created: 2021-12-16T13:28:59Z date_published: 2020-03-03T00:00:00Z date_updated: 2021-12-21T10:04:50Z day: '03' department: - _id: ElKo extern: '1' ipc: ' H04L9/3247 ; G06Q20/29 ; G06Q20/382 ; H04L9/3236' ipn: '10581613' main_file_link: - open_access: '1' url: https://patents.google.com/patent/US10581613B2/en month: '03' oa: 1 oa_version: Published Version publication_date: 2020-03-03 related_material: link: - relation: earlier_version url: https://patents.google.com/patent/US20180359096A1/en status: public title: Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods type: patent user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2020' ...