--- _id: '1424' abstract: - lang: eng text: We consider the problem of statistical computations with persistence diagrams, a summary representation of topological features in data. These diagrams encode persistent homology, a widely used invariant in topological data analysis. While several avenues towards a statistical treatment of the diagrams have been explored recently, we follow an alternative route that is motivated by the success of methods based on the embedding of probability measures into reproducing kernel Hilbert spaces. In fact, a positive definite kernel on persistence diagrams has recently been proposed, connecting persistent homology to popular kernel-based learning techniques such as support vector machines. However, important properties of that kernel enabling a principled use in the context of probability measure embeddings remain to be explored. Our contribution is to close this gap by proving universality of a variant of the original kernel, and to demonstrate its effective use in twosample hypothesis testing on synthetic as well as real-world data. acknowledgement: This work was partially supported by the Austrian Science FUnd, project no. KLI 00012. alternative_title: - Advances in Neural Information Processing Systems author: - first_name: Roland full_name: Kwitt, Roland last_name: Kwitt - first_name: Stefan full_name: Huber, Stefan id: 4700A070-F248-11E8-B48F-1D18A9856A87 last_name: Huber orcid: 0000-0002-8871-5814 - first_name: Marc full_name: Niethammer, Marc last_name: Niethammer - first_name: Weili full_name: Lin, Weili last_name: Lin - first_name: Ulrich full_name: Bauer, Ulrich id: 2ADD483A-F248-11E8-B48F-1D18A9856A87 last_name: Bauer orcid: 0000-0002-9683-0724 citation: ama: 'Kwitt R, Huber S, Niethammer M, Lin W, Bauer U. Statistical topological data analysis-A kernel perspective. In: Vol 28. Neural Information Processing Systems; 2015:3070-3078.' apa: 'Kwitt, R., Huber, S., Niethammer, M., Lin, W., & Bauer, U. (2015). Statistical topological data analysis-A kernel perspective (Vol. 28, pp. 3070–3078). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.' chicago: Kwitt, Roland, Stefan Huber, Marc Niethammer, Weili Lin, and Ulrich Bauer. “Statistical Topological Data Analysis-A Kernel Perspective,” 28:3070–78. Neural Information Processing Systems, 2015. ieee: 'R. Kwitt, S. Huber, M. Niethammer, W. Lin, and U. Bauer, “Statistical topological data analysis-A kernel perspective,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol. 28, pp. 3070–3078.' ista: 'Kwitt R, Huber S, Niethammer M, Lin W, Bauer U. 2015. Statistical topological data analysis-A kernel perspective. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 28, 3070–3078.' mla: Kwitt, Roland, et al. Statistical Topological Data Analysis-A Kernel Perspective. Vol. 28, Neural Information Processing Systems, 2015, pp. 3070–78. short: R. Kwitt, S. Huber, M. Niethammer, W. Lin, U. Bauer, in:, Neural Information Processing Systems, 2015, pp. 3070–3078. conference: end_date: 2015-12-12 location: Montreal, Canada name: 'NIPS: Neural Information Processing Systems' start_date: 2015-12-07 date_created: 2018-12-11T11:51:56Z date_published: 2015-12-01T00:00:00Z date_updated: 2021-01-12T06:50:38Z day: '01' department: - _id: HeEd intvolume: ' 28' language: - iso: eng main_file_link: - open_access: '1' url: https://papers.nips.cc/paper/5887-statistical-topological-data-analysis-a-kernel-perspective month: '12' oa: 1 oa_version: Submitted Version page: 3070 - 3078 publication_status: published publisher: Neural Information Processing Systems publist_id: '5782' quality_controlled: '1' status: public title: Statistical topological data analysis-A kernel perspective type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 28 year: '2015' ... --- _id: '1430' abstract: - lang: eng text: Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse their runtime on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrence of new mutations is much longer than the time it takes for a new beneficial mutation to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a (1+1)-type process where the probability of accepting a new genotype (improvements or worsenings) depends on the change in fitness. We present an initial runtime analysis of SSWM, quantifying its performance for various parameters and investigating differences to the (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient. author: - first_name: Tiago full_name: Paixao, Tiago id: 2C5658E6-F248-11E8-B48F-1D18A9856A87 last_name: Paixao orcid: 0000-0003-2361-3953 - first_name: Dirk full_name: Sudholt, Dirk last_name: Sudholt - first_name: Jorge full_name: Heredia, Jorge last_name: Heredia - first_name: Barbora full_name: Trubenova, Barbora id: 42302D54-F248-11E8-B48F-1D18A9856A87 last_name: Trubenova orcid: 0000-0002-6873-2967 citation: ama: 'Paixao T, Sudholt D, Heredia J, Trubenova B. First steps towards a runtime comparison of natural and artificial evolution. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. ACM; 2015:1455-1462. doi:10.1145/2739480.2754758' apa: 'Paixao, T., Sudholt, D., Heredia, J., & Trubenova, B. (2015). First steps towards a runtime comparison of natural and artificial evolution. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp. 1455–1462). Madrid, Spain: ACM. https://doi.org/10.1145/2739480.2754758' chicago: Paixao, Tiago, Dirk Sudholt, Jorge Heredia, and Barbora Trubenova. “First Steps towards a Runtime Comparison of Natural and Artificial Evolution.” In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 1455–62. ACM, 2015. https://doi.org/10.1145/2739480.2754758. ieee: T. Paixao, D. Sudholt, J. Heredia, and B. Trubenova, “First steps towards a runtime comparison of natural and artificial evolution,” in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, 2015, pp. 1455–1462. ista: 'Paixao T, Sudholt D, Heredia J, Trubenova B. 2015. First steps towards a runtime comparison of natural and artificial evolution. Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO: Genetic and evolutionary computation conference, 1455–1462.' mla: Paixao, Tiago, et al. “First Steps towards a Runtime Comparison of Natural and Artificial Evolution.” Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, ACM, 2015, pp. 1455–62, doi:10.1145/2739480.2754758. short: T. Paixao, D. Sudholt, J. Heredia, B. Trubenova, in:, Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, ACM, 2015, pp. 1455–1462. conference: end_date: 2015-07-15 location: Madrid, Spain name: 'GECCO: Genetic and evolutionary computation conference' start_date: 2015-07-11 date_created: 2018-12-11T11:51:58Z date_published: 2015-07-11T00:00:00Z date_updated: 2021-01-12T06:50:41Z day: '11' department: - _id: NiBa - _id: CaGu doi: 10.1145/2739480.2754758 ec_funded: 1 language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1504.06260 month: '07' oa: 1 oa_version: Preprint page: 1455 - 1462 project: - _id: 25B1EC9E-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '618091' name: Speed of Adaptation in Population Genetics and Evolutionary Computation publication: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation publication_status: published publisher: ACM publist_id: '5768' quality_controlled: '1' scopus_import: 1 status: public title: First steps towards a runtime comparison of natural and artificial evolution type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '1474' abstract: - lang: eng text: Cryptographic access control offers selective access to encrypted data via a combination of key management and functionality-rich cryptographic schemes, such as attribute-based encryption. Using this approach, publicly available meta-data may inadvertently leak information on the access policy that is enforced by cryptography, which renders cryptographic access control unusable in settings where this information is highly sensitive. We begin to address this problem by presenting rigorous definitions for policy privacy in cryptographic access control. For concreteness we set our results in the model of Role-Based Access Control (RBAC), where we identify and formalize several different flavors of privacy, however, our framework should serve as inspiration for other models of access control. Based on our insights we propose a new system which significantly improves on the privacy properties of state-of-the-art constructions. Our design is based on a novel type of privacy-preserving attribute-based encryption, which we introduce and show how to instantiate. We present our results in the context of a cryptographic RBAC system by Ferrara et al. (CSF'13), which uses cryptography to control read access to files, while write access is still delegated to trusted monitors. We give an extension of the construction that permits cryptographic control over write access. Our construction assumes that key management uses out-of-band channels between the policy enforcer and the users but eliminates completely the need for monitoring read/write access to the data. article_processing_charge: No author: - first_name: Anna full_name: Ferrara, Anna last_name: Ferrara - first_name: Georg full_name: Fuchsbauer, Georg id: 46B4C3EE-F248-11E8-B48F-1D18A9856A87 last_name: Fuchsbauer - first_name: Bin full_name: Liu, Bin last_name: Liu - first_name: Bogdan full_name: Warinschi, Bogdan last_name: Warinschi citation: ama: 'Ferrara A, Fuchsbauer G, Liu B, Warinschi B. Policy privacy in cryptographic access control. In: IEEE; 2015:46-60. doi:10.1109/CSF.2015.11' apa: 'Ferrara, A., Fuchsbauer, G., Liu, B., & Warinschi, B. (2015). Policy privacy in cryptographic access control (pp. 46–60). Presented at the CSF: Computer Security Foundations, Verona, Italy: IEEE. https://doi.org/10.1109/CSF.2015.11' chicago: Ferrara, Anna, Georg Fuchsbauer, Bin Liu, and Bogdan Warinschi. “Policy Privacy in Cryptographic Access Control,” 46–60. IEEE, 2015. https://doi.org/10.1109/CSF.2015.11. ieee: 'A. Ferrara, G. Fuchsbauer, B. Liu, and B. Warinschi, “Policy privacy in cryptographic access control,” presented at the CSF: Computer Security Foundations, Verona, Italy, 2015, pp. 46–60.' ista: 'Ferrara A, Fuchsbauer G, Liu B, Warinschi B. 2015. Policy privacy in cryptographic access control. CSF: Computer Security Foundations, 46–60.' mla: Ferrara, Anna, et al. Policy Privacy in Cryptographic Access Control. IEEE, 2015, pp. 46–60, doi:10.1109/CSF.2015.11. short: A. Ferrara, G. Fuchsbauer, B. Liu, B. Warinschi, in:, IEEE, 2015, pp. 46–60. conference: end_date: 2015-07-17 location: Verona, Italy name: 'CSF: Computer Security Foundations' start_date: 2015-07-13 date_created: 2018-12-11T11:52:14Z date_published: 2015-09-04T00:00:00Z date_updated: 2021-01-12T06:50:59Z day: '04' department: - _id: KrPi doi: 10.1109/CSF.2015.11 ec_funded: 1 language: - iso: eng main_file_link: - open_access: '1' url: http://epubs.surrey.ac.uk/808055/ month: '09' oa: 1 oa_version: Submitted Version page: 46-60 project: - _id: 258C570E-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '259668' name: Provable Security for Physical Cryptography publication_status: published publisher: IEEE publist_id: '5722' quality_controlled: '1' status: public title: Policy privacy in cryptographic access control type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '1473' abstract: - lang: eng text: In this paper we survey geometric and arithmetic techniques to study the cohomology of semiprojective hyperkähler manifolds including toric hyperkähler varieties, Nakajima quiver varieties and moduli spaces of Higgs bundles on Riemann surfaces. The resulting formulae for their Poincaré polynomials are combinatorial and representation theoretical in nature. In particular we will look at their Betti numbers and will establish some results and state some expectations on their asymptotic shape. author: - first_name: Tamas full_name: Tamas Hausel id: 4A0666D8-F248-11E8-B48F-1D18A9856A87 last_name: Hausel - first_name: Fernando full_name: Rodríguez Villegas, Fernando last_name: Rodríguez Villegas citation: ama: Hausel T, Rodríguez Villegas F. Cohomology of large semiprojective hyperkähler varieties. Asterisque. 2015;2015(370):113-156. apa: Hausel, T., & Rodríguez Villegas, F. (2015). Cohomology of large semiprojective hyperkähler varieties. Asterisque. Societe Mathematique de France. chicago: Hausel, Tamás, and Fernando Rodríguez Villegas. “Cohomology of Large Semiprojective Hyperkähler Varieties.” Asterisque. Societe Mathematique de France, 2015. ieee: T. Hausel and F. Rodríguez Villegas, “Cohomology of large semiprojective hyperkähler varieties,” Asterisque, vol. 2015, no. 370. Societe Mathematique de France, pp. 113–156, 2015. ista: Hausel T, Rodríguez Villegas F. 2015. Cohomology of large semiprojective hyperkähler varieties. Asterisque. 2015(370), 113–156. mla: Hausel, Tamás, and Fernando Rodríguez Villegas. “Cohomology of Large Semiprojective Hyperkähler Varieties.” Asterisque, vol. 2015, no. 370, Societe Mathematique de France, 2015, pp. 113–56. short: T. Hausel, F. Rodríguez Villegas, Asterisque 2015 (2015) 113–156. date_created: 2018-12-11T11:52:13Z date_published: 2015-01-01T00:00:00Z date_updated: 2021-01-12T06:50:59Z day: '01' extern: 1 intvolume: ' 2015' issue: '370' main_file_link: - open_access: '1' url: http://arxiv.org/abs/1309.4914 month: '01' oa: 1 page: 113 - 156 publication: Asterisque publication_status: published publisher: Societe Mathematique de France publist_id: '5723' quality_controlled: 0 status: public title: Cohomology of large semiprojective hyperkähler varieties type: review volume: 2015 year: '2015' ... --- _id: '1483' abstract: - lang: eng text: Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In this work, we establish such a connection by designing a multi-scale kernel for persistence diagrams, a stable summary representation of topological features in data. We show that this kernel is positive definite and prove its stability with respect to the 1-Wasserstein distance. Experiments on two benchmark datasets for 3D shape classification/retrieval and texture recognition show considerable performance gains of the proposed method compared to an alternative approach that is based on the recently introduced persistence landscapes. author: - first_name: Jan full_name: Reininghaus, Jan id: 4505473A-F248-11E8-B48F-1D18A9856A87 last_name: Reininghaus - first_name: Stefan full_name: Huber, Stefan id: 4700A070-F248-11E8-B48F-1D18A9856A87 last_name: Huber orcid: 0000-0002-8871-5814 - first_name: Ulrich full_name: Bauer, Ulrich id: 2ADD483A-F248-11E8-B48F-1D18A9856A87 last_name: Bauer orcid: 0000-0002-9683-0724 - first_name: Roland full_name: Kwitt, Roland last_name: Kwitt citation: ama: 'Reininghaus J, Huber S, Bauer U, Kwitt R. A stable multi-scale kernel for topological machine learning. In: IEEE; 2015:4741-4748. doi:10.1109/CVPR.2015.7299106' apa: 'Reininghaus, J., Huber, S., Bauer, U., & Kwitt, R. (2015). A stable multi-scale kernel for topological machine learning (pp. 4741–4748). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE. https://doi.org/10.1109/CVPR.2015.7299106' chicago: Reininghaus, Jan, Stefan Huber, Ulrich Bauer, and Roland Kwitt. “A Stable Multi-Scale Kernel for Topological Machine Learning,” 4741–48. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7299106. ieee: 'J. Reininghaus, S. Huber, U. Bauer, and R. Kwitt, “A stable multi-scale kernel for topological machine learning,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 4741–4748.' ista: 'Reininghaus J, Huber S, Bauer U, Kwitt R. 2015. A stable multi-scale kernel for topological machine learning. CVPR: Computer Vision and Pattern Recognition, 4741–4748.' mla: Reininghaus, Jan, et al. A Stable Multi-Scale Kernel for Topological Machine Learning. IEEE, 2015, pp. 4741–48, doi:10.1109/CVPR.2015.7299106. short: J. Reininghaus, S. Huber, U. Bauer, R. Kwitt, in:, IEEE, 2015, pp. 4741–4748. conference: end_date: 2015-06-12 location: Boston, MA, USA name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2015-06-07 date_created: 2018-12-11T11:52:17Z date_published: 2015-10-14T00:00:00Z date_updated: 2021-01-12T06:51:03Z day: '14' department: - _id: HeEd doi: 10.1109/CVPR.2015.7299106 language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1412.6821 month: '10' oa: 1 oa_version: Preprint page: 4741 - 4748 publication_identifier: eisbn: - '978-1-4673-6964-0 ' publication_status: published publisher: IEEE publist_id: '5709' scopus_import: 1 status: public title: A stable multi-scale kernel for topological machine learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ...