TY - CHAP A1 - Roy, Aurko A1 - Pokutta, Sebastian T1 - Hierarchical Clustering via Spreading Metrics T2 - Proceedings of NIPS Y1 - 2016 N1 - Additional Note: http://https://papers.nips.cc/paper/by-source-2016-1199 https://arxiv.org/abs/1610.09269 N1 - URL of the PDF: https://papers.nips.cc/paper/by-source-2016-1199 ER - TY - CHAP A1 - Braun, Gábor A1 - Roy, Aurko A1 - Pokutta, Sebastian T1 - Stronger Reductions for Extended Formulations T2 - Proceedings of IPCO Y1 - 2016 ER - TY - CHAP A1 - Braun, Gábor A1 - Brown-Cohen, Jonah A1 - Huq, Arefin A1 - Pokutta, Sebastian A1 - Raghavendra, Prasad A1 - Weitz, Benjamin A1 - Zink, Daniel T1 - The matching problem has no small symmetric SDP T2 - Proceddings of SODA 2016 Y1 - 2016 N1 - URL of the PDF: http://ieeexplore.ieee.org/abstract/document/7447134/ ER - TY - CHAP A1 - Hammerschmidt, Martin A1 - Barth, Carlo A1 - Burger, Sven A1 - Becker, Christiane A1 - Schmidt, Frank T1 - Determining 2D photonic crystal geometries from reflectance spectra with a reduced basis method T2 - Proc. Europ. Opt. Soc. Ann. Meet. 2016 (EOSAM) Y1 - 2016 SN - 978-1-5108-4796-5 SP - 281 ER - TY - CHAP A1 - Barth, Carlo A1 - Roder, Sebastian A1 - Brodoceanu, Daniel A1 - Kraus, Tobias A1 - Burger, Sven A1 - Hammerschmidt, Martin A1 - Schmidt, Frank A1 - Becker, Christiane T1 - Increased fluorescence of PbS quantum dots on photonic crystal slab structures T2 - Proc. Europ. Opt. Soc. Ann. Meet. 2016 (EOSAM) Y1 - 2016 SN - 978-1-5108-4796-5 SP - 181 ER - TY - CHAP A1 - Zschiedrich, Lin A1 - Schmidt, Frank A1 - Pomplun, Jan A1 - Burger, Sven T1 - Finite elements for nano-optics applications T2 - Proc. Europ. Opt. Soc. Ann. Meet. 2016 (EOSAM) Y1 - 2016 SN - 978-1-5108-4796-5 SP - 178 ER - TY - CHAP A1 - Lassila, Antti A1 - Shpak, Maksim A1 - Vaigu, A. A1 - Smid, M. A1 - Velasco, A. A1 - Pokatilov, A. A1 - Fatadin, I. A1 - Ferguson, R. A1 - Robinson, D. A1 - Walbaum, T. A1 - Burger, Sven A1 - Haapalainen, Mertsi A1 - Vahimaa, P. A1 - Lukner, H. A1 - Pernice, W. A1 - Pitwon, R. A1 - Deninger, A. A1 - Morel, Jacques A1 - Castagna, Natascia A1 - Peters, O. A1 - Husu, H. T1 - Joint research for photonics fibres, waveguides and interconnects T2 - Proc. Europ. Opt. Soc. Ann. Meet. 2016 (EOSAM) Y1 - 2016 SN - 978-1-5108-4796-5 SP - 25 ER - TY - CHAP A1 - Scheffler, Daniel A1 - Sips, Mike A1 - Behling, Robert A1 - Dransch, Doris A1 - Eggert, Daniel A1 - Fajerski, Jan A1 - Freytag, Johann-Christoph A1 - Griffiths, Patrick A1 - Hollstein, André A1 - Hostert, Patrick A1 - Köthur, Patrick A1 - Peters, Mathias A1 - Pflugmacher, Dirk A1 - Rabe, Andreas A1 - Reinefeld, Alexander A1 - Schintke, Florian A1 - Segel, Karl T1 - Geomultisens – a common automatic processing and analysis system for multi-sensor satellite data T2 - Advancing Horizons for Land Cover Services Entering the Big Data Era, Second joint Workshop of the EARSeL Special Interest Group on Land Use & Land Cover and the NASA LCLUC Program Y1 - 2016 SP - 18 EP - 19 ER - TY - CHAP A1 - Yokoyama, Ryohei A1 - Nakamura, Ryo A1 - Wakui, Tetsuya A1 - Shinano, Yuji T1 - Evaluation of Performance Robustness of a Gas Turbine Cogeneration Plant Based on a Mixed-Integer Linear Model T2 - ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition N2 - In designing energy supply systems, designers are requested to rationally determine equipment types, capacities, and numbers in consideration of equipment operational strategies corresponding to seasonal and hourly variations in energy demands. However, energy demands have some uncertainty at the design stage, and the energy demands which become certain at the operation stage may differ from those estimated at the design stage. Therefore, designers should consider that energy demands have some uncertainty, evaluate the performance robustness against the uncertainty, and design the systems to heighten the robustness. Especially, this issue is important for cogeneration plants, because their performances depend significantly on both heat and power demands. Although robust optimal design methods of energy supply systems under uncertain energy demands were developed, all of them are based on linear models for energy supply systems. However, it is still a hard challenge to develop a robust optimal design method even based on a mixed-integer linear model. At the first step for this challenge, in this paper, a method of evaluating the performance robustness of energy supply systems under uncertain energy demands is proposed based on a mixed-integer linear model. This problem is formulated as a bilevel mixed-integer linear programming one, and a sequential solution method is applied to solve it approximately by discretizing uncertain energy demands within their intervals. In addition, a hierarchical optimization method in consideration of the hierarchical relationship between design and operation variables is applied to solve large scale problems efficiently. Through a case study on a gas turbine cogeneration plant for district energy supply, the validity and effectiveness of the proposed method and features of the performance robustness of the plant are clarified. Y1 - 2016 U6 - https://doi.org/doi:10.1115/GT2016-56217 VL - 3 SP - 1 EP - 9 ER - TY - CHAP A1 - Shao, Borong A1 - Conrad, Tim T1 - Epithelial Mesenchymal Transition Regulatory Network-based Feature Selection in Lung Cancer Prognosis Prediction T2 - Lecture Notes in Computer Science (LNCS) N2 - Feature selection technique is often applied in identifying cancer prognosis biomarkers. However, many feature selection methods are prone to over-fitting or poor biological interpretation when applied on biological high-dimensional data. Network-based feature selection and data integration approaches are proposed to identify more robust biomarkers. We conducted experiments to investigate the advantages of the two approaches using epithelial mesenchymal transition regulatory network, which is demonstrated as highly relevant to cancer prognosis. We obtained data from The Cancer Genome Atlas. Prognosis prediction was made using Support Vector Machine. Under our experimental settings, the results showed that network-based features gave significantly more accurate predictions than individual molecular features, and features selected from integrated data (RNA-Seq and micro-RNA data) gave significantly more accurate predictions than features selected from single source data (RNA-Seq data). Our study indicated that biological network-based feature transformation and data integration are two useful approaches to identify robust cancer biomarkers. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-31744-1_13 VL - 9656 SP - 1235 EP - 146 ER -