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    <title language="eng">Medical Image registration with B-Spline Transformation</title>
    <abstract language="eng">Medical image registration is an important task in various applications such as temporal analysis of medical images from a single modality (where structural or anatomical changes over time are studied), multi-modal image fusion (in which data acquired from different imaging devices or protocols is fused to help with diagnosis and treatment planning), or atlas matching (where uses normal anatomical variability). Establishing functional and/or spatial anatomical correspondences between different image acquisitions is the goal of image registration. As rigid alignment might be insufficient to describe variations of images in certain applications where deformations play a role, we should rely on non-rigid image registration. Non-rigid registration methods are capable of aligning images where correspondence cannot be achieved without localized deformations and can therefore better accommodate anatomical, physiological, and pathological variability between patients. Elastix image registration software is employed in this thesis to conduct our experiments.&#13;
&#13;
In this thesis, we use interpolation-based B-spline transformation models that parameterize a Free-Form Deformation Field. B-splines interpolate the dense deformation field from a given set of control points. B-spline transformation model assumes the smoothness and continuity of the deformation field on the image. However, when aligning two images that contain sliding contact, B-spline transformation leads to an inaccurate registration result, and the deformation field is not always smooth or the deformation field is always discontinuous, but not necessarily everywhere. When the knee is bent, the sliding contact occurs at the knee cartilage and around the capsular tissues. Different types of discontinuities in the overall deformation field may occur when sliding contact occurs. Imposing smoothness and continuity in the registration will generate artifacts in the consequent deformations, as well as any quantities derived from it. To handle the artifacts in the resulting deformations, one can use a regularizer or implement the sliding motion in the transformation model. Hence, in this thesis, we compare the B-spline registration, B-spline with Transform Rigidity Penalty, B-spline with Distance Rigidity Penalty, and the sliding motion with B-splines. B-spline with Transform Rigidity Penalty (TRP) and B-spline with Distance Rigidity Penalty (DPRP) models use a regularizer to handle the sliding motion. However, the sliding Motion with B-splines model handles sliding motion in the transformation model itself.&#13;
&#13;
The chosen registration parameters and registration algorithm were assessed first using three synthetic datasets. In a second validation, Knee MRI images that belong to two different patient datasets are registered on a temporal sequence of each patient. Registration accuracy was assessed based on the corresponding segmented bones and neighboring soft tissue. Compared to B-spline approach, the B-spline with TRP, B-spline with DPRP, and the sliding motion with B-splines gave deformation fields that represented more plausible image correspondences, while giving rise to comparable registration accuracy. In one dataset, the B-spline with TRP and B-spline with DPRP models reflected the deformation better than other approaches. However, sliding motion with B-splines model outperformed all other approaches in the other dataset.</abstract>
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    <advisor>Alexander Tack</advisor>
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    <title language="eng">Analysis of the NORA Anaphylaxis Database: Towards Identifying Severity Factors using Machine Learning</title>
    <abstract language="eng">Anaphylaxis is a severe allergic reaction, which occurs instantaneously with potential life-threatening symptoms by affecting different organ systems. The reaction could be triggered by an exposure to various types of allergens including foods, drugs, insects, venom and others. The European Anaphylaxis Registry (NORA) is a large database gathered to collect information on anaphylaxis cases through the allergy centers in European and other countries. This questionnaire-based survey data was designed in a way to provide information on patients’ demographic characteristics, eliciting allergens, symptoms, reaction severity, emergency treatment and long-term management of the disease and aims to improve the knowledge on reactions and provide treatment methods to affected patients.&#13;
&#13;
Unfortunately, the data is partly incomplete. However, incomplete data hinders some algorithms to work properly and may lead to misleading analysis results. Another issue regarding the dataset is, it contains a large number of variables, which introduces complex linear or/and non-linear relationships to the data and makes it difficult to reveal those relationships, which makes analyzing and driving conclusions from the data a challenging task.&#13;
&#13;
The problems mentioned above, leads us to the questions asked in this thesis. The fact that the data is incomplete raises the main question, which is whether and how it is possible to impute the missing data and how this affects subsequent steps, such as classification. The size and the complexity of the data raises the second question: Are there certain relationships within variables that can be used to predict the severity of the disease? In case there is, are there any potential augmentation factors, also called cofactors in the dataset that may increase the severity of a reaction to an allergen that would normally not occur when the cofactor does not exist?&#13;
&#13;
As explained, the goal of this thesis is to find answers to the questions indicated above. For that reason, initially, we searched for a suitable method for data imputation and compared performances of two different tree-based multiple imputation methods on the dataset. After obtaining multiple complete datasets, we tried to predict the severity of anaphylaxis from the dataset to determine hidden risk factors behind anaphylaxis using high power gradient boosting methods.&#13;
&#13;
It is concluded that, multiple imputation with chained equations (MICE) based on classification and regression trees can be useful for imputing NORA dataset. However, we could not detect enough associations between study variables and anaphylaxis severity in the data, to predict anaphylaxis severity from the dataset.</abstract>
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    <title language="eng">Modeling Functional Time Series and Mixed-Type Predictors With Partially Functional Autoregressions</title>
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    <title language="eng">Restarting Frank-Wolfe: Faster Rates under Hölderian Error Bounds</title>
    <parentTitle language="eng">Journal of Optimization Theory and Applications</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Thomas Kerdreux</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Alexandre d'Aspremont</author>
    <author>Sebastian Pokutta</author>
    <collection role="projects" number="no-project">no-project</collection>
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    <id>9697</id>
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    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Anti-Ramsey threshold of cycles for sparse graphs</title>
    <parentTitle language="eng">Discrete Applied Mathematics</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Gabriel F. Barros</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Bruno P. Cavalar</author>
    <author>Guilherme O. Mota</author>
    <author>Olaf Parczyk</author>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="parczyk">Parczyk, Olaf</collection>
  </doc>
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    <id>9711</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
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    <type>article</type>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Near optimal sparsity-constrained group testing: improved bounds</title>
    <parentTitle language="eng">IEEE Transactions on Information Theory</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Oliver Gebhard</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Max Hahn-Klimroth</author>
    <author>Manuel Penschuck</author>
    <author>Maurice Rolvien</author>
    <author>Jonathan Scarlett</author>
    <author>Nelvin Tan</author>
    <author>Olaf Parczyk</author>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="parczyk">Parczyk, Olaf</collection>
  </doc>
  <doc>
    <id>9714</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <type>article</type>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Triangles in randomly perturbed graphs</title>
    <parentTitle language="eng">Combinatorics, Probability and Computing</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Julia Böttcher</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Amedeo Sgueglia</author>
    <author>Jozef Skokan</author>
    <author>Olaf Parczyk</author>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="parczyk">Parczyk, Olaf</collection>
  </doc>
  <doc>
    <id>9659</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
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    <type>conferenceobject</type>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Discovering collective variable dynamics of agent-based models</title>
    <abstract language="eng">Analytical approximations of the macroscopic behavior of agent-based models (e.g.&#13;
via mean-field theory) often introduce a significant error, especially in the transient phase. For an example model called continuous-time noisy voter model, we use two data-driven approaches to learn the evolution of collective variables instead. The first approach utilizes the SINDy method to approximate the macroscopic dynamics without prior knowledge, but has proven itself to be not particularly robust. The second approach employs an informed learning strategy which includes knowledge about the agent-based model. Both approaches exhibit a considerably smaller error than the conventional analytical approximation.</abstract>
    <parentTitle language="eng">25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022</parentTitle>
    <identifier type="doi">https://doi.org/10.15495/EPub_UBT_00006809</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Marvin Lücke</author>
    <submitter>Stefanie Winkelmann</submitter>
    <author>Peter Koltai</author>
    <author>Stefanie Winkelmann</author>
    <author>Nora Molkethin</author>
    <author>Jobst Heitzig</author>
    <collection role="persons" number="winkelmann">Winkelmann, Stefanie</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="persons" number="luecke">Lücke, Marvin</collection>
    <collection role="projects" number="MathPlusEF4-8">MathPlusEF4-8</collection>
  </doc>
  <doc>
    <id>9487</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
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    <type>incollection</type>
    <publisherName/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Algorithm configuration problem</title>
    <parentTitle language="eng">Encyclopedia of Optimization</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Gabriele Iommazzo</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Claudia D'Ambrosio</author>
    <author>Antonio Frangioni</author>
    <author>Leo Liberti</author>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
  </doc>
  <doc>
    <id>9488</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>incollection</type>
    <publisherName/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generalized Nash equilibrium problems with partial differential operators: theory, algorithms, and risk aversion</title>
    <parentTitle language="eng">Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Deborah Gahururu</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Michael Hintermüller</author>
    <author>Steven-Marian Stengl</author>
    <author>Thomas M. Surowiec</author>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="stengl">Stengl, Steven-Marian</collection>
  </doc>
  <doc>
    <id>9491</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <type>article</type>
    <publisherName/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimizing a low-dimensional convex function over a high-dimensional cube</title>
    <parentTitle language="eng">SIAM Journal on Optimization</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Christoph Hunkenschröder</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Sebastian Pokutta</author>
    <author>Robert Weismantel</author>
    <collection role="persons" number="pokutta">Pokutta, Sebastian</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
  </doc>
  <doc>
    <id>9493</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Accelerated Riemannian optimization: Handling constraints with a prox to bound geometric penalties</title>
    <parentTitle language="eng">Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2022)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>David Martínez-Rubio</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Sebastian Pokutta</author>
    <collection role="persons" number="pokutta">Pokutta, Sebastian</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="projects" number="MathPlus-AA5-1">MathPlus-AA5-1</collection>
  </doc>
  <doc>
    <id>9443</id>
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    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">a variational inequality model for the construction of signals from inconsistent nonlinear equations</title>
    <parentTitle language="eng">SIAM Journal on Imaging Sciences</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>P. L. Combettes</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Zev Woodstock</author>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="woodstock">Woodstock, Zev</collection>
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  <doc>
    <id>9442</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">signal recovery from inconsistent nonlinear observations</title>
    <parentTitle language="eng">Proceedings of ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>P. L. Combettes</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Zev Woodstock</author>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="woodstock">Woodstock, Zev</collection>
  </doc>
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    <id>9440</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <type>article</type>
    <publisherName/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models</title>
    <parentTitle language="eng">Metabolic Engineering</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>St. Elmo Wilken</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Mathieu Besançon</author>
    <author>Miroslav Kratochvíl</author>
    <author>Chilperic Armel Foko Kuate</author>
    <author>Christophe Trefois</author>
    <author>Wei Gu</author>
    <author>Oliver Ebenhöh</author>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
  </doc>
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    <id>9434</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
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    <pageNumber/>
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    <type>conferenceobject</type>
    <publisherName/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Machine Learning for Combinatorial Optimization Competition (ML4CO): results and insights</title>
    <parentTitle language="eng">Proceedings of Conference on Neural Information Processing Systems</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Maxime Gasse</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Simon Bowly</author>
    <author>Quentin Cappart</author>
    <author>Jonas Charfreitag</author>
    <author>Laurent Charlin</author>
    <author>Didier Chételat</author>
    <author>Antonia Chmiela</author>
    <author>Justin Dumouchelle</author>
    <author>Ambros Gleixner</author>
    <author>Aleksandr M. Kazachkov</author>
    <author>Elias Khalil</author>
    <author>Pawel Lichocki</author>
    <author>Andrea Lodi</author>
    <author>Miles Lubin</author>
    <author>Chris J. Maddison</author>
    <author>Morris Christopher</author>
    <author>Dimitri J. Papageorgiou</author>
    <author>Augustin Parjadis</author>
    <author>Sebastian Pokutta</author>
    <author>Antoine Prouvost</author>
    <author>Lara Scavuzzo</author>
    <author>Giulia Zarpellon</author>
    <author>Linxin Yang</author>
    <author>Sha Lai</author>
    <author>Akang Wang</author>
    <author>Xiaodong Luo</author>
    <author>Xiang Zhou</author>
    <author>Haohan Huang</author>
    <author>Shengcheng Shao</author>
    <author>Yuanming Zhu</author>
    <author>Dong Zhang</author>
    <author>Tao Quan</author>
    <author>Zixuan Cao</author>
    <author>Yang Xu</author>
    <author>Zhewei Huang</author>
    <author>Shuchang Zhou</author>
    <author>Chen Binbin</author>
    <author>He Minggui</author>
    <author>Hao Hao</author>
    <author>Zhang Zhiyu</author>
    <author>An Zhiwu</author>
    <author>Mao Kun</author>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="persons" number="pokutta">Pokutta, Sebastian</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
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    <id>9430</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">block-activated algorithms for multicomponent fully nonsmooth minimization</title>
    <parentTitle language="eng">Proceedings of ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>M. N. Búi</author>
    <submitter>Christoph Spiegel</submitter>
    <author>P. L. Combettes</author>
    <author>Zev Woodstock</author>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="persons" number="woodstock">Woodstock, Zev</collection>
  </doc>
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    <id>9431</id>
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    <language>eng</language>
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    <pageLast/>
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    <type>article</type>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">a framework for generalized Benders' decomposition and its applications to multilevel optimization</title>
    <parentTitle language="eng">Mathematical Programming</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Suresh Bolusani</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Ted K. Ralphs</author>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
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