TY - THES A1 - Özmen, Mahmut T1 - Medical Image registration with B-Spline Transformation N2 - 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. 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. 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. Y1 - 2022 ER - TY - THES A1 - Mengüllüoglu, Ecegül T1 - Analysis of the NORA Anaphylaxis Database: Towards Identifying Severity Factors using Machine Learning N2 - 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. 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. 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? 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. 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. Y1 - 2022 ER - TY - JOUR A1 - Ramos, Alejandro A1 - Kühn, Oliver T1 - Manipulating the dynamics of a Fermi resonance with light. A direct optimal control theory approach JF - Chemical Physics Y1 - 2022 ER - TY - JOUR A1 - Xu, Xiaofei A1 - Chen, Ying A1 - Zhang, Ge A1 - Koch, Thorsten T1 - Modeling Functional Time Series and Mixed-Type Predictors With Partially Functional Autoregressions JF - Journal of Business & Economic Statistics Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1080/07350015.2021.2011299 SN - 0735-0015 VL - 42 IS - 2 SP - 349 EP - 366 PB - Informa UK Limited ER - TY - JOUR A1 - Hartzer, Jacob A1 - Röhrig, Olivia A1 - Wolff, Timo A1 - Yürük, Oğuzhan T1 - Initial steps in the classification of maximal mediated sets JF - Journal of Symbolic Computation Y1 - 2022 ER - TY - JOUR A1 - Kerdreux, Thomas A1 - d'Aspremont, Alexandre A1 - Pokutta, Sebastian T1 - Restarting Frank-Wolfe: Faster Rates under Hölderian Error Bounds JF - Journal of Optimization Theory and Applications Y1 - 2022 ER - TY - JOUR A1 - Barros, Gabriel F. A1 - Cavalar, Bruno P. A1 - Mota, Guilherme O. A1 - Parczyk, Olaf T1 - Anti-Ramsey threshold of cycles for sparse graphs JF - Discrete Applied Mathematics Y1 - 2022 ER - TY - JOUR A1 - Gebhard, Oliver A1 - Hahn-Klimroth, Max A1 - Penschuck, Manuel A1 - Rolvien, Maurice A1 - Scarlett, Jonathan A1 - Tan, Nelvin A1 - Parczyk, Olaf T1 - Near optimal sparsity-constrained group testing: improved bounds JF - IEEE Transactions on Information Theory Y1 - 2022 ER - TY - JOUR A1 - Böttcher, Julia A1 - Sgueglia, Amedeo A1 - Skokan, Jozef A1 - Parczyk, Olaf T1 - Triangles in randomly perturbed graphs JF - Combinatorics, Probability and Computing Y1 - 2022 ER - TY - CHAP A1 - Lücke, Marvin A1 - Koltai, Peter A1 - Winkelmann, Stefanie A1 - Molkethin, Nora A1 - Heitzig, Jobst T1 - Discovering collective variable dynamics of agent-based models T2 - 25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022 N2 - Analytical approximations of the macroscopic behavior of agent-based models (e.g. 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. Y1 - 2022 U6 - https://doi.org/https://doi.org/10.15495/EPub_UBT_00006809 ER - TY - GEN A1 - Iommazzo, Gabriele A1 - D'Ambrosio, Claudia A1 - Frangioni, Antonio A1 - Liberti, Leo T1 - Algorithm configuration problem T2 - Encyclopedia of Optimization Y1 - 2022 ER - TY - GEN A1 - Gahururu, Deborah A1 - Hintermüller, Michael A1 - Stengl, Steven-Marian A1 - Surowiec, Thomas M. T1 - Generalized Nash equilibrium problems with partial differential operators: theory, algorithms, and risk aversion T2 - Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization Y1 - 2022 ER - TY - JOUR A1 - Hunkenschröder, Christoph A1 - Pokutta, Sebastian A1 - Weismantel, Robert T1 - Optimizing a low-dimensional convex function over a high-dimensional cube JF - SIAM Journal on Optimization Y1 - 2022 ER - TY - CHAP A1 - Martínez-Rubio, David A1 - Pokutta, Sebastian T1 - Accelerated Riemannian optimization: Handling constraints with a prox to bound geometric penalties T2 - Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2022) Y1 - 2022 ER - TY - JOUR A1 - Combettes, P. L. A1 - Woodstock, Zev T1 - a variational inequality model for the construction of signals from inconsistent nonlinear equations JF - SIAM Journal on Imaging Sciences Y1 - 2022 ER - TY - CHAP A1 - Combettes, P. L. A1 - Woodstock, Zev T1 - signal recovery from inconsistent nonlinear observations T2 - Proceedings of ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Y1 - 2022 ER - TY - JOUR A1 - Wilken, St. Elmo A1 - Besançon, Mathieu A1 - Kratochvíl, Miroslav A1 - Kuate, Chilperic Armel Foko A1 - Trefois, Christophe A1 - Gu, Wei A1 - Ebenhöh, Oliver T1 - Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models JF - Metabolic Engineering Y1 - 2022 ER - TY - CHAP A1 - Gasse, Maxime A1 - Bowly, Simon A1 - Cappart, Quentin A1 - Charfreitag, Jonas A1 - Charlin, Laurent A1 - Chételat, Didier A1 - Chmiela, Antonia A1 - Dumouchelle, Justin A1 - Gleixner, Ambros A1 - Kazachkov, Aleksandr M. A1 - Khalil, Elias A1 - Lichocki, Pawel A1 - Lodi, Andrea A1 - Lubin, Miles A1 - Maddison, Chris J. A1 - Christopher, Morris A1 - Papageorgiou, Dimitri J. A1 - Parjadis, Augustin A1 - Pokutta, Sebastian A1 - Prouvost, Antoine A1 - Scavuzzo, Lara A1 - Zarpellon, Giulia A1 - Yang, Linxin A1 - Lai, Sha A1 - Wang, Akang A1 - Luo, Xiaodong A1 - Zhou, Xiang A1 - Huang, Haohan A1 - Shao, Shengcheng A1 - Zhu, Yuanming A1 - Zhang, Dong A1 - Quan, Tao A1 - Cao, Zixuan A1 - Xu, Yang A1 - Huang, Zhewei A1 - Zhou, Shuchang A1 - Binbin, Chen A1 - Minggui, He A1 - Hao, Hao A1 - Zhiyu, Zhang A1 - Zhiwu, An A1 - Kun, Mao T1 - The Machine Learning for Combinatorial Optimization Competition (ML4CO): results and insights T2 - Proceedings of Conference on Neural Information Processing Systems Y1 - 2022 ER - TY - CHAP A1 - Búi, M. N. A1 - Combettes, P. L. A1 - Woodstock, Zev T1 - block-activated algorithms for multicomponent fully nonsmooth minimization T2 - Proceedings of ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Y1 - 2022 ER - TY - JOUR A1 - Bolusani, Suresh A1 - Ralphs, Ted K. T1 - a framework for generalized Benders' decomposition and its applications to multilevel optimization JF - Mathematical Programming Y1 - 2022 ER -