TY - JOUR A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven T1 - Cover Picture: Resonance Expansion of Quadratic Quantities with Regularized Quasinormal Modes JF - Phys. Status Solidi A Y1 - 2023 U6 - https://doi.org/10.1002/pssa.202370013 VL - 220 SP - 2370013 ER - TY - THES A1 - Bautz, Lisa T1 - Unsupervised Shape Correspondence Estimation for Anatomical Shapes N2 - The concept of shape correspondence describes a relation between two or more shapes of the same class. It often consists of a mapping between points on semantically similar locations of all shapes. One possible application for shape correspondence in medicine is the automatic location of anatomical landmarks. Another popular application is the construction of statistical shape models. These models are an established way to represent geometric variation of anatomical shapes in a compact way. Possible applications range from the generation of shapes and reconstruction tasks to disease classification. This thesis aims to investigate unsupervised methods that can be used to estimate such a correspondence on anatomical shapes. While most methods used in the medical domain focus on classical optimization algorithms to establish correspondence, the broader computer vision domain developed a versatile field of data-driven methods. Recently, the new shape model FlowSSM was introduced, which does not require predefined correspondences for training as it generates them itself. As the performance of the shape model is quite competitive, it is natural to assume that the generated correspondences are of high quality as well. For this reason, we evaluate the quality of the correspondences generated by FlowSSM within this thesis. Furthermore, we modify the method by adding a second loss term that minimizes geodesic distortions. This is done to favor isometric deformations which can lead to better correspondences. We compare the results with two established methods from the medical domain, LDDMM and Meshmonk. Furthermore, we investigate the performance of a fourth method called Neuromoph. This data-driven method comes from the wider computer vision field and was not tested on anatomical data yet. All methods are evaluated with a set of different metrics. This includes metrics to assess the quality of the resulting meshes, a sparse correspondence error on anatomical landmarks, and metrics to measure the quality of the resulting shape models. Furthermore, we test all methods on three datasets with different degrees of geometric variation, namely liver, distal femur and face. We show that FlowSSM produces correspondences with state-of-the-art quality. Moreover, our modification further improved the quality of correspondences at a global level. Nevertheless, there is no clear ranking between all methods, as the results differ between metrics and datasets. Thereby, we can show that there are different qualities to a proper correspondence which are reflected in the different metrics. It is therefore strongly recommendable to choose a correspondence estimation method specifically for the problem at hand. N2 - Das Konzept der Formkorrespondenz zwischen 3D-Objekten einer Klasse beschreibt eine Beziehung zwischen den Instanzen (oft Punkten) der unterschiedlichen Objekten. Hierbei werden Punkte, die an semantisch gleichwertigen Orten liegen, miteinander in Verbindung gebracht. Eine mögliche Anwednung der Formkorrespondenz im medizinischen Bereich ist daher die automatisierte Lokalisierung von anatomischen Landmarken. Eine weitere Anwendung ist das Erstellen von statistischen Formmodellen. Mit diesen kann die geometrische Variation anatomischer Formen kompakt abgebildet werden. Medizinische Anwendungen reichen dabei von der einfachen Formgenerierung zu komplexeren Rekonstruktionsaufgaben und der Klassifizierung von gesunden und pathologischen Formen. In dieser Arbeit werden unterschiedliche Methoden zur Erzeugung von Formkorrespondenzen untersucht. Die entsprechende Literatur im medizinischen Bereich verwendet hierzu meist Methoden, die das klassische Optimierungsproblem einer nichtrigiden Transformation lösen. Im Computer Vision Bereich wurden in den letzten Jahren auch einige datengetriebene Methoden zur Korrespondenzgenerierung veröffentlicht. Im letzten Jahr wurde außerdem die Methode FlowSSM zur Erstellung statistischer Formmodelle vorgestellt, die nicht auf korrespondierenden Oberflächen basiert, sondern diese selbst erzeugt. Da FlowSSM trotzdem konkurenzfähige Ergebnisse erzielt, ist naheliegend, dass auch die zugrundeliegenden, selbst generierten Korrespondenzen von hoher Qualität sind. Innerhalb dieser Arbeit wird daher die Qualität der von FlowSSM erzeugten Korrespondenzen evaluiert. Außerdem wird die Methode um eine zusätzliche Kostenfunktion erweitert, die geod#tische Verzerrungen verhindern soll. Dadurch sollen nichtisometrische Deformationen vermieden werden, wodurch die Qualität der resultierenden Korrenspondenzen gesteigert werden kann. Die Ergebnisse von FlowSSM werden mit zwei etablierten Methoden aus dem medizinischen Bereich, LDDMM und Meshmonk, verglichen. Außerdem wird NeuroMorph, eine aktuelle, datengetriebene Methode aus dem Bereich des maschinellen Sehens getestet. Letztere wurde bisher noch nicht auf medizinischen Daten evaluiert. Die Bewertung aller generierten Korrespondenzen basiert auf ausgewählten indirekten Metriken. Hierzu gehört auch die Performance bei konkreten Anwendungsfällen wie der Lokalisierung von Landmarken und dem Erstellen von statistischen Formmodellen. Im Rahmen der Arbeit wird gezeigt, dass FlowSSM Korrespondenzen produziert, deren Qualität dem aktuellen State-of-the-art entspricht. Durch das Hinzufügen der zweiten Kostenfunktion wird die Qualität der Korrespondenzen auf einem globalen Level noch weiter gesteigert. Prinzipiell lässt sich jedoch keine Hierarchie zwischen den Methoden ableiten, da die Performance stark innerhalb der untersuchten Metriken und Datensätzen schwankt. Die Auswahl einer passenden Methode sollte sich daher vor allem am Anwendungsfall orientieren. Y1 - 2023 ER - TY - CHAP A1 - Kuen, Lilli A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Heermeier, Niels A1 - Rodt, Sven A1 - Reitzenstein, Stephan A1 - Burger, Sven T1 - Applying a Riesz-projection-based contour integral eigenvalue solver to compute resonance modes of a VCSEL T2 - Proc. SPIE Y1 - 2023 U6 - https://doi.org/10.1117/12.2665490 VL - 12575 SP - 125750J ER - TY - JOUR A1 - Fogalli, Giovani Bressan A1 - Peres Line, Sérgio Roberto A1 - Baum, Daniel T1 - Segmentation of tooth enamel microstructure images using classical image processing and U-Net approaches JF - Frontiers in Imaging N2 - Tooth enamel is the hardest tissue in human organism, formed by prism layers in regularly alternating directions. These prisms form the Hunter-Schreger Bands (HSB) pattern when under side illumination, which is composed of light and dark stripes resembling fingerprints. We have shown in previous works that HSB pattern is highly variable, seems to be unique for each tooth and can be used as a biometric method for human identification. Since this pattern cannot be acquired with sensors, the HSB region in the digital photograph must be identified and correctly segmented from the rest of the tooth and background. Although these areas can be manually removed, this process is not reliable as excluded areas can vary according to the individual‘s subjective impression. Therefore, the aim of this work was to develop an algorithm that automatically selects the region of interest (ROI), thus, making the entire biometric process straightforward. We used two different approaches: a classical image processing method which we called anisotropy-based segmentation (ABS) and a machine learning method known as U-Net, a fully convolutional neural network. Both approaches were applied to a set of extracted tooth images. U-Net with some post processing outperformed ABS in the segmentation task with an Intersection Over Union (IOU) of 0.837 against 0.766. Even with a small dataset, U-Net proved to be a potential candidate for fully automated in-mouth application. However, the ABS technique has several parameters which allow a more flexible segmentation with interactive adjustments specific to image properties. Y1 - 2023 U6 - https://doi.org/10.3389/fimag.2023.1215764 VL - 2 ER - TY - JOUR A1 - Bai, Mingze A1 - Deng, Jingwen A1 - Dai, Chengxin A1 - Pfeuffer, Julianus A1 - Sachsenberg, Timo A1 - Perez-Riverol, Yasset T1 - LFQ-Based Peptide and Protein Intensity Differential Expression Analysis JF - J. Proteome Res. N2 - Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings. Y1 - 2023 U6 - https://doi.org/10.1021/acs.jproteome.2c00812 VL - 22 IS - 6 SP - 2114 EP - 2123 PB - American Chemical Society ER - TY - JOUR A1 - Kontou, Eftychia E. A1 - Walter, Axel A1 - Alka, Oliver A1 - Pfeuffer, Julianus A1 - Sachsenberg, Timo A1 - Mohite, Omkar A1 - Nuhamunanda, Matin A1 - Kohlbacher, Oliver A1 - Weber, Tilmann T1 - UmetaFlow: An untargeted metabolomics workflow for high-throughput data processing and analysis JF - Journal of Cheminformatics N2 - Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. Y1 - 2023 U6 - https://doi.org/10.1186/s13321-023-00724-w VL - 15 ER - TY - JOUR A1 - Paltra, Sydney A1 - Conrad, Tim T1 - Clinical Effectiveness of Ritonavir-Boosted Nirmatrelvir—A Literature Review JF - Advances in Respiratory Medicine N2 - Nirmatrelvir/Ritonavir is an oral treatment for mild to moderate COVID-19 cases with a high risk for a severe course of the disease. For this paper, a comprehensive literature review was performed, leading to a summary of currently available data on Nirmatrelvir/Ritonavir’s ability to reduce the risk of progressing to a severe disease state. Herein, the focus lies on publications that include comparisons between patients receiving Nirmatrelvir/Ritonavir and a control group. The findings can be summarized as follows: Data from the time when the Delta-variant was dominant show that Nirmatrelvir/Ritonavir reduced the risk of hospitalization or death by 88.9% for unvaccinated, non-hospitalized high-risk individuals. Data from the time when the Omicron variant was dominant found decreased relative risk reductions for various vaccination statuses: between 26% and 65% for hospitalization. The presented papers that differentiate between unvaccinated and vaccinated individuals agree that unvaccinated patients benefit more from treatment with Nirmatrelvir/Ritonavir. However, when it comes to the dependency of potential on age and comorbidities, further studies are necessary. From the available data, one can conclude that Nirmatrelvir/Ritonavir cannot substitute vaccinations; however, its low manufacturing cost and easy administration make it a valuable tool in fighting COVID-19, especially for countries with low vaccination rates. Y1 - 2024 U6 - https://doi.org/10.3390/arm92010009 VL - 92 IS - 1 ER - TY - THES A1 - Pfeuffer, Julianus T1 - Computational Methods for Protein Inference in Shotgun Proteomics Experiments N2 - Since the beginning of this millennium, the advent of high-throughput methods in numerous fields of the life sciences led to a shift in paradigms. A broad variety of technologies emerged that allow comprehensive quantification of molecules involved in biological processes. Simultaneously, a major increase in data volume has been recorded with these techniques through enhanced instrumentation and other technical advances. By supplying computational methods that automatically process raw data to obtain biological information, the field of bioinformatics plays an increasingly important role in the analysis of the ever-growing mass of data. Computational mass spectrometry in particular, is a bioinformatics field of research which provides means to gather, analyze and visualize data from high-throughput mass spectrometric experiments. For the study of the entirety of proteins in a cell or an environmental sample, even current techniques reach limitations that need to be circumvented by simplifying the samples subjected to the mass spectrometer. These pre-digested (so-called bottom-up) proteomics experiments then pose an even bigger computational burden during analysis since complex ambiguities need to be resolved during protein inference, grouping and quantification. In this thesis, we present several developments in the pursuit of our goal to provide means for a fully automated analysis of complex and large-scale bottom-up proteomics experiments. Firstly, due to prohibitive computational complexities in state-of-the-art Bayesian protein inference techniques, a refined, more stable technique for performing inference on sums of random variables was developed to enable a variation of standard Bayesian inference for the problem. nextflow and part of a set of standardized, well-tested, and community-maintained workflows by the nf-core collective. Our workflow runs on large-scale data with complex experimental designs and allows a one-command analysis of local and publicly available data sets with state-of-the-art accuracy on various high-performance computing environments or the cloud. Y1 - 2023 UR - http://hdl.handle.net/10900/142644 ER - TY - CHAP A1 - Bestuzheva, Ksenia A1 - Gleixner, Ambros A1 - Achterberg, Tobias T1 - Efficient Separation of RLT Cuts for Implicit and Explicit Bilinear Products T2 - Integer Programming and Combinatorial Optimization. IPCO 2023. N2 - The reformulation-linearization technique (RLT) is a prominent approach to constructing tight linear relaxations of non-convex continuous and mixed-integer optimization problems. The goal of this paper is to extend the applicability and improve the performance of RLT for bilinear product relations. First, a method for detecting bilinear product relations implicitly contained in mixed-integer linear programs is developed based on analyzing linear constraints with binary variables, thus enabling the application of bilinear RLT to a new class of problems. Our second contribution addresses the high computational cost of RLT cut separation, which presents one of the major difficulties in applying RLT efficiently in practice. We propose a new RLT cutting plane separation algorithm which identifies combinations of linear constraints and bound factors that are expected to yield an inequality that is violated by the current relaxation solution. A detailed computational study based on implementations in two solvers evaluates the performance impact of the proposed methods. Y1 - 2023 U6 - https://doi.org/10.1007/978-3-031-32726-1_2 VL - 13904 SP - 14 EP - 28 PB - Springer, Cham ER - TY - CHAP A1 - Mexi, Gioni A1 - Berthold, Timo A1 - Gleixner, Ambros A1 - Nordström, Jakob T1 - Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning T2 - 29th International Conference on Principles and Practice of Constraint Programming (CP 2023) N2 - Conflict analysis has been successfully generalized from Boolean satisfiability (SAT) solving to mixed integer programming (MIP) solvers, but although MIP solvers operate with general linear inequalities, the conflict analysis in MIP has been limited to reasoning with the more restricted class of clausal constraint. This is in contrast to how conflict analysis is performed in so-called pseudo-Boolean solving, where solvers can reason directly with 0-1 integer linear inequalities rather than with clausal constraints extracted from such inequalities. In this work, we investigate how pseudo-Boolean conflict analysis can be integrated in MIP solving, focusing on 0-1 integer linear programs (0-1 ILPs). Phrased in MIP terminology, conflict analysis can be understood as a sequence of linear combinations and cuts. We leverage this perspective to design a new conflict analysis algorithm based on mixed integer rounding (MIR) cuts, which theoretically dominates the state-of-the-art division-based method in pseudo-Boolean solving. We also report results from a first proof-of-concept implementation of different pseudo-Boolean conflict analysis methods in the open-source MIP solver SCIP. When evaluated on a large and diverse set of 0-1 ILP instances from MIPLIB2017, our new MIR-based conflict analysis outperforms both previous pseudo-Boolean methods and the clause-based method used in MIP. Our conclusion is that pseudo-Boolean conflict analysis in MIP is a promising research direction that merits further study, and that it might also make sense to investigate the use of such conflict analysis to generate stronger no-goods in constraint programming. Y1 - 2023 U6 - https://doi.org/10.4230/LIPIcs.CP.2023.27 VL - 280 SP - 27:1 EP - 27:19 PB - Schloss Dagstuhl - Leibniz-Zentrum für Informatik ER - TY - JOUR A1 - Bestuzheva, Ksenia A1 - Gleixner, Ambros A1 - Vigerske, Stefan T1 - A Computational Study of Perspective Cuts JF - Mathematical Programming Computation N2 - The benefits of cutting planes based on the perspective function are well known for many specific classes of mixed-integer nonlinear programs with on/off structures. However, we are not aware of any empirical studies that evaluate their applicability and computational impact over large, heterogeneous test sets in general-purpose solvers. This paper provides a detailed computational study of perspective cuts within a linear programming based branch-and-cut solver for general mixed-integer nonlinear programs. Within this study, we extend the applicability of perspective cuts from convex to nonconvex nonlinearities. This generalization is achieved by applying a perspective strengthening to valid linear inequalities which separate solutions of linear relaxations. The resulting method can be applied to any constraint where all variables appearing in nonlinear terms are semi-continuous and depend on at least one common indicator variable. Our computational experiments show that adding perspective cuts for convex constraints yields a consistent improvement of performance, and adding perspective cuts for nonconvex constraints reduces branch-and-bound tree sizes and strengthens the root node relaxation, but has no significant impact on the overall mean time. Y1 - 2023 U6 - https://doi.org/10.1007/s12532-023-00246-4 VL - 15 SP - 703 EP - 731 ER - TY - GEN A1 - Binkowski, Felix A1 - Betz, Fridtjof A1 - Colom, Remi A1 - Genevet, Patrice A1 - Burger, Sven T1 - Source code and simulation results: Poles and zeros of electromagnetic quantities in photonic systems T2 - Zenodo Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.8063931 SP - 8063931 ER - TY - JOUR A1 - Binkowski, Felix A1 - Betz, Fridtjof A1 - Colom, Remi A1 - Genevet, Patrice A1 - Burger, Sven T1 - Poles and zeros in non-Hermitian systems: Application to photonics JF - Phys. Rev. B Y1 - 2024 U6 - https://doi.org/10.1103/PhysRevB.109.045414 VL - 109 SP - 045414 ER - TY - JOUR A1 - Plock, Matthias A1 - Hammerschmidt, Martin A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel A1 - Schütte, Christof T1 - Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions JF - Metrologia N2 - In optical nano metrology numerical models are used widely for parameter reconstructions. Using the Bayesian target vector optimization method we fit a finite element numerical model to a Grazing Incidence x-ray fluorescence data set in order to obtain the geometrical parameters of a nano structured line grating. Gaussian process, stochastic machine learning surrogate models, were trained during the reconstruction and afterwards sampled with a Markov chain Monte Carlo sampler to determine the distribution of the reconstructed model parameters. The numerical discretization parameters of the used finite element model impact the numerical discretization error of the forward model. We investigated the impact of the polynomial order of the finite element ansatz functions on the reconstructed parameters as well as on the model parameter distributions. We showed that such a convergence study allows to determine numerical parameters which allows for efficient and accurate reconstruction results. Y1 - 2023 U6 - https://doi.org/10.1088/1681-7575/ace4cd VL - 60 SP - 054001 ER - TY - GEN A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Burger, Sven T1 - RPExpand (Version 1) T2 - Zenodo Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.7840116 SP - doi: 10.5281/zenodo.7840116 ER - TY - CHAP A1 - Sekulic, Ivan A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Schaible, Jonas A1 - Burger, Sven T1 - Physics informed Bayesian optimization for inverse design of diffractive optical elements T2 - Proc. SPIE Y1 - 2025 U6 - https://doi.org/10.1117/12.3064372 VL - PC13573 SP - PC135730R ER - TY - CHAP A1 - Hammerschmidt, Martin A1 - Plock, Matthias A1 - Burger, Sven A1 - Truong, Vinh A1 - Soltwisch, Victor A1 - Schneider, Philipp-Immanuel T1 - Machine learning approach for full Bayesian parameter reconstruction T2 - Proc. SPIE Y1 - 2025 U6 - https://doi.org/10.1117/12.3062268 VL - 13568 SP - 1356806 ER - TY - CHAP A1 - Xu, Liding A1 - Mexi, Gioni A1 - Bestuzheva, Ksenia T1 - Sparsity-driven Aggregation of Mixed Integer Programs T2 - 23rd International Symposium on Experimental Algorithms (SEA 2025) Y1 - 2025 U6 - https://doi.org/10.4230/LIPIcs.SEA.2025.27 VL - 338 SP - 27:1 EP - 27:15 CY - Schloss Dagstuhl – Leibniz-Zentrum für Informatik ER - TY - JOUR A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Self-supervised pre-training with joint-embedding predictive architecture boosts ECG classification performance JF - Computers in Biology and Medicine N2 - Accurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated datasets, which are difficult and costly to collect. To address this issue, transfer learning is often employed, where models are pre-trained on large datasets and fine-tuned for specific ECG classification tasks with limited labeled data. Self-supervised learning has become a widely adopted pre-training method, enabling models to learn meaningful representations from unlabeled datasets. In this work, we explore the joint-embedding predictive architecture (JEPA) for self-supervised learning from ECG data. Unlike invariance-based methods, JEPA does not rely on hand-crafted data augmentations, and unlike generative methods, it predicts latent features rather than reconstructing input data. We create a large unsupervised pre-training dataset by combining ten public ECG databases, amounting to over one million records. We pre-train Vision Transformers using JEPA on this dataset and fine-tune them on various PTB-XL benchmarks. Our results show that JEPA outperforms existing invariance-based and generative approaches, achieving an AUC of 0.945 on the PTB-XL all statements task. JEPA consistently learns the highest quality representations, as demonstrated in frozen evaluations, and proves advantageous for pre-training even in the absence of additional data. Y1 - 2025 U6 - https://doi.org/10.1016/j.compbiomed.2025.110809 SN - 0010-4825 VL - 196 PB - Elsevier BV ER - TY - JOUR A1 - Binkowski, Felix A1 - Koulas-Simos, Aris A1 - Betz, Fridtjof A1 - Plock, Matthias A1 - Sekulic, Ivan A1 - Manley, Phillip A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Munkhbat, Battulga A1 - Reitzenstein, Stephan A1 - Burger, Sven T1 - High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers JF - Phys. Rev. B Y1 - 2025 U6 - https://doi.org/10.1103/nxh9-dhvx VL - 112 SP - 235410 ER - TY - GEN A1 - Binkowski, Felix A1 - Koulas-Simos, Aris A1 - Betz, Fridtjof A1 - Plock, Matthias A1 - Sekulic, Ivan A1 - Manley, Phillip A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Munkhbat, Battulga A1 - Reitzenstein, Stephan A1 - Burger, Sven T1 - Source code and simulation results: High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers T2 - Zenodo Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16533803 ER - TY - JOUR A1 - Bartoli, Adrien A1 - Sengupta, Agniva T1 - Camera Pose in SfT and NRSfM under Isometric and Weaker Deformation Models JF - Computer Vision and Image Understanding N2 - Camera pose is a very natural concept in 3D vision in the rigid setting. It is however much more difficult to work with in deformable settings. Consequently, numerous deformable reconstruction methods simply ignore camera pose. We analyse the concept of pose in deformable settings and prove that it is unconstrained with the existing formulations, properly justifying the existing pose-less methods reconstructing structure only. We explain this result intuitively by the impossibility to define an intrinsic coordinate frame to a general deforming object. The proposed analysis uses the isometric deformation model and extends to the weaker models including conformality and equiareality. We propose a novel prior to rescue camera pose estimation in deformable settings, which attributes the deforming object’s dominant rigid-body motion to the camera. We show that adding this prior to any existing formulation fully constrains camera pose and leads to elegant two-step solution methods, involving deformable structure reconstruction using a base method in the first step, and absolute orientation or Procrustes analysis in the second step. We derive the proposed approach for the template-based and template-less settings, respectively implemented using Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) as base methods, and validate them experimentally, showing that the computed pose is qualitatively and quantitatively plausible. Y1 - 2025 U6 - https://doi.org/10.1016/j.cviu.2025.104488 VL - 261 ER - TY - THES A1 - Amiranashvili, Tamaz T1 - Universal and Expressive Statistical Shape Models for Anatomical Structures N2 - Form and function of anatomical structures are intimately linked. Pathological changes in form can be associated with the loss of function. For example, diseases often cause characteristic shape changes, making shape a sensitive structural biomarker for medical diagnosis. If the link between form and function is causal, correcting a pathological shape can even restore the healthy function of an organ. Accurate shape reconstruction is then crucial for effective, patient-specific treatment planning. This demonstrates the importance of shape in clinical interventions and its potential to improve overall patient outcomes. Statistical shape models are computational methods that capture shape variations in a given population and enable precise shape analysis and generation. We focus on two key properties of a good statistical shape model. First, it should be easy to construct, and second, it should accurately represent the underlying shape distribution. Established existing approaches can only be constructed from surfaces with pre-defined dense correspondence. Such correspondence is tedious to obtain, can introduce undesired biases, and prevents training on partial or sparse observations. While correspondence-free methods exist, they struggle to accurately capture shape distributions with intricate details and large variations. In this thesis, we develop shape models that simplify training and improve accuracy over state-of-the-art. To achieve these goals, we build on approximately diffeomorphic neural deformations and implicit neural representations. First, our proposed methods are trainable on correspondence-free surfaces and even partial segmentations with large slice distances. This makes them universal since they can be trained on heterogeneous data, enabling scalability to large datasets and avoiding potential biases of pre-defined correspondence. Second, our methods are highly expressive, accurately capturing intricate shape details in complex distributions. We evaluate effectiveness of our models on multiple anatomical structures, outperforming established baselines in both generative and discriminative settings. Y1 - 2025 UR - https://mediatum.ub.tum.de/?id=1776778 UR - https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20250717-1776778-0-4 ER - TY - JOUR A1 - Xie, Kunpeng A1 - Gruber, Lennart Johannes A1 - Crampen, Martin A1 - Li, Yao A1 - Ferreira, André A1 - Tappeiner, Elias A1 - Gillot, Maxime A1 - Schepers, Jan A1 - Xu, Jiangchang A1 - Pankert, Tobias A1 - Beyer, Michel A1 - Shahamiri, Negar A1 - ten Brink, Reinier A1 - Dot, Gauthier A1 - Weschke, Charlotte A1 - van Nistelrooij, Niels A1 - Verhelst, Pieter-Jan A1 - Guo, Yan A1 - Xu, Zhibin A1 - Bienzeisler, Jonas A1 - Rashad, Ashkan A1 - Flügge, Tabea A1 - Cotton, Ross A1 - Vinayahalingam, Shankeeth A1 - Ilesan, Robert A1 - Raith, Stefan A1 - Madsen, Dennis A1 - Seibold, Constantin A1 - Xi, Tong A1 - Bergé, Stefaan A1 - Nebelung, Sven A1 - Kodym, Oldřich A1 - Sundqvist, Osku A1 - Thieringer, Florian A1 - Lamecker, Hans A1 - Coppens, Antoine A1 - Potrusil, Thomas A1 - Kraeima, Joep A1 - Witjes, Max A1 - Wu, Guomin A1 - Chen, Xiaojun A1 - Lambrechts, Adriaan A1 - Cevidanes, Lucia H Soares A1 - Zachow, Stefan A1 - Hermans, Alexander A1 - Truhn, Daniel A1 - Alves, Victor A1 - Egger, Jan A1 - Röhrig, Rainer A1 - Hölzle, Frank A1 - Puladi, Behrus T1 - Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems JF - Expert Systems With Applications N2 - Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare. Y1 - 2025 UR - https://www.medrxiv.org/content/10.1101/2025.06.11.25329022v1 U6 - https://doi.org/10.1016/j.eswa.2025.130031 VL - 299 IS - Part D ER - TY - CHAP A1 - Schneider, Philipp-Immanuel A1 - Sekulic, Ivan A1 - Plock, Matthias A1 - Hammerschmidt, Martin A1 - Rodt, Sven A1 - Reitzenstein, Stephan A1 - Burger, Sven T1 - Physics-informed Bayesian optimization of nanophotonic devices T2 - Advanced Photonics Congress Y1 - 2025 U6 - https://doi.org/10.1364/IPRSN.2025.ITu1A.2 VL - IPRSN SP - ITu1A.2 ER - TY - JOUR A1 - Tillmann, Peter A1 - Bläsi, Benedikt A1 - Burger, Sven A1 - Hammerschmidt, Martin A1 - Höhn, Oliver A1 - Becker, Christiane A1 - Jäger, Klaus T1 - Optimizing metal grating back reflectors for III-V-on-silicon multijunction solar cells JF - Opt. Express Y1 - 2021 U6 - https://doi.org/10.1364/OE.426761 VL - 29 SP - 22517 ER - TY - JOUR A1 - Plock, Matthias A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel T1 - Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology JF - Proc. SPIE Y1 - 2021 U6 - https://doi.org/10.1117/12.2592266 VL - 11783 SP - 117830J ER - TY - JOUR A1 - Gemander, Patrick A1 - Chen, Wei-Kun A1 - Weninger, Dieter A1 - Gottwald, Leona A1 - Gleixner, Ambros T1 - Two-row and two-column mixed-integer presolve using hashing-based pairing methods JF - EURO Journal on Computational Optimization N2 - In state-of-the-art mixed-integer programming solvers, a large array of reduction techniques are applied to simplify the problem and strengthen the model formulation before starting the actual branch-and-cut phase. Despite their mathematical simplicity, these methods can have significant impact on the solvability of a given problem. However, a crucial property for employing presolve techniques successfully is their speed. Hence, most methods inspect constraints or variables individually in order to guarantee linear complexity. In this paper, we present new hashing-based pairing mechanisms that help to overcome known performance limitations of more powerful presolve techniques that consider pairs of rows or columns. Additionally, we develop an enhancement to one of these presolve techniques by exploiting the presence of set-packing structures on binary variables in order to strengthen the resulting reductions without increasing runtime. We analyze the impact of these methods on the MIPLIB 2017 benchmark set based on an implementation in the MIP solver SCIP. Y1 - 2020 U6 - https://doi.org/10.1007/s13675-020-00129-6 VL - 8 IS - 3-4 SP - 205 EP - 240 ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Husseini, Malek E. A1 - Bayat, Amirhossein A1 - Löffler, Maximilian A1 - Liebl, Hans A1 - Li, Hongwei A1 - Tetteh, Giles A1 - Kukačka, Jan A1 - Payer, Christian A1 - Štern, Darko A1 - Urschler, Martin A1 - Chen, Maodong A1 - Cheng, Dalong A1 - Lessmann, Nikolas A1 - Hu, Yujin A1 - Wang, Tianfu A1 - Yang, Dong A1 - Xu, Daguang A1 - Ambellan, Felix A1 - Amiranashvili, Tamaz A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Lehnert, Sebastian A1 - Lirio, Marilia A1 - de Olaguer, Nicolás Pérez A1 - Ramm, Heiko A1 - Sahu, Manish A1 - Tack, Alexander A1 - Zachow, Stefan A1 - Jiang, Tao A1 - Ma, Xinjun A1 - Angerman, Christoph A1 - Wang, Xin A1 - Brown, Kevin A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie A1 - Chen, Di A1 - Bai, Yiwei A1 - Rapazzo, Brandon H. A1 - Yeah, Timyoas A1 - Zhang, Amber A1 - Xu, Shangliang A1 - Hou, Feng A1 - He, Zhiqiang A1 - Zeng, Chan A1 - Xiangshang, Zheng A1 - Liming, Xu A1 - Netherton, Tucker J. A1 - Mumme, Raymond P. A1 - Court, Laurence E. A1 - Huang, Zixun A1 - He, Chenhang A1 - Wang, Li-Wen A1 - Ling, Sai Ho A1 - Huynh, Lê Duy A1 - Boutry, Nicolas A1 - Jakubicek, Roman A1 - Chmelik, Jiri A1 - Mulay, Supriti A1 - Sivaprakasam, Mohanasankar A1 - Paetzold, Johannes C. A1 - Shit, Suprosanna A1 - Ezhov, Ivan A1 - Wiestler, Benedikt A1 - Glocker, Ben A1 - Valentinitsch, Alexander A1 - Rempfler, Markus A1 - Menze, Björn H. A1 - Kirschke, Jan S. T1 - VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images JF - Medical Image Analysis N2 - Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse. Y1 - 2021 U6 - https://doi.org/10.1016/j.media.2021.102166 VL - 73 ER - TY - GEN A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Burger, Sven T1 - RPExpand: Software for Riesz projection expansion of resonance phenomena [Source Code] Y1 - 2021 U6 - https://doi.org/10.24433/CO.2372084.v1 ER - TY - JOUR A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Burger, Sven T1 - RPExpand: Software for Riesz projection expansion of resonance phenomena JF - SoftwareX Y1 - 2021 U6 - https://doi.org/10.1016/j.softx.2021.100763 VL - 15 SP - 100763 ER - TY - THES A1 - Punjabi, Dev T1 - Orientation-invariant Dense Correspondence using Graph Convolutional Neural Networks Y1 - 2021 ER - TY - JOUR A1 - Roux, Christophe A1 - Pokutta, Sebastian A1 - Wirth, Elias A1 - Kerdreux, Thomas T1 - Efficient Online-Bandit Strategies for Minimax Learning Problems N2 - Several learning problems involve solving min-max problems, e.g., empirical distributional robust learning [Namkoong and Duchi, 2016, Curi et al., 2020] or learning with non-standard aggregated losses [Shalev- Shwartz and Wexler, 2016, Fan et al., 2017]. More specifically, these problems are convex-linear problems where the minimization is carried out over the model parameters w ∈ W and the maximization over the empirical distribution p ∈ K of the training set indexes, where K is the simplex or a subset of it. To design efficient methods, we let an online learning algorithm play against a (combinatorial) bandit algorithm. We argue that the efficiency of such approaches critically depends on the structure of K and propose two properties of K that facilitate designing efficient algorithms. We focus on a specific family of sets Sn,k encompassing various learning applications and provide high-probability convergence guarantees to the minimax values. Y1 - 2021 ER - TY - JOUR A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven T1 - Resonance expansion of quadratic quantities with regularized quasinormal modes JF - Phys. Status Solidi A Y1 - 2023 U6 - https://doi.org/10.1002/pssa.202200892 VL - 220 SP - 2200892 ER - TY - JOUR A1 - Melnyk, Kateryna A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Understanding microbiome dynamics via interpretable graph representation learning JF - Scientific Reports N2 - Large-scale perturbations in the microbiome constitution are strongly correlated, whether as a driver or a consequence, with the health and functioning of human physiology. However, understanding the difference in the microbiome profiles of healthy and ill individuals can be complicated due to the large number of complex interactions among microbes. We propose to model these interactions as a time-evolving graph whose nodes are microbes and edges are interactions among them. Motivated by the need to analyse such complex interactions, we develop a method that learns a low-dimensional representation of the time-evolving graph and maintains the dynamics occurring in the high-dimensional space. Through our experiments, we show that we can extract graph features such as clusters of nodes or edges that have the highest impact on the model to learn the low-dimensional representation. This information can be crucial to identify microbes and interactions among them that are strongly correlated with clinical diseases. We conduct our experiments on both synthetic and real-world microbiome datasets. Y1 - 2023 U6 - https://doi.org/10.1038/s41598-023-29098-7 VL - 13 SP - 2058 ER - TY - THES A1 - Şirin, Ege T1 - Probabilistic Image Segmentation With Continuous Shape Representations Y1 - 2023 ER - TY - JOUR A1 - Eifler, Leon A1 - Gleixner, Ambros T1 - Safe and Verified Gomory Mixed Integer Cuts in a Rational MIP Framework JF - SIAM Journal on Optimization N2 - This paper is concerned with the exact solution of mixed-integer programs (MIPs) over the rational numbers, i.e., without any roundoff errors and error tolerances. Here, one computational bottleneck that should be avoided whenever possible is to employ large-scale symbolic computations. Instead it is often possible to use safe directed rounding methods, e.g., to generate provably correct dual bounds. In this work, we continue to leverage this paradigm and extend an exact branch-and-bound framework by separation routines for safe cutting planes, based on the approach first introduced by Cook, Dash, Fukasawa, and Goycoolea in 2009. Constraints are aggregated safely using approximate dual multipliers from an LP solve, followed by mixed-integer rounding to generate provably valid, although slightly weaker inequalities. We generalize this approach to problem data that is not representable in floating-point arithmetic, add routines for controlling the encoding length of the resulting cutting planes, and show how these cutting planes can be verified according to the VIPR certificate standard. Furthermore, we analyze the performance impact of these cutting planes in the context of an exact MIP framework, showing that we can solve 21.5% more instances and reduce solving times by 26.8% on the MIPLIB 2017 benchmark test set. Y1 - 2023 U6 - https://doi.org/10.1137/23M156046X VL - 34 IS - 1 ER - TY - GEN A1 - Eifler, Leon A1 - Gleixner, Ambros T1 - Safe and Verified Gomory Mixed Integer Cuts in a Rational MIP Framework N2 - This paper is concerned with the exact solution of mixed-integer programs (MIPs) over the rational numbers, i.e., without any roundoff errors and error tolerances. Here, one computational bottleneck that should be avoided whenever possible is to employ large-scale symbolic computations. Instead it is often possible to use safe directed rounding methods, e.g., to generate provably correct dual bounds. In this work, we continue to leverage this paradigm and extend an exact branch-and-bound framework by separation routines for safe cutting planes, based on the approach first introduced by Cook, Dash, Fukasawa, and Goycoolea in 2009. Constraints are aggregated safely using approximate dual multipliers from an LP solve, followed by mixed-integer rounding to generate provably valid, although slightly weaker inequalities. We generalize this approach to problem data that is not representable in floating-point arithmetic, add routines for controlling the encoding length of the resulting cutting planes, and show how these cutting planes can be verified according to the VIPR certificate standard. Furthermore, we analyze the performance impact of these cutting planes in the context of an exact MIP framework, showing that we can solve 21.5% more instances and reduce solving times by 26.8% on the MIPLIB 2017 benchmark test set. T3 - ZIB-Report - 23-09 KW - Mixed integer programming, Exact computation, Rational arithmetic, Cutting Planes, Symbolic Computations, Certificate of correctness Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-90159 SN - 1438-0064 ER - TY - JOUR A1 - Secker, Christopher A1 - Fackeldey, Konstantin A1 - Weber, Marcus A1 - Ray, Sourav A1 - Gorgulla, Christoph A1 - Schütte, Christof T1 - Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists JF - Journal of Cheminformatics N2 - Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the μ-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported β-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale. Y1 - 2023 U6 - https://doi.org/10.1186/s13321-023-00746-4 VL - 15 ER - TY - GEN A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven T1 - Source code and simulation results for computing resonance expansions of quadratic quantities with regularized quasinormal modes T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7376556 ER - TY - JOUR A1 - Carderera, Alejandro A1 - Pokutta, Sebastian A1 - Schütte, Christof A1 - Weiser, Martin T1 - An efficient first-order conditional gradient algorithm in data-driven sparse identification of nonlinear dynamics to solve sparse recovery problems under noise JF - Journal of Computational and Applied Mathematics N2 - Governing equations are essential to the study of nonlinear dynamics, often enabling the prediction of previously unseen behaviors as well as the inclusion into control strategies. The discovery of governing equations from data thus has the potential to transform data-rich fields where well-established dynamical models remain unknown. This work contributes to the recent trend in data-driven sparse identification of nonlinear dynamics of finding the best sparse fit to observational data in a large library of potential nonlinear models. We propose an efficient first-order Conditional Gradient algorithm for solving the underlying optimization problem. In comparison to the most prominent alternative algorithms, the new algorithm shows significantly improved performance on several essential issues like sparsity-induction, structure-preservation, noise robustness, and sample efficiency. We demonstrate these advantages on several dynamics from the field of synchronization, particle dynamics, and enzyme chemistry. Y1 - 2021 ER - TY - GEN A1 - Lenz, Ralf A1 - Serrano, Felipe T1 - Tight Convex Relaxations for the Expansion Planning Problem N2 - Secure energy transport is considered as highly relevant for the basic infrastructure of nowadays society and economy. To satisfy increasing demands and to handle more diverse transport situations, operators of energy networks regularly expand the capacity of their network by building new network elements, known as the expansion planning problem. A key constraint function in expansion planning problems is a nonlinear and nonconvex potential loss function. In order to improve the algorithmic performance of state-of-the-art MINLP solvers, this paper presents an algebraic description for the convex envelope of this function. Through a thorough computational study, we show that this tighter relaxation tremendously improve the performance of the MINLP solver SCIP on a large test set of practically relevant instances for the expansion planning problem. In particular, the results show that our achievements lead to an improvement of the solver performance for a development version by up to 58%. T3 - ZIB-Report - 21-05 KW - Convex envelopes KW - Mixed-Integer Nonlinear Programming KW - Expansion planning of energy networks Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-81459 SN - 1438-0064 ER - TY - JOUR A1 - Gelss, Patrick A1 - Klus, Stefan A1 - Schuster, Ingmar A1 - Schütte, Christof T1 - Feature space approximation for kernel-based supervised learning JF - Knowledge-Based Sytems Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1016/j.knosys.2021.106935 VL - 221 PB - Elsevier ER - TY - JOUR A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Zimmer, Max T1 - Deep Neural Network Training with Frank-Wolfe N2 - This paper studies the empirical efficacy and benefits of using projection-free first-order methods in the form of Conditional Gradients, a.k.a. Frank-Wolfe methods, for training Neural Networks with constrained parameters. We draw comparisons both to current state-of-the-art stochastic Gradient Descent methods as well as across different variants of stochastic Conditional Gradients. In particular, we show the general feasibility of training Neural Networks whose parameters are constrained by a convex feasible region using Frank-Wolfe algorithms and compare different stochastic variants. We then show that, by choosing an appropriate region, one can achieve performance exceeding that of unconstrained stochastic Gradient Descent and matching state-of-the-art results relying on L2-regularization. Lastly, we also demonstrate that, besides impacting performance, the particular choice of constraints can have a drastic impact on the learned representations. Y1 - 2020 ER - TY - JOUR A1 - Cryille W., Combettes A1 - Spiegel, Christoph A1 - Pokutta, Sebastian T1 - Projection-Free Adaptive Gradients for Large-Scale Optimization N2 - The complexity in large-scale optimization can lie in both handling the objective function and handling the constraint set. In this respect, stochastic Frank-Wolfe algorithms occupy a unique position as they alleviate both computational burdens, by querying only approximate first-order information from the objective and by maintaining feasibility of the iterates without using projections. In this paper, we improve the quality of their first-order information by blending in adaptive gradients. We derive convergence rates and demonstrate the computational advantage of our method over the state-of-the-art stochastic Frank-Wolfe algorithms on both convex and nonconvex objectives. The experiments further show that our method can improve the performance of adaptive gradient algorithms for constrained optimization. Y1 - 2020 ER - TY - JOUR A1 - Liang, YongTian A1 - Piao, Chengji A1 - Beuschel, Christine B. A1 - Toppe, David A1 - Kollipara, Laxmikanth A1 - Bogdanow, Boris A1 - Maglione, Marta A1 - Lützkendorf, Janine A1 - See, Jason Chun Kit A1 - Huang, Sheng A1 - Conrad, Tim A1 - Kintscher, Ulrich A1 - Madeo, Frank A1 - Liu, Fan A1 - Sickmann, Albert A1 - Sigrist, Stephan J. T1 - eIF5A hypusination, boosted by dietary spermidine, protects from premature brain aging and mitochondrial dysfunction JF - Cell Reports Y1 - 2021 U6 - https://doi.org/10.1016/j.celrep.2021.108941 VL - 35 IS - 2 ER - TY - JOUR A1 - Melnyk, Kateryna A1 - Montavon, Grègoire A1 - Klus, Stefan A1 - Conrad, Tim T1 - Graph Kernel Koopman Embedding for Human Microbiome Analysis JF - Applied Network Science N2 - More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes. Y1 - 2020 U6 - https://doi.org/10.1007/s41109-020-00339-2 VL - 5 IS - 96 ER - TY - JOUR A1 - Iravani, Sahar A1 - Conrad, Tim T1 - An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics N2 - Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks. Y1 - 2023 U6 - https://doi.org/10.1109/tcbb.2022.3141656 VL - 20 IS - 1 SP - 151 EP - 161 ER - TY - JOUR A1 - Rams, Mona A1 - Conrad, Tim T1 - Dictionary learning allows model-free pseudotime estimation of transcriptomics data JF - BMC Genomics Y1 - 2022 U6 - https://doi.org/10.1186/s12864-021-08276-9 VL - 23 PB - BioMed Central ER - TY - JOUR A1 - Pakhomov, Anton V. A1 - Hammerschmidt, Martin A1 - Burger, Sven A1 - Pertsch, Thomas A1 - Setzpfand, Frank T1 - Modeling of surface-induced second-harmonic generation from multilayer structures by the transfer matrix method JF - Opt. Express Y1 - 2021 U6 - https://doi.org/10.1364/OE.417066 VL - 29 SP - 9098 ER - TY - GEN A1 - Bestuzheva, Ksenia A1 - Gleixner, Ambros A1 - Vigerske, Stefan T1 - A Computational Study of Perspective Cuts N2 - The benefits of cutting planes based on the perspective function are well known for many specific classes of mixed-integer nonlinear programs with on/off structures. However, we are not aware of any empirical studies that evaluate their applicability and computational impact over large, heterogeneous test sets in general-purpose solvers. This paper provides a detailed computational study of perspective cuts within a linear programming based branch-and-cut solver for general mixed-integer nonlinear programs. Within this study, we extend the applicability of perspective cuts from convex to nonconvex nonlinearities. This generalization is achieved by applying a perspective strengthening to valid linear inequalities which separate solutions of linear relaxations. The resulting method can be applied to any constraint where all variables appearing in nonlinear terms are semi-continuous and depend on at least one common indicator variable. Our computational experiments show that adding perspective cuts for convex constraints yields a consistent improvement of performance, and adding perspective cuts for nonconvex constraints reduces branch-and-bound tree sizes and strengthens the root node relaxation, but has no significant impact on the overall mean time. T3 - ZIB-Report - 21-07 KW - perspective cuts, mixed-integer nonlinear programming, nonconvex optimization, computational study Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-81821 SN - 1438-0064 ER - TY - JOUR A1 - Weimann, K. A1 - Conrad, Tim T1 - Transfer Learning for ECG Classification JF - Scientific Reports N2 - Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to 6.57%, effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at https://github.com/kweimann/ecg-transfer-learning. Y1 - 2021 U6 - https://doi.org/10.1038/s41598-021-84374-8 VL - 11 ER - TY - JOUR A1 - Le Duc, Huy A1 - Conrad, Tim T1 - A light-weight and highly flexible software system for analyzing large bio-medical datasets JF - Future Generation Computer Systems Y1 - 2020 ER - TY - JOUR A1 - Juds, Carmen A1 - Schmidt, Johannes A1 - Weller, Michael A1 - Lange, Thorid A1 - Conrad, Tim A1 - Boerner, Hans T1 - Combining Phage Display and Next-generation Sequencing for Materials Sciences: A Case Study on Probing Polypropylene Surfaces JF - Journal of the American Chemical Society N2 - Phage display biopanning with Illumina next-generation sequencing (NGS) is applied to reveal insights into peptide-based adhesion domains for polypropylene (PP). One biopanning round followed by NGS selects robust PP-binding peptides that are not evident by Sanger sequencing. NGS provides a significant statistical base that enables motif analysis, statistics on positional residue depletion/enrichment, and data analysis to suppress false-positive sequences from amplification bias. The selected sequences are employed as water-based primers for PP?metal adhesion to condition PP surfaces and increase adhesive strength by 100\% relative to nonprimed PP. Y1 - 2020 U6 - https://doi.org/10.1021/jacs.0c03482 VL - 142 IS - 24 SP - 10624 EP - 10628 ER - TY - JOUR A1 - Cvetkovic, Nada A1 - Conrad, Tim A1 - Lie, Han Cheng T1 - A Convergent Discretisation Method for Transition Path Theory for Diffusion Processes JF - Multiscale Modeling & Simulation Y1 - 2021 U6 - https://doi.org/10.1137/20M1329354 VL - 19 IS - 1 SP - 242 EP - 266 PB - Society for Industrial and Applied Mathematics ER - TY - JOUR A1 - Witzig, Jakob A1 - Berthold, Timo T1 - Conflict Analysis for MINLP JF - INFORMS Journal on Computing N2 - The generalization of MIP techniques to deal with nonlinear, potentially non-convex, constraints have been a fruitful direction of research for computational MINLP in the last decade. In this paper, we follow that path in order to extend another essential subroutine of modern MIP solvers towards the case of nonlinear optimization: the analysis of infeasible subproblems for learning additional valid constraints. To this end, we derive two different strategies, geared towards two different solution approaches. These are using local dual proofs of infeasibility for LP-based branch-and-bound and the creation of nonlinear dual proofs for NLP-based branch-and-bound, respectively. We discuss implementation details of both approaches and present an extensive computational study, showing that both techniques can significantly enhance performance when solving MINLPs to global optimality. Y1 - 2021 U6 - https://doi.org/10.1287/ijoc.2020.1050 VL - 33 IS - 2 SP - 421 EP - 435 ER - TY - JOUR A1 - Gamrath, Gerald A1 - Berthold, Timo A1 - Salvagnin, Domenico T1 - An exploratory computational analysis of dual degeneracy in mixed-integer programming JF - EURO Journal on Computational Optimization N2 - Dual degeneracy, i.e., the presence of multiple optimal bases to a linear programming (LP) problem, heavily affects the solution process of mixed integer programming (MIP) solvers. Different optimal bases lead to different cuts being generated, different branching decisions being taken and different solutions being found by primal heuristics. Nevertheless, only a few methods have been published that either avoid or exploit dual degeneracy. The aim of the present paper is to conduct a thorough computational study on the presence of dual degeneracy for the instances of well-known public MIP instance collections. How many instances are affected by dual degeneracy? How degenerate are the affected models? How does branching affect degeneracy: Does it increase or decrease by fixing variables? Can we identify different types of degenerate MIPs? As a tool to answer these questions, we introduce a new measure for dual degeneracy: the variable–constraint ratio of the optimal face. It provides an estimate for the likelihood that a basic variable can be pivoted out of the basis. Furthermore, we study how the so-called cloud intervals—the projections of the optimal face of the LP relaxations onto the individual variables—evolve during tree search and the implications for reducing the set of branching candidates. Y1 - 2020 U6 - https://doi.org/10.1007/s13675-020-00130-z IS - 8 SP - 241 EP - 246 ER - TY - JOUR A1 - Devriendt, Jo A1 - Gleixner, Ambros A1 - Nordström, Jakob T1 - Learn to Relax: Integrating 0-1 Integer Linear Programming with Pseudo-Boolean Conflict-Driven Search JF - Constraints N2 - Conflict-driven pseudo-Boolean solvers optimize 0-1 integer linear programs by extending the conflict-driven clause learning (CDCL) paradigm from SAT solving. Though pseudo-Boolean solvers have the potential to be exponentially more efficient than CDCL solvers in theory, in practice they can sometimes get hopelessly stuck even when the linear programming (LP) relaxation is infeasible over the reals. Inspired by mixed integer programming (MIP), we address this problem by interleaving incremental LP solving with cut generation within the conflict-driven pseudo-Boolean search. This hybrid approach, which for the first time combines MIP techniques with full-blown conflict analysis operating directly on linear inequalities using the cutting planes method, significantly improves performance on a wide range of benchmarks, approaching a "best-of-both-worlds" scenario between SAT-style conflict-driven search and MIP-style branch-and-cut. Y1 - 2021 U6 - https://doi.org/10.1007/s10601-020-09318-x VL - 26 SP - 26 EP - 55 ER - TY - CHAP A1 - Shinano, Yuji A1 - Tateiwa, N. A1 - Nakamura, S. A1 - Yoshida, A. A1 - Yasuda, M. A1 - Kaji, S. A1 - Fujisawa, K. T1 - Massive Parallelization for Finding Shortest Lattice Vectors Based on Ubiquity Generator Framework T2 - 2020 SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (SC) N2 - Lattice-based cryptography has received attention as a next-generation encryption technique, because it is believed to be secure against attacks by classical and quantum computers. Its essential security depends on the hardness of solving the shortest vector problem (SVP). In the cryptography, to determine security levels, it is becoming significantly more important to estimate the hardness of the SVP by high-performance computing. In this study, we develop the world’s first distributed and asynchronous parallel SVP solver, the MAssively Parallel solver for SVP (MAP-SVP). It can parallelize algorithms for solving the SVP by applying the Ubiquity Generator framework, which is a generic framework for branch-and-bound algorithms. The MAP-SVP is suitable for massive-scale parallelization, owing to its small memory footprint, low communication overhead, and rapid checkpoint and restart mechanisms. We demonstrate its performance and scalability of the MAP-SVP by using up to 100,032 cores to solve instances of the Darmstadt SVP Challenge. Y1 - 2020 U6 - https://doi.org/10.1109/SC41405.2020.00064 SP - 834 EP - 848 ER - TY - JOUR A1 - Yokoyama, Ryohei A1 - Shinano, Yuji A1 - Wakayama, Yuki A1 - Wakui, Tetsuya T1 - Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method JF - Energy N2 - Mixed-integer linear programming (MILP) methods have been applied widely to optimal design of energy supply systems in consideration of multi-period operation. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. An original problem has been solved by dividing it into a relaxed optimal design problem at the upper level and optimal operation problems which are independent of one another at the lower level. In addition, some strategies have been proposed to enhance the computation efficiency furthermore. In this paper, a method of reducing model by time aggregation is proposed as a novel strategy to search design candidates efficiently in the relaxed optimal design problem at the upper level. In addition, the previous strategies are modified in accordance with the novel strategy. This method is realized only by clustering periods and averaging energy demands for clustered periods, while it guarantees to derive the optimal solution. Thus, it may decrease the computation time at the upper level. Through a case study on the optimal design of a gas turbine cogeneration system, it is clarified how the model reduction is effective to enhance the computation efficiency in comparison and combination with the modified previous strategies. Y1 - 2019 U6 - https://doi.org/https://doi.org/10.1016/j.energy.2019.04.066 VL - 181 SP - 782 EP - 792 ER - TY - CHAP A1 - Mortagy, Hassan A1 - Gupta, Swati A1 - Pokutta, Sebastian T1 - Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization T2 - Proceedings of NeurIPS Y1 - 2020 N1 - URL of the Code: https://github.com/pokutta/Walking-in-the-Shadow N1 - URL of the Poster: https://app.box.com/s/y266djezdjdidsswaopvdcnsnkp8774i N1 - URL of the Slides: https://app.box.com/s/wjhpe4nh8kv5pw6vl5jbp902v3mksivs ER - TY - THES A1 - Lüdke, David T1 - Neural flow-based deformations for statistical shape modelling N2 - Statistical shape models learn to capture the most characteristic geometric variations of anatomical structures given samples from their population. Accordingly, shape models have become an essential tool for many medical applications and are used in, for example, shape generation, reconstruction, and classification tasks. However, established statistical shape models require precomputed dense correspondence between shapes, often lack robustness, and ignore the global surface topology. This thesis presents a novel neural flow-based shape model that does not require any precomputed correspondence. The proposed model relies on continuous flows of a neural ordinary differential equation to model shapes as deformations of a template. To increase the expressivity of the neural flow and disentangle global, low-frequency deformations from the generation of local, high- frequency details, we propose to apply a hierarchy of flows. We evaluate the performance of our model on two anatomical structures, liver, and distal femur. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior, as indicated by its generalization ability and specificity. More so, we demonstrate the effectiveness of our shape model on shape reconstruction tasks and find anatomically plausible solutions. Finally, we assess the quality of the emerging shape representation in an unsupervised setting and discriminate healthy from pathological shapes. KW - statistical shape model KW - statistical shape analysis KW - representation learning Y1 - 2022 UR - https://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/searchtype/authorsearch/author/David+L%C3%BCdke/docId/70904/start/0/rows/10 ER - TY - JOUR A1 - Ramos Leite, Tiago A1 - Zschiedrich, Lin A1 - Kizilkaya, Orhan A1 - McPeak, Kevin M. T1 - Resonant Plasmonic–Biomolecular Chiral Interactions in the Far-Ultraviolet: Enantiomeric Discrimination of sub-10 nm Amino Acid Films JF - Nano Lett. Y1 - 2022 UR - https://digitalcommons.lsu.edu/chem_engineering_pubs/34/ U6 - https://doi.org/10.1021/acs.nanolett.2c01724 VL - 22 SP - 7343 ER - TY - JOUR A1 - Šofranac, Boro A1 - Gleixner, Ambros A1 - Pokutta, Sebastian T1 - An Algorithm-independent Measure of Progress for Linear Constraint Propagation JF - Constraints Y1 - 2022 U6 - https://doi.org/10.1007/s10601-022-09338-9 VL - 27 SP - 432 EP - 455 ER - TY - JOUR A1 - Rickert, Lucas A1 - Betz, Fridtjof A1 - Plock, Matthias A1 - Burger, Sven A1 - Heindel, Tobias T1 - High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings JF - Opt. Express Y1 - 2023 U6 - https://doi.org/10.1364/OE.486060 VL - 31 SP - 14750 ER - TY - GEN A1 - Rickert, Lucas A1 - Betz, Fridtjof A1 - Plock, Matthias A1 - Burger, Sven A1 - Heindel, Tobias T1 - Data publication for "High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings" T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7360516 SP - 7360516 ER - TY - JOUR A1 - Tuncay, Erhun Giray A1 - Erdur, Rıza Cenk A1 - Conrad, Tim T1 - Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics N2 - The aim of Network Alignment in Protein-Protein Interaction Networks is discovering functionally similar regions between compared organisms. One major compromise for solving a network alignment problem is the trade-off among multiple similarity objectives while applying an alignment strategy. An alignment may lose its biological relevance while favoring certain objectives upon others due to the actual relevance of unfavored objectives. One possible solution for solving this issue may be blending the stronger aspects of various alignment strategies until achieving mature solutions. This study proposes a parallel approach called PERSONA that allows aligners to share their partial solutions continuously while they progress. All these aligners pursue their particular heuristics as part of a particle swarm that searches for multi-objective solutions of the same alignment problem in a reactive actor environment. The actors use the stronger portion of a solution as a subgraph that they receive from leading or other actors and send their own stronger subgraphs back upon evaluation of those partial solutions. Moreover, the individual heuristics of each actor takes randomized parameter values at each cycle of parallel execution so that the problem search space can thoroughly be investigated. The results achieved with PERSONA are remarkably optimized and balanced for both topological and node similarity objectives. Y1 - 2023 U6 - https://doi.org/10.1109/TCBB.2022.3231489 VL - 20 IS - 3 SP - 2064 EP - 2077 ER - TY - JOUR A1 - Mohammadzadeh, Saeed A1 - H. Nascimento, Vítor A1 - C. de Lamare, Rodrigo A1 - Hajarolasvadi, Noushin T1 - Robust Beamforming Based on Complex-Valued Convolutional Neural Networks for Sensor Arrays JF - IEEE Signal Processing Letters N2 - Robust adaptive beamforming (RAB) plays a vital role in modern communications by ensuring the reception of high-quality signals. This article proposes a deep learning approach to robust adaptive beamforming. In particular, we propose a novel RAB approach where the sample covariance matrix (SCM) is used as the input of a deep 1D Complex-Valued Convolutional Neural Network (CVCNN). The network employs complex convolutional and pooling layers, as well as a Cartesian Scaled Exponential Linear Unit activation function to directly compute the nearly-optimum weight vector through the training process and without prior knowledge about the direction of arrival of the desired signal. This means that reconstruction of the interference plus noise (IPN) covariance matrix is not required. The trained CVCNN accurately computes the nearly-optimum weight vector for data not used during training. The computed weight vector is employed to estimate the signal-to-interference plus noise ratio. Simulations show that the proposed RAB can provide performance close to that of the optimal beamformer. Y1 - 2022 U6 - https://doi.org/10.1109/LSP.2022.3212637 VL - 29 SP - 2018 EP - 2021 ER - TY - THES A1 - Rams, Mona Milena T1 - New approaches for unsupervised transcriptomic data analysis based on Dictionary learning Y1 - 2022 ER - TY - CHAP A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Hammerschmidt, Martin A1 - Kuen, Lilli A1 - Sekulic, Ivan A1 - Kluge, Julien A1 - Leykauf, Bastian A1 - Krutzik, Markus A1 - Burger, Sven T1 - Boosting quantum optics experiments with Bayesian optimization T2 - Proc. SPIE Y1 - 2022 U6 - https://doi.org/10.1117/12.2632419 VL - 12227 SP - 122270F ER - TY - JOUR A1 - Alchikh, Maren A1 - Conrad, Tim A1 - Obermeier, Patrick A1 - Ma, Xiaolin A1 - Schweiger, Brunhilde A1 - Opota, Onya A1 - Rath, Barbara T1 - Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study JF - Viruses N2 - Respiratory viral infections (RVIs) are common reasons for healthcare consultations. The inpatient management of RVIs consumes significant resources. From 2009 to 2014, we assessed the costs of RVI management in 4776 hospitalized children aged 0–18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by detailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU (‘non-ICU’) versus management requiring ICU care (‘ICU’) added up to EUR 2767.14 (non-ICU) vs. EUR 29,941.71 (ICU) for influenza, EUR 2713.14 (non-ICU) vs. EUR 16,951.06 (ICU) for RSV infections, and EUR 2767.33 (non-ICU) vs. EUR 14,394.02 (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all eight RVIs studied: influenza, RSV, hRV, adenovirus (hAdV), metapneumovirus (hMPV), parainfluenza virus (hPIV), bocavirus (hBoV), and seasonal coronavirus (hCoV) infections. ICU costs for influenza, however, exceeded all other RVIs. At the time of the study, influenza was the only RVI with antiviral treatment options available for children, but only 9.8% of influenza patients (non-ICU) and 1.5% of ICU patients with influenza received antivirals; only 2.9% were vaccinated. Future studies should investigate the economic impact of treatment and prevention of influenza, COVID-19, and RSV post vaccine introduction. Y1 - 2024 U6 - https://doi.org/10.3390/v16040507 VL - 16 IS - 4 ER - TY - JOUR A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven A1 - Binkowski, Felix T1 - Efficient rational approximation of optical response functions with the AAA algorithm JF - Laser Photonics Rev. Y1 - 2024 U6 - https://doi.org/10.1002/lpor.202400584 VL - 18 SP - 2400584 ER - TY - JOUR A1 - Sherratt, Katharine A1 - Srivastava, Ajitesh A1 - Ainslie, Kylie A1 - Singh, David E. A1 - Cublier, Aymar A1 - Marinescu, Maria Cristina A1 - Carretero, Jesus A1 - Garcia, Alberto Cascajo A1 - Franco, Nicolas A1 - Willem, Lander A1 - Abrams, Steven A1 - Faes, Christel A1 - Beutels, Philippe A1 - Hens, Niel A1 - Müller, Sebastian A1 - Charlton, Billy A1 - Ewert, Ricardo A1 - Paltra, Sydney A1 - Rakow, Christian A1 - Rehmann, Jakob A1 - Conrad, Tim A1 - Schütte, Christof A1 - Nagel, Kai A1 - Abbott, Sam A1 - Grah, Rok A1 - Niehus, Rene A1 - Prasse, Bastian A1 - Sandmann, Frank A1 - Funk, Sebastian T1 - Characterising information gains and losses when collecting multiple epidemic model outputs JF - Epidemics N2 - Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods We compared July 2022 projections from the European COVID-19 Scenario Modelling Hub. Five modelling teams projected incidence in Belgium, the Netherlands, and Spain. We compared projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results. By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions. We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts. Data availability All code and data available on Github: https://github.com/covid19-forecast-hub-europe/aggregation-info-loss KW - Virology KW - Infectious Diseases KW - Public Health, Environmental and Occupational Health KW - Microbiology KW - Parasitology KW - Epidemiology Y1 - 2024 U6 - https://doi.org/10.1016/j.epidem.2024.100765 SN - 1755-4365 VL - 47 PB - Elsevier BV ER - TY - GEN A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven A1 - Binkowski, Felix T1 - Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm T2 - Zenodo Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.10853692 SP - doi: 10.5281/zenodo.10853692 ER - TY - CHAP A1 - Mexi, Gioni A1 - Shamsi, Somayeh A1 - Besançon, Mathieu A1 - Bodic, Pierre T1 - Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model T2 - Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024 N2 - Strong Branching (SB) is a cornerstone of all modern branching rules used in the Branch-and-Bound (BnB) algorithm, which is at the center of Mixed-Integer Programming solvers. In its full form, SB evaluates all variables to branch on and then selects the one producing the best relaxation, leading to small trees, but high runtimes. State-of-the-art branching rules therefore use SB with working limits to achieve both small enough trees and short run times. So far, these working limits have been established empirically. In this paper, we introduce a theoretical approach to guide how much SB to use at each node within the BnB. We first define an abstract stochastic tree model of the BnB algorithm where the geometric mean dual gains of all variables follow a given probability distribution. This model allows us to relate expected dual gains to tree sizes and explicitly compare the cost of sampling an additional SB candidate with the reward in expected tree size reduction. We then leverage the insight from the abstract model to design a new stopping criterion for SB, which fits a distribution to the dual gains and, at each node, dynamically continues or interrupts SB. This algorithm, which we refer to as Probabilistic Lookahead Strong Branching, improves both the tree size and runtime over MIPLIB instances, providing evidence that the method not only changes the amount of SB, but allocates it better. Y1 - 2024 U6 - https://doi.org/10.1007/978-3-031-60599-4_4 VL - 14743 ER - TY - JOUR A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Kuen, Lilli A1 - Burger, Sven T1 - Version 2 — RPExpand: Software for Riesz projection expansion of resonance phenomena JF - SoftwareX Y1 - 2024 U6 - https://doi.org/10.1016/j.softx.2024.101694 VL - 26 SP - 101694 ER - TY - JOUR A1 - Plock, Matthias A1 - Binkowski, Felix A1 - Zschiedrich, Lin A1 - Schneider, Phillip-Immanuel A1 - Burger, Sven T1 - Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes JF - J. Opt. Soc. Am. B Y1 - 2024 U6 - https://doi.org/10.1364/JOSAB.505767 VL - 41 SP - 850 ER - TY - GEN A1 - Plock, Matthias A1 - Binkowski, Felix A1 - Zschiedrich, Lin A1 - Schneider, Phillip-Immanuel A1 - Burger, Sven T1 - Research data for "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes" T2 - Zenodo Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.8131611 SP - doi: 10.5281/zenodo.8131611 ER - TY - GEN A1 - Musial, Anna A1 - Sek, Grzegorz A1 - Schneider, Philipp-Immanuel A1 - Reitzenstein, Stephan T1 - Fiber-coupled solid-state-based single-photon sources T2 - Specialty Optical Fibers Y1 - 2024 U6 - https://doi.org/10.1016/B978-0-443-18495-6.00004-4 SP - 345 EP - 384 PB - Woodhead Publishing ER - TY - JOUR A1 - Eifler, Leon A1 - Gleixner, Ambros T1 - Safe and verified Gomory mixed integer cuts in a rational MIP framework JF - SIAM Journal on Optimization N2 - This paper is concerned with the exact solution of mixed-integer programs (MIPs) over the rational numbers, i.e., without any roundoff errors and error tolerances. Here, one computational bottleneck that should be avoided whenever possible is to employ large-scale symbolic computations. Instead it is often possible to use safe directed rounding methods, e.g., to generate provably correct dual bounds. In this work, we continue to leverage this paradigm and extend an exact branch-and-bound framework by separation routines for safe cutting planes, based on the approach first introduced by Cook, Dash, Fukasawa, and Goycoolea in 2009 [INFORMS J. Comput., 21 (2009), pp. 641–649]. Constraints are aggregated safely using approximate dual multipliers from an LP solve, followed by mixed-integer rounding to generate provably valid, although slightly weaker inequalities. We generalize this approach to problem data that is not representable in floating-point arithmetic, add routines for controlling the encoding length of the resulting cutting planes, and show how these cutting planes can be verified according to the VIPR certificate standard. Furthermore, we analyze the performance impact of these cutting planes in the context of an exact MIP framework, showing that we can solve 21.5% more instances to exact optimality and reduce solving times by 26.8% on the MIPLIB 2017 benchmark test set. Y1 - 2024 U6 - https://doi.org/10.1137/23M156046X VL - 34 IS - 1 ER - TY - CHAP A1 - Ghannam, Mohammed A1 - Gleixner, Ambros T1 - Hybrid genetic search for dynamic vehicle routing with time windows T2 - Proceedings of Conference of the Society for Operations Research in Germany Y1 - 2023 ER - TY - CHAP A1 - Hoen, Alexander A1 - Oertel, Andy A1 - Gleixner, Ambros A1 - Nordström, Jakob T1 - Certifying MIP-based presolve reductions for 0-1 integer linear programs T2 - Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024 N2 - It is well known that reformulating the original problem can be crucial for the performance of mixed-integer programming (MIP) solvers. To ensure correctness, all transformations must preserve the feasibility status and optimal value of the problem, but there is currently no established methodology to express and verify the equivalence of two mixed-integer programs. In this work, we take a first step in this direction by showing how the correctness of MIP presolve reductions on – integer linear programs can be certified by using (and suitably extending) the VeriPB tool for pseudo-Boolean proof logging. Our experimental evaluation on both decision and optimization instances demonstrates the computational viability of the approach and leads to suggestions for future revisions of the proof format that will help to reduce the verbosity of the certificates and to accelerate the certification and verification process further. Y1 - 2024 U6 - https://doi.org/10.1007/978-3-031-60597-0_20 VL - 14742 ER - TY - JOUR A1 - Kuen, Lilli A1 - Löffler, Lorenz A1 - Tsarapkin, Aleksei A1 - Zschiedrich, Lin A1 - Feichtner, Thorsten A1 - Burger, Sven A1 - Höflich, Katja T1 - Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna JF - Appl. Phys. Lett. Y1 - 2024 U6 - https://doi.org/10.1063/5.0201748 VL - 124 SP - 231102 ER - TY - JOUR A1 - Amiranashvili, Tamaz A1 - Lüdke, David A1 - Li, Hongwei Bran A1 - Zachow, Stefan A1 - Menze, Bjoern T1 - Learning continuous shape priors from sparse data with neural implicit functions JF - Medical Image Analysis N2 - Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g., to reduce radiation exposure in CT or image acquisition time in MR imaging. For existing shape modeling approaches, the resolution of the emerging model is limited to the resolution of the training shapes. Therefore, any missing information between slices prohibits existing methods from learning a high-resolution shape prior. We propose a novel shape modeling approach that can be trained on sparse, binary segmentation masks with large slice distances. This is achieved through employing continuous shape representations based on neural implicit functions. After training, our model can reconstruct shapes from various sparse inputs at high target resolutions beyond the resolution of individual training examples. We successfully reconstruct high-resolution shapes from as few as three orthogonal slices. Furthermore, our shape model allows us to embed various sparse segmentation masks into a common, low-dimensional latent space — independent of the acquisition direction, resolution, spacing, and field of view. We show that the emerging latent representation discriminates between healthy and pathological shapes, even when provided with sparse segmentation masks. Lastly, we qualitatively demonstrate that the emerging latent space is smooth and captures characteristic modes of shape variation. We evaluate our shape model on two anatomical structures: the lumbar vertebra and the distal femur, both from publicly available datasets. Y1 - 2024 U6 - https://doi.org/10.1016/j.media.2024.103099 VL - 94 SP - 103099 ER - TY - GEN A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Burger, Sven A1 - Kuen, Lilli T1 - RPExpand (Version 2.0) T2 - Zenodo Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.10371002 SP - doi: 10.5281/zenodo.10371002 ER - TY - THES A1 - Peter, Clea T1 - Improving the Realism of Synthetic Cryogenic Electron Micrographs Using Generative Adversarial Networks N2 - This thesis addresses the problem of synthetic-to-real image refinement applied to tilt series of cryogenic electron micrographs. It explores the possibility of improving the realism of synthesized micrographs using generative adversarial networks, which could help to improve the automatic segmentation of cellular structures based on deep learning methods. For image refinement, three image-to-image translation networks were used to transfer the appearance of real micrographs to synthetic micrographs while preserving their original content, including the location and shape of particles. The first model, called SimGAN, was unable to produce any meaningful refinement. Instead, the content of the synthetic micrographs was corrupted by the addition of extensive noise, making SimGAN unsuitable for the problem of this thesis. As a result, CycleGAN was introduced and its refinement of synthetic micrographs matches the appearance of real micrographs very well. However, structural changes in the position and shape of particles were observed after translation. To avoid this behavior, CUT was used as a third model on an exploratory basis but its performance was inferior to that of CycleGAN. In conclusion, CycleGAN proved to be the most promising image-to-image translation model for the images presented, although it does not solve the main problem of this thesis. In order to do so, further modifications, such as the addition of a structural constraint during translation, are required. Y1 - 2023 ER - TY - THES A1 - Tack, Alexander T1 - Machine Learning-based Assessment of Multiple Anatomical Structures in Medical Image Data for Diagnosis and Prediction of Knee Osteoarthritis N2 - Knee osteoarthritis (KOA) is a degenerative disease that leads to pain and loss of function. It is estimated to affect over 500 million humans world-wide and is one of the most common reasons for disability. KOA is usually diagnosed by radiologists or clinical experts by anamnesis, physical examination, and by assessing medical image data. The latter is typically acquired using X-Ray or magnetic resonance imaging. Since manual image reading is subjective, tedious and time-consuming, automated methods are required for a fast and objective decision support and for a better understanding of the pathogenesis of KOA. This thesis sets a foundation towards automated computation of image-based KOA biomarkers for holistic assessment of the knee. This involves the assessment of multiple knee bones and soft tissues. An assessment of particular structures requires localization of these tissues. In order to automate a faithful localization of anatomical structures, deep learning-based methods are investigated and utilized. Additionally, convolutional neural networks (CNNs) are used for classification of medical image data, i.e., for a direct determination of the disease status and to detect anatomical structures and landmarks. The automatically computed anatomical volumes, locations, and other measurements are finally compared to values acquired by clinical experts and evaluated for clustering of KOA groups, classification of KOA severity, prediction of KOA progression, and prediction of total knee replacement. In various experiments it is shown that CNN-based methods are suitable for accurate medical image segmentation, object detection, landmark detection, and direct classification of disease stages from the image data. Computed features related to the menisci are found to be most expressive in terms of clustering of KOA groups and predicting of future disease states, thus allowing diagnosis of current KOA conditions and prediction of future conditions. The conclusion of this thesis is that machine learning-based, fully automated processing of medical image data shows potential for diagnosis and prediction of KOA grades. Future studies could investigate additional features in order to achieve an assessment of the whole knee or validate the findings of this work in clinical studies. KW - Deep learning KW - cartilage KW - meniscus KW - segmentation KW - classification KW - prediction KW - incident osteoarthritis KW - total knee replacement KW - landmark detection KW - hip-knee-ankle angle Y1 - 2024 U6 - https://doi.org/10.14279/depositonce-19738 ER - TY - JOUR A1 - Eifler, Leon A1 - Gleixner, Ambros T1 - A computational status update for exact rational mixed integer programming JF - Mathematical Programming N2 - The last milestone achievement for the roundoff-error-free solution of general mixed integer programs over the rational numbers was a hybrid-precision branch-and-bound algorithm published by Cook, Koch, Steffy, and Wolter in 2013. We describe a substantial revision and extension of this framework that integrates symbolic presolving, features an exact repair step for solutions from primal heuristics, employs a faster rational LP solver based on LP iterative refinement, and is able to produce independently verifiable certificates of optimality. We study the significantly improved performance and give insights into the computational behavior of the new algorithmic components. On the MIPLIB 2017 benchmark set, we observe an average speedup of 10.7x over the original framework and 2.9 times as many instances solved within a time limit of two hours. Y1 - 2023 U6 - https://doi.org/10.1007/s10107-021-01749-5 VL - 197 SP - 793 EP - 812 ER - TY - JOUR A1 - Besançon, Mathieu A1 - Garcia, Joaquim Dias A1 - Legat, Benoît A1 - Sharma, Akshay T1 - Flexible Differentiable Optimization via Model Transformations JF - INFORMS Journal on Computing N2 - We introduce DiffOpt.jl, a Julia library to differentiate through the solution of optimization problems with respect to arbitrary parameters present in the objective and/or constraints. The library builds upon MathOptInterface, thus leveraging the rich ecosystem of solvers and composing well with modeling languages like JuMP. DiffOpt offers both forward and reverse differentiation modes, enabling multiple use cases from hyperparameter optimization to backpropagation and sensitivity analysis, bridging constrained optimization with end-to-end differentiable programming. DiffOpt is built on two known rules for differentiating quadratic programming and conic programming standard forms. However, thanks to its ability to differentiate through model transformations, the user is not limited to these forms and can differentiate with respect to the parameters of any model that can be reformulated into these standard forms. This notably includes programs mixing affine conic constraints and convex quadratic constraints or objective function. Y1 - 2023 U6 - https://doi.org/10.1287/ijoc.2022.0283 VL - 36 IS - 2 SP - 456 EP - 478 ER - TY - JOUR A1 - Binkowski, Felix A1 - Kullig, Julius A1 - Betz, Fridtjof A1 - Zschiedrich, Lin A1 - Walther, Andrea A1 - Wiersig, Jan A1 - Burger, Sven T1 - Computing eigenfrequency sensitivities near exceptional points JF - Phys. Rev. Research Y1 - 2024 U6 - https://doi.org/10.1103/PhysRevResearch.6.023148 VL - 6 SP - 023148 ER - TY - GEN A1 - Binkowski, Felix A1 - Kullig, Julius A1 - Betz, Fridtjof A1 - Zschiedrich, Lin A1 - Walther, Andrea A1 - Wiersig, Jan A1 - Burger, Sven T1 - Source code and simulation results: Computing eigenfrequency sensitivities near exceptional points T2 - Zenodo Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.10715639 SP - doi: 10.5281/zenodo.10715639 ER - TY - CHAP A1 - Rickert, Lucas A1 - Gao, Timm A1 - Urban, Felix A1 - Große, Jan A1 - Srocka, Nicole A1 - Rodt, Sven A1 - Musial, Anna A1 - Zolnacz, Kinga A1 - Mergo, Pawel A1 - Dybka, Kamil A1 - Urbanczyk, Waclaw A1 - Sek, Grzegorz A1 - Burger, Sven A1 - Reitzenstein, Stephan A1 - Heindel, Tobias T1 - A Plug&Play Telecom-Wavelength Single-Photon Source for Quantum Key Distribution T2 - Quantum 2.0 Y1 - 2022 U6 - https://doi.org/10.1364/QUANTUM.2022.QTu4C.6 SP - QTu4C.6 ER - TY - GEN A1 - Plock, Matthias A1 - Andrle, Kas A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel T1 - Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction" T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.6359594 ER - TY - GEN A1 - Binkowski, Felix A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Burger, Sven T1 - Source code and simulation data for Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.6614951 ER - TY - JOUR A1 - Avalos-Ovando, Oscar A1 - Santiago, Eva Yazmin A1 - Movsesyan, Artur A1 - Kong, Xiang-Tian A1 - Yu, Peng A1 - Besteiro, Lucas V. A1 - Khosravi Khorashad, Larousse A1 - Okamoto, Hiromi A1 - Slocik, Joseph M. A1 - Correa-Duarte, Miguel A1 - Comesana-Hermo, Miguel A1 - Liedl, Tim A1 - Markovich, Gil A1 - Burger, Sven A1 - Govorov, Alexander O. T1 - Chiral Bioinspired Plasmonics: A Paradigm Shift for Optical Activity and Photochemistry JF - ACS Photon. Y1 - 2022 U6 - https://doi.org/10.1021/acsphotonics.2c00445 VL - 9 SP - 2219 ER - TY - JOUR A1 - Plock, Matthias A1 - Andrle, Kas A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel T1 - Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction JF - Adv. Theory Simul. Y1 - 2022 U6 - https://doi.org/10.1002/adts.202200112 VL - 5 SP - 2200112 ER - TY - CHAP A1 - Amiranashvili, Tamaz A1 - Lüdke, David A1 - Li, Hongwei A1 - Menze, Bjoern A1 - Zachow, Stefan T1 - Learning Shape Reconstruction from Sparse Measurements with Neural Implicit Functions T2 - Medical Imaging with Deep Learning N2 - Reconstructing anatomical shapes from sparse or partial measurements relies on prior knowledge of shape variations that occur within a given population. Such shape priors are learned from example shapes, obtained by segmenting volumetric medical images. For existing models, the resolution of a learned shape prior is limited to the resolution of the training data. However, in clinical practice, volumetric images are often acquired with highly anisotropic voxel sizes, e.g. to reduce image acquisition time in MRI or radiation exposure in CT imaging. The missing shape information between the slices prohibits existing methods to learn a high-resolution shape prior. We introduce a method for high-resolution shape reconstruction from sparse measurements without relying on high-resolution ground truth for training. Our method is based on neural implicit shape representations and learns a continuous shape prior only from highly anisotropic segmentations. Furthermore, it is able to learn from shapes with a varying field of view and can reconstruct from various sparse input configurations. We demonstrate its effectiveness on two anatomical structures: vertebra and femur, and successfully reconstruct high-resolution shapes from sparse segmentations, using as few as three orthogonal slices. Y1 - 2022 ER - TY - JOUR A1 - Obermeier, Patrick E A1 - Heim, Albert A1 - Biere, Barbara A1 - Hage, Elias A1 - Alchikh, Maren A1 - Conrad, Tim A1 - Schweiger, Brunhilde A1 - Rath, Barbara A T1 - Linking digital surveillance and in-depth virology to study clinical patterns of viral respiratory infections in vulnerable patient populations JF - iScience N2 - To improve the identification and management of viral respiratory infections, we established a clinical and virologic surveillance program for pediatric patients fulfilling pre-defined case criteria of influenza-like illness and viral respiratory infections. The program resulted in a cohort comprising 6,073 patients (56% male, median age 1.6 years, range 0–18.8 years), where every patient was assessed with a validated disease severity score at the point-of-care using the ViVI ScoreApp. We used machine learning and agnostic feature selection to identify characteristic clinical patterns. We tested all patients for human adenoviruses, 571 (9%) were positive. Adenovirus infections were particularly common and mild in children ≥1 month of age but rare and potentially severe in neonates: with lower airway involvement, disseminated disease, and a 50% mortality rate (n = 2/4). In one fatal case, we discovered a novel virus … Y1 - 2022 U6 - https://doi.org/10.1016/j.isci.2022.104276 VL - 25 IS - 5 PB - Cell Press ER - TY - JOUR A1 - Hajarolasvadi, Noushin A1 - Sunkara, Vikram A1 - Khavnekar, Sagar A1 - Beck, Florian A1 - Brandt, Robert A1 - Baum, Daniel T1 - Volumetric macromolecule identification in cryo-electron tomograms using capsule networks JF - BMC Bioinformatics N2 - Background: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning methods constructed for this problem is limited to conventional Convolutional Neural Networks (CNNs). Identifying macromolecules of different types and sizes is a tedious and time-consuming task. In this paper, we employ a capsule-based architecture to automate the task of macro- molecule identification, that we refer to as 3D-UCaps. In particular, the architecture is composed of three components: feature extractor, capsule encoder, and CNN decoder. The feature extractor converts voxel intensities of input sub-tomograms to activities of local features. The encoder is a 3D Capsule Network (CapsNet) that takes local features to generate a low-dimensional representation of the input. Then, a 3D CNN decoder reconstructs the sub-tomograms from the given representation by upsampling. Results: We performed binary and multi-class localization and identification tasks on synthetic and experimental data. We observed that the 3D-UNet and the 3D-UCaps had an F1−score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the F1-score when identifying very small particles (PDB entry 3GL1) compared to a large particle (PDB entry 4D8Q). In the multi-class identification task of experimental data, 3D-UCaps had an F1-score of 91% on the test data in contrast to 64% of the 3D-UNet. The better F1-score of 3D-UCaps compared to 3D-UNet is obtained by a higher precision score. We speculate this to be due to the capsule network employed in the encoder. To study the effect of the CapsNet-based encoder architecture further, we performed an ablation study and perceived that the F1-score is boosted as network depth is increased which is in contrast to the previously reported results for the 3D-UNet. To present a reproducible work, source code, trained models, data as well as visualization results are made publicly available. Conclusion: Quantitative and qualitative results show that 3D-UCaps successfully perform various downstream tasks including identification and localization of macro- molecules and can at least compete with CNN architectures for this task. Given that the capsule layers extract both the existence probability and the orientation of the molecules, this architecture has the potential to lead to representations of the data that are better interpretable than those of 3D-UNet. Y1 - 2022 U6 - https://doi.org/10.1186/s12859-022-04901-w VL - 23 IS - 360 ER - TY - JOUR A1 - Bonami, Pierre A1 - Gleixner, Ambros A1 - Linderoth, Jeff A1 - Misener, Ruth T1 - Designing and Implementing Algorithms for Mixed-Integer Nonlinear Optimization (Dagstuhl Seminar 18081) JF - Dagstuhl Reports N2 - Mathematical models for optimal decisions often require both nonlinear and discrete components. These mixed-integer nonlinear programs (MINLP) may be used to optimize the energy use of large industrial plants, integrate renewable sources into energy networks, design biological and biomedical systems, and address numerous other applications of societal importance. The first MINLP algorithms and software were designed by application engineers. While these efforts initially proved useful, scientists, engineers, and practitioners have realized that a transformational shift in technology will be required for MINLP to achieve its full potential. MINLP has transitioned to a forefront position in computer science, with researchers actively developing MINLP theory, algorithms, and implementations. Even with their concerted effort, algorithms and available software are often unable to solve practically-sized instances of these important models. Current obstacles include characterizing the computability boundary, effectively exploiting known optimization technologies for specialized classes of MINLP, and effectively using logical formulas holistically throughout algorithms. Y1 - 2018 U6 - https://doi.org/10.4230/DagRep.8.2.64 VL - 8 IS - 2 SP - 64 EP - 87 ER -