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  • 1
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Physics Letters B 294 (1992), S. 466-478 
    ISSN: 0370-2693
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Physics
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Physics Letters B 317 (1993), S. 474-484 
    ISSN: 0370-2693
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Physics
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 2024-01-11
    Description: Mixed-Integer Linear Programming (MILP) is a ubiquitous and practical modelling paradigm that is essential for optimising a broad range of real-world systems. The backbone of all modern MILP solvers is the branch-and-cut algorithm, which is a hybrid of the branch-and-bound and cutting planes algorithms. Cutting planes (cuts) are linear inequalities that tighten the relaxation of a MILP. While a lot of research has gone into deriving valid cuts for MILPs, less emphasis has been put on determining which cuts to select. Cuts in general are generated in rounds, and a subset of the generated cuts must be added to the relaxation. The decision on which subset of cuts to add is called cut selection. This is a crucial task since adding too many cuts makes the relaxation large and slow to optimise over. Conversely, adding too few cuts results in an insufficiently tightened relaxation, and more relaxations need to be enumerated. To further emphasise the difficulty, the effectiveness of an applied cut is both dependent on the other applied cuts, and the state of the MILP solver. In this thesis, we present theoretical results on the importance and difficulty of cut selection, as well as practical results that use cut selection to improve general MILP solver performance. Improving general MILP solver performance is of great importance for practitioners and has many runoff effects. Reducing the solve time of currently solved systems can directly improve efficiency within the application area. In addition, improved performance enables larger systems to be modelled and optimised, and MILP to be used in areas where it was previously impractical due to time restrictions. Each chapter of this thesis corresponds to a publication on cut selection, where the contributions of this thesis can naturally be divided into four components. The first two components are motivated by instance-dependent performance. In practice, for each subroutine, including cut selection, MILP solvers have adjustable parameters with hard-coded default values. It is ultimately unrealistic to expect these default values to perform well for every instance. Rather, it would be ideal if the parameters were dependent on the given instance. To show this motivation is well founded, we first introduce a family of parametric MILP instances and cuts to showcase worst-case performance of cut selection for any fixed parameter value. We then introduce a graph neural network architecture and reinforcement learning framework for learning instance-dependent cut scoring parameters. In the following component, we formalise language for determining if a cut has theoretical usefulness from a polyhedral point of view in relation to other cuts. In addition, to overcome issues of infeasible projections and dual degeneracy, we introduce analytic center based distance measures. We then construct a lightweight multi-output regression model that predicts relative solver performance of an instance for a set of distance measures. The final two components are motivated by general MILP solver improvement via cut selection. Such improvement was shown to be possible, albeit difficult to achieve, by the first half of this thesis. We relate branch-and-bound and cuts through their underlying disjunctions. Using a history of previously computed Gomory mixed-integer cuts, we reduce the solve time of SCIP over the 67% of affected MIPLIB 2017 instances by 4%. In the final component, we introduce new cut scoring measures and filtering methods based on information from other MILP solving processes. The new cut selection techniques reduce the solve time of SCIP over the 97% of affected MIPLIB 2017 instances by 5%.
    Language: English
    Type: doctoralthesis , doc-type:doctoralThesis
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  • 4
    Publication Date: 2024-01-19
    Description: 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.
    Language: German
    Type: article , doc-type:article
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  • 5
    Publication Date: 2024-01-23
    Description: Cardiac electrograms are an important tool to study the spread of excitation waves inside the heart, which in turn underlie muscle contraction. Electrograms can be used to analyse the dynamics of these waves, e.g. in fibrotic tissue. In computational models, these analyses can be done with greater detail than during minimally invasive in vivo procedures. Whilst homogenised models have been used to study electrogram genesis, such analyses have not yet been done in cellularly resolved models. Such high resolution may be required to develop a thorough understanding of the mechanisms behind abnormal excitation patterns leading to arrhythmias. In this study, we derived electrograms from an excitation propagation simulation in the Extracellular, Membrane, Intracellular (EMI) model, which represents these three domains explicitly in the mesh. We studied the effects of the microstructural excitation dynamics on electrogram genesis and morphology. We found that electrograms are sensitive to the myocyte alignment and connectivity, which translates into micro-fractionations in the electrograms.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 6
    Publication Date: 2024-01-24
    Description: The stability of shear layers in fluid flows is a crucial factor in forming vortices and jets and plays a fundamental role in the development of turbulence. Such shear layer instabilities are ubiquitous in natural phenomena, such as atmospheric and oceanic flows, contributing to the formation of weather systems and predicting tsunamis. This study specifically focuses on the stability of a shear layer sandwiched between two semi-infinite layers within a two-dimensional flow. The velocity profile of the shear layer is assumed to be linearly dependent on the vertical coordinate, while the velocity of the other layers remains uniform with differing strengths. The effect of viscosity and surface tension is ignored to simplify the analysis. The shallow water equations are used to analyze the interface stability of the shear layer, and the resulting dispersion relation between wave frequency and other wave characteristics is obtained. This relation incorporates Whittaker functions and their first derivatives and is used to derive appropriate limits corresponding to various physical conditions. Our study thus contributes to a deeper understanding of the stability of shear layers and their implications for natural phenomena.
    Language: English
    Type: article , doc-type:article
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  • 7
    Publication Date: 2024-01-24
    Description: This paper explores memory mechanisms in complex socio-technical systems, using a mobility demand model as an example case. We simplified a large-scale agent-based mobility model into a Markov process and discover that the mobility decision process is non-Markovian. This is due to its dependence on the system’s history, including social structure and local infrastructure, which evolve based on prior mobility decisions. To make the process Markovian, we extend the state space by incorporating two history-dependent components. Although our model is a very much reduced version of the original one, it remains too complex for the application of usual analytic methods. Instead, we employ simulations to examine the functionalities of the two history-dependent components. We think that the structure of the analyzed stochastic process is exemplary for many socio-technical, -economic, -ecological systems. Additionally, it exhibits analogies with the framework of extended evolution, which has previously been used to study cultural evolution.
    Language: English
    Type: article , doc-type:article
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  • 8
  • 9
    Publication Date: 2024-01-29
    Description: The Robust Perron Cluster Analysis (PCCA+) has become a popular spectral clustering algorithm for coarse-graining transition matrices of nearly decomposable Markov chains with transition states. Originally developed for reversible Markov chains, the algorithm only worked for transition matrices with real eigenvalues. In this paper, we therefore extend the theoretical framework of PCCA+ to Markov chains with a complex eigen-decomposition. We show that by replacing a complex conjugate pair of eigenvectors by their real and imaginary components, a real representation of the same subspace is obtained, which is suitable for the cluster analysis. We show that our approach leads to the same results as the generalized PCCA+ (GPCCA), which replaces the complex eigen-decomposition by a conceptually more difficult real Schur decomposition. We apply the method on non-reversible Markov chains, including circular chains, and demonstrate its efficiency compared to GPCCA. The experiments are performed in the Matlab programming language and codes are provided.
    Language: German
    Type: article , doc-type:article
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  • 10
    Publication Date: 2024-01-31
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 11
    Publication Date: 2024-01-31
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 12
    Publication Date: 2024-02-02
    Description: We analyze how Langevin dynamics is affected by the friction coefficient using an invariant subspace projection of the associated Koopman operator. This provides the friction-dependent metastable macro-states of the dynamical system as well as the transition rates in the entire phase space. We used the algorithm ISOKANN for a wide range of friction coefficient values and reproduced results consistent with the Kramers turnover.
    Language: English
    Type: article , doc-type:article
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  • 13
    Publication Date: 2024-02-02
    Language: English
    Type: article , doc-type:article
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  • 14
    Publication Date: 2024-02-07
    Description: Periodic timetabling is a challenging planning task in public transport. As safety requirements are crucial, track allocation is indispensable for validating the practical feasibility of a railway timetable. For busy stations with limited capacities, this requires a detailed planning of turnarounds. It is therefore desirable to integrate timetabling not only with track allocation, but also with vehicle scheduling and line planning. This is captured by the Integrated Line Planning and Turn-Sensitive Periodic Timetabling Problem with Track Choice, whose MIP formulation has been demonstrated to be effective for construction site railway rescheduling, as long as a good quality initial solution is available. In this paper, we discuss how to generate such a solution by extending the SAT formulation of the Periodic Event Scheduling Problem with track choice, track occupation, and minimum service frequency components. The SAT approach is superior to pure MIP on real-world instances of the S-Bahn Berlin network.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 15
    Publication Date: 2024-02-12
    Description: Collective variables (CVs) are low-dimensional projections of high-dimensional system states. They are used to gain insights into complex emergent dynamical behaviors of processes on networks. The relation between CVs and network measures is not well understood and its derivation typically requires detailed knowledge of both the dynamical system and the network topology. In this Letter, we present a data-driven method for algorithmically learning and understanding CVs for binary-state spreading processes on networks of arbitrary topology. We demonstrate our method using four example networks: the stochastic block model, a ring-shaped graph, a random regular graph, and a scale-free network generated by the Albert-Barabási model. Our results deliver evidence for the existence of low-dimensional CVs even in cases that are not yet understood theoretically.
    Language: English
    Type: article , doc-type:article
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  • 16
    Publication Date: 2024-02-16
    Language: English
    Type: article , doc-type:article
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  • 17
    Publication Date: 2024-02-13
    Description: In this work, we study the geodesics of the space of certain geometrically and physically motivated subspaces of the space of immersed curves endowed with a first order Sobolev metric. This includes elastic curves and also an extension of some results on planar concentric circles to surfaces. The work focuses on intrinsic and constructive approaches.
    Language: English
    Type: article , doc-type:article
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  • 18
    Publication Date: 2024-02-14
    Language: English
    Type: article , doc-type:article
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  • 19
    Publication Date: 2024-02-20
    Description: This article addresses reaction networks in which spatial and stochastic effects are of crucial importance. For such systems, particle-based models allow us to describe all microscopic details with high accuracy. However, they suffer from computational inefficiency if particle numbers and density get too large. Alternative coarse-grained-resolution models reduce computational effort tremendously, e.g., by replacing the particle distribution by a continuous concentration field governed by reaction-diffusion PDEs. We demonstrate how models on the different resolution levels can be combined into hybrid models that seamlessly combine the best of both worlds, describing molecular species with large copy numbers by macroscopic equations with spatial resolution while keeping the stochastic-spatial particle-based resolution level for the species with low copy numbers. To this end, we introduce a simple particle-based model for the binding dynamics of ions and vesicles at the heart of the neurotransmission process. Within this framework, we derive a novel hybrid model and present results from numerical experiments which demonstrate that the hybrid model allows for an accurate approximation of the full particle-based model in realistic scenarios.
    Language: English
    Type: article , doc-type:article
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  • 20
    Publication Date: 2024-02-21
    Description: Finding collective variables to describe some important coarse-grained information on physical systems, in particular metastable states, remains a key issue in molecular dynamics. Recently, machine learning techniques have been intensively used to complement and possibly bypass expert knowledge in order to construct collective variables. Our focus here is on neural network approaches based on autoencoders. We study some relevant mathematical properties of the loss function considered for training autoencoders, and provide physical interpretations based on conditional variances and minimum energy paths. We also consider various extensions in order to better describe physical systems, by incorporating more information on transition states at saddle points, and/or allowing for multiple decoders in order to describe several transition paths. Our results are illustrated on toy two dimensional systems and on alanine dipeptide.
    Language: English
    Type: article , doc-type:article
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  • 21
    Publication Date: 2024-02-27
    Language: German
    Type: incollection , doc-type:Other
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  • 22
    Publication Date: 2024-02-27
    Language: English
    Type: researchdata , doc-type:ResearchData
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  • 23
    Publication Date: 2024-02-28
    Language: English
    Type: article , doc-type:article
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  • 24
    Publication Date: 2024-02-28
    Language: English
    Type: researchdata , doc-type:ResearchData
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  • 25
    Publication Date: 2024-03-04
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 26
    Publication Date: 2024-03-04
    Description: For decades, de Casteljau's algorithm has been used as a fundamental building block in curve and surface design and has found a wide range of applications in fields such as scientific computing, and discrete geometry to name but a few. With increasing interest in nonlinear data science, its constructive approach has been shown to provide a principled way to generalize parametric smooth curves to manifolds. These curves have found remarkable new applications in the analysis of parameter-dependent, geometric data. This article provides a survey of the recent theoretical developments in this exciting area as well as its applications in fields such as geometric morphometrics and longitudinal data analysis in medicine, archaeology, and meteorology.
    Language: English
    Type: article , doc-type:article
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  • 27
    Publication Date: 2024-03-06
    Language: English
    Type: article , doc-type:article
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  • 28
    Publication Date: 2024-03-05
    Description: In our combined experimental, theoretical and numerical work, we study the out of equilibrium deformations in a shrinking ring of optically trapped, interacting colloidal particles. Steerable optical tweezers are used to confine dielectric microparticles along a circle of discrete harmonic potential wells, and to reduce the ring radius at a controlled quench speed. We show that excluded-volume interactions are enough to induce particle sliding from their equilibrium positions and nonequilibrium zigzag roughening of the colloidal structure. Our work unveils the underlying mechanism of interfacial deformation in radially driven microscopic discrete rings.
    Language: English
    Type: article , doc-type:article
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  • 29
    Publication Date: 2024-03-19
    Language: English
    Type: article , doc-type:article
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  • 30
    Publication Date: 2024-03-19
    Description: This work explores a synchronization-like phenomenon induced by common noise for continuous-time Markov jump processes given by chemical reaction networks. Based on Gillespie’s stochastic simulation algorithm, a corresponding random dynamical system is formulated in a two-step procedure, at first for the states of the embedded discrete-time Markov chain and then for the augmented Markov chain including random jump times. We uncover a time-shifted synchronization in the sense that—after some initial waiting time—one trajectory exactly replicates another one with a certain time delay. Whether or not such a synchronization behavior occurs depends on the combination of the initial states. We prove this partial time-shifted synchronization for the special setting of a birth-death process by analyzing the corresponding two-point motion of the embedded Markov chain and determine the structure of the associated random attractor. In this context, we also provide general results on existence and form of random attractors for discrete-time, discrete-space random dynamical systems.
    Language: English
    Type: article , doc-type:article
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  • 31
    Publication Date: 2024-03-19
    Description: Epidemiological models can not only be used to forecast the course of a pandemic like COVID-19, but also to propose and design non-pharmaceutical interventions such as school and work closing. In general, the design of optimal policies leads to nonlinear optimization problems that can be solved by numerical algorithms. Epidemiological models come in different complexities, ranging from systems of simple ordinary differential equations (ODEs) to complex agent-based models (ABMs). The former allow a fast and straightforward optimization, but are limited in accuracy, detail, and parameterization, while the latter can resolve spreading processes in detail, but are extremely expensive to optimize. We consider policy optimization in a prototypical situation modeled as both ODE and ABM, review numerical optimization approaches, and propose a heterogeneous multilevel approach based on combining a fine-resolution ABM and a coarse ODE model. Numerical experiments, in particular with respect to convergence speed, are given for illustrative examples.
    Language: English
    Type: article , doc-type:article
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  • 32
    Publication Date: 2024-03-19
    Language: English
    Type: article , doc-type:article
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  • 33
    Publication Date: 2024-03-19
    Language: English
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  • 34
    Publication Date: 2024-03-19
    Description: We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PdM) by an iterative refinement approach that is based on a state-expanded event-graph. In this graph, the states are parameters of a failure distribution, and paths correspond to vehicle rotations with associated health state approximations. An optimal set of paths including maintenance can be computed by solving an integer linear program. Afterwards, the graph is refined and the procedure repeated. An associated linear program gives rise to a lower bound that can be used to determine the solution quality. Computational results for six instances derived from real-world timetables of a German railway company are presented. The results show the effectiveness of the approach and the quality of the solutions.
    Language: English
    Type: article , doc-type:article
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  • 35
    Publication Date: 2024-03-19
    Language: English
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  • 36
    Publication Date: 2024-03-19
    Description: 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.
    Language: English
    Type: article , doc-type:article
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  • 37
  • 38
    Publication Date: 2024-03-19
    Description: In multipartite Bell scenarios, we study the nonlocality robustness of the Greenberger-Horne-Zeilinger (GHZ) state. When each party performs planar measurements forming a regular polygon, we exploit the symmetry of the resulting correlation tensor to drastically accelerate the computation of (i) a Bell inequality via Frank-Wolfe algorithms and (ii) the corresponding local bound. The Bell inequalities obtained are facets of the symmetrized local polytope and they give the best-known upper bounds on the nonlocality robustness of the GHZ state for three to ten parties. Moreover, for four measurements per party, we generalize our facets and hence show, for any number of parties, an improvement on Mermin's inequality in terms of noise robustness. We also compute the detection efficiency of our inequalities and show that some give rise to the activation of nonlocality in star networks, a property that was only shown with an infinite number of measurements.
    Language: English
    Type: article , doc-type:article
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  • 39
    Publication Date: 2024-03-18
    Description: Agent-based models (ABMs) provide an intuitive and powerful framework for studying social dynamics by modeling the interactions of individuals from the perspective of each individual. In addition to simulating and forecasting the dynamics of ABMs, the demand to solve optimization problems to support, for example, decision-making processes naturally arises. Most ABMs, however, are non-deterministic, high-dimensional dynamical systems, so objectives defined in terms of their behavior are computationally expensive. In particular, if the number of agents is large, evaluating the objective functions often becomes prohibitively time-consuming. We consider data-driven reduced models based on the Koopman generator to enable the efficient solution of multi-objective optimization problems involving ABMs. In a first step, we show how to obtain data-driven reduced models of non-deterministic dynamical systems (such as ABMs) that depend on potentially nonlinear control inputs. We then use them in the second step as surrogate models to solve multi-objective optimal control problems. We first illustrate our approach using the example of a voter model, where we compute optimal controls to steer the agents to a predetermined majority, and then using the example of an epidemic ABM, where we compute optimal containment strategies in a prototypical situation. We demonstrate that the surrogate models effectively approximate the Pareto-optimal points of the ABM dynamics by comparing the surrogate-based results with test points, where the objectives are evaluated using the ABM. Our results show that when objectives are defined by the dynamic behavior of ABMs, data-driven surrogate models support or even enable the solution of multi-objective optimization problems.
    Language: English
    Type: article , doc-type:article
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  • 40
    Publication Date: 2024-03-18
    Description: The microphysical structure of the lunar regolith provides information on the geologic history of the Moon. We used remote sensing measurements of thermal emission and a thermophysical model to determine the microphysical properties of the lunar regolith. We expand upon previous investigations by developing a microphysical thermal model, which more directly simulates regolith properties, such as grain size and volume filling factor. The modeled temperatures are matched with surface temperatures measured by the Diviner Lunar Radiometer Experiment on board the Lunar Reconnaissance Orbiter. The maria and highlands are investigated separately and characterized in the model by a difference in albedo and grain density. We find similar regolith temperatures for both terrains, which can be well described by similar volume filling factor profiles and mean grain sizes obtained from returned Apollo samples. We also investigate a significantly lower thermal conductivity for highlands, which formally also gives a very good solution, but in a parameter range that is well outside the Apollo data. We then study the latitudinal dependence of regolith properties up to ±80° latitude. When assuming constant regolith properties, we find that a variation of the solar incidence-dependent albedo can reduce the initially observed latitudinal gradient between model and Diviner measurements significantly. A better match between measurements and model can be achieved by a variation in intrinsic regolith properties with a decrease in bulk density with increasing latitude. We find that a variation in grain size alone cannot explain the Diviner measurements at higher latitudes.
    Language: English
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  • 41
    Publication Date: 2024-03-14
    Language: English
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  • 42
    Publication Date: 2024-03-14
    Description: In this article, we propose an interval constraint programming method for globally solving catalog-based categorical optimization problems. It supports catalogs of arbitrary size and properties of arbitrary dimension, and does not require any modeling effort from the user. A novel catalog-based contractor (or filtering operator) guarantees consistency between the categorical properties and the existing catalog items. This results in an intuitive and generic approach that is exact, rigorous (robust to roundoff errors) and can be easily implemented in an off-the-shelf interval-based continuous solver that interleaves branching and constraint propagation. We demonstrate the validity of the approach on a numerical problem in which a categorical variable is described by a two-dimensional property space. A Julia prototype is available as open-source software under the MIT license.
    Language: English
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  • 43
    Publication Date: 2024-03-14
    Language: English
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  • 44
    Publication Date: 2024-03-14
    Description: Task-adapted image reconstruction methods using end-to-end trainable neural networks (NNs) have been proposed to optimize reconstruction for subsequent processing tasks, such as segmentation. However, their training typically requires considerable hardware resources and thus, only relatively simple building blocks, e.g. U-Nets, are typically used, which, albeit powerful, do not integrate model-specific knowledge. In this work, we extend an end-to-end trainable task-adapted image reconstruction method for a clinically realistic reconstruction and segmentation problem of bone and cartilage in 3D knee MRI by incorporating statistical shape models (SSMs). The SSMs model the prior information and help to regularize the segmentation maps as a final post-processing step. We compare the proposed method to a state-of-the-art (SOTA) simultaneous multitask learning approach for image reconstruction and segmentation (MTL) and to a complex SSMs-informed segmentation pipeline (SIS). Our experiments show that the combination of joint end-to-end training and SSMs to further regularize the segmentation maps obtained by MTL highly improves the results, especially in terms of mean and maximal surface errors. In particular, we achieve the segmentation quality of SIS and, at the same time, a substantial model reduction that yields a five-fold decimation in model parameters and a computational speedup of an order of magnitude. Remarkably, even for undersampling factors of up to R=8, the obtained segmentation maps are of comparable quality to those obtained by SIS from ground-truth images.
    Language: English
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  • 45
    Publication Date: 2024-03-07
    Description: The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bug fixes. The SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. The SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver.
    Language: English
    Type: reportzib , doc-type:preprint
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  • 46
    Publication Date: 2024-03-26
    Description: We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state (NESS) systems. EPR-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product space. Remarkably, our loss function has an intimate connection with the steady entropy production rate (EPR), enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness, and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an 8D limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.
    Language: English
    Type: article , doc-type:article
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  • 47
    Publication Date: 2024-03-26
    Description: Grazing-incidence X-ray diffraction (GIXRD) is a scattering technique which allows one to characterize the structure of fluid interfaces down to the molecular scale, including the measurement of the surface tension and of the interface roughness. However, the corresponding standard data analysis at non-zero wave numbers has been criticized as to be inconclusive because the scattering intensity is polluted by the unavoidable scattering from the bulk. Here we overcome this ambiguity by proposing a physically consistent model of the bulk contribution which is based on a minimal set of assumptions of experimental relevance. To this end, we derive an explicit integral expression for the background scattering, which can be determined numerically from the static structure factors of the coexisting bulk phases as independent input. Concerning the interpretation of GIXRD data inferred from computer simulations, we account also for the finite sizes of the bulk phases, which are unavoidable in simulations. The corresponding leading-order correction beyond the dominant contribution to the scattered intensity is revealed by asymptotic analysis, which is characterized by the competition between the linear system size and the X-ray penetration depth in the case of simulations. Specifically, we have calculated the expected GIXRD intensity for scattering at the planar liquid--vapor interface of Lennard-Jones fluids with truncated pair interactions via extensive, high-precision simulations. The reported data cover interfacial and bulk properties of fluid states along the whole liquid--vapor coexistence line. A sensitivity analysis demonstrates the robustness of our findings concerning the detailed definition of the mean interface position. We conclude that previous claims of an enhanced surface tension at mesoscopic scales are amenable to unambiguous tests via scattering experiments.
    Language: English
    Type: article , doc-type:article
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  • 48
    Publication Date: 2024-03-26
    Description: Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based methods, such as the Square Root Approximation (SqRA), directly derive these quantities from the potential energy functions. In this article, we demonstrate how the SqRA formalism naturally blends with the tensor structure obtained by coupling multiple systems, resulting in the tensor-based Square Root Approximation (tSqRA). It enables efficient treatment of high-dimensional systems using the SqRA and provides an algebraic expression of the impact of coupling energies between molecular subsystems. Based on the tSqRA, we also develop the projected rate estimation, a hybrid data-model-based algorithm that efficiently estimates the slowest rates for coupled systems. In addition, we investigate the possibility of integrating low-rank approximations within this framework to maximize the potential of the tSqRA.
    Language: English
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  • 49
    Publication Date: 2024-03-22
    Description: Markov processes serve as foundational models in many scientific disciplines, such as molecular dynamics, and their simulation forms a common basis for analysis. While simulations produce useful trajectories, obtaining macroscopic information directly from microstate data presents significant challenges. This paper addresses this gap by introducing the concept of membership functions being the macrostates themselves. We derive equations for the holding times of these macrostates and demonstrate their consistency with the classical definition. Furthermore, we discuss the application of the ISOKANN method for learning these quantities from simulation data. In addition, we present a novel method for extracting transition paths based on the ISOKANN results and demonstrate its efficacy by applying it to simulations of the 𝜇-opioid receptor. With this approach we provide a new perspective on analyzing the macroscopic behaviour of Markov systems.
    Language: English
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  • 50
    Publication Date: 2024-03-22
    Description: We propose two graph neural network layers for graphs with features in a Riemannian manifold. First, based on a manifold-valued graph diffusion equation, we construct a diffusion layer that can be applied to an arbitrary number of nodes and graph connectivity patterns. Second, we model a tangent multilayer perceptron by transferring ideas from the vector neuron framework to our general setting. Both layers are equivariant with respect to node permutations and isometries of the feature manifold. These properties have been shown to lead to a beneficial inductive bias in many deep learning tasks. Numerical examples on synthetic data as well as on triangle meshes of the right hippocampus to classify Alzheimer's disease demonstrate the very good performance of our layers.
    Language: English
    Type: article , doc-type:article
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  • 51
    Publication Date: 2024-03-21
    Language: English
    Type: article , doc-type:article
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  • 52
    Publication Date: 2024-03-21
    Description: 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.
    Language: English
    Type: doctoralthesis , doc-type:doctoralThesis
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  • 53
    Publication Date: 2024-03-21
    Language: English
    Type: article , doc-type:article
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  • 54
    Publication Date: 2024-03-21
    Description: It has been shown that any 9 by 9 Sudoku puzzle must contain at least 17 clues to have a unique solution. This paper investigates the more specific question: given a particular completed Sudoku grid, what is the minimum number of clues in any puzzle whose unique solution is the given grid? We call this problem the Minimum Sudoku Clue Problem (MSCP). We formulate MSCP as a binary bilevel linear program, present a class of globally valid inequalities, and provide a computational study on 50 MSCP instances of 9 by 9 Sudoku grids. Using a general bilevel solver, we solve 95% of instances to optimality, and show that the solution process benefits from the addition of a moderate amount of inequalities. Finally, we extend the proposed model to other combinatorial problems in which uniqueness of the solution is of interest.
    Language: English
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  • 55
    Publication Date: 2024-03-21
    Language: English
    Type: article , doc-type:article
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  • 56
    Publication Date: 2024-03-21
    Description: The dominant eigenfunctions of the Koopman operator characterize the metastabilities and slow-timescale dynamics of stochastic diffusion processes. In the context of molecular dynamics and Markov state modeling, they allow for a description of the location and frequencies of rare transitions, which are hard to obtain by direct simulation alone. In this article, we reformulate the eigenproblem in terms of the ISOKANN framework, an iterative algorithm that learns the eigenfunctions by alternating between short burst simulations and a mixture of machine learning and classical numerics, which naturally leads to a proof of convergence. We furthermore show how the intermediate iterates can be used to reduce the sampling variance by importance sampling and optimal control (enhanced sampling), as well as to select locations for further training (adaptive sampling). We demonstrate the usage of our proposed method in experiments, increasing the approximation accuracy by several orders of magnitude.
    Language: English
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  • 57
    Book
    Book
    Beazil ; Mexico ; Singapore :Cengage Learning,
    Title: Introductory econometrics : a modern approach
    Author: Wooldridge, Jeffrey M.
    Edition: 8th edition
    Publisher: Beazil ; Mexico ; Singapore :Cengage Learning,
    Year of publication: 2024
    Pages: XXII, 826 Seiten
    ISBN: 978-0-357-90016-1
    Type of Medium: Book
    Language: English
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  • 58
    Title: Logic-Based Benders Decomposition : Theory and Applications
    Author: Hooker, John
    Edition: 1st ed. 2024.
    Publisher: Cham :Springer,
    Year of publication: 2024
    ISBN: 978-3-031-45039-6
    Type of Medium: Book
    Language: English
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  • 59
    Title: Künstliche Intelligenz und wissenschaftliches Arbeiten : ChatGPT & Co.: Der Turbo für ein erfolgreiches Studium
    Author: Bucher, Ulrich
    Contributer: Holzweißig, Kai , Schwarzer, Markus
    Publisher: München :Verlag Franz Vahlen,
    Year of publication: 2024
    Pages: X, 181 Seiten
    ISBN: 978-3-8006-7322-3
    Type of Medium: Book
    Language: German
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  • 60
    Title: Anwendungen mit GPT-4 und ChatGPT entwickeln : intelligente Chatbots, Content-Generatoren und mehr erstellen
    Author: Caelen, Olivier
    Contributer: Blete, Marie-Alice
    Edition: 1. Auflage
    Publisher: Heidelberg :O'Reilly,
    Year of publication: 2024
    Pages: 158 Seiten
    ISBN: 978-3-96009-241-4
    Type of Medium: Book
    Language: German
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  • 61
    Publication Date: 2023-01-28
    Description: Consolidation of commodities and coordination of vehicle routes are fundamental features of supply chain management problems. While locations for consolidation and coordination are typically known a priori, in adaptive transportation networks this is not the case. The identification of such consolidation locations forms part of the decision making process. Supply chain management problems integrating the designation of consolidation locations with the coordination of long haul and local vehicle routing is not only challenging to solve, but also very difficult to formulate mathematically. In this paper, the first mathematical model integrating location clustering with long haul and local vehicle routing is proposed. This mathematical formulation is used to develop algorithms to find high quality solutions. A novel parallel framework is developed that combines exact and heuristic methods to improve the search for high quality solutions and provide valid bounds. The results demonstrate that using exact methods to guide heuristic search is an effective approach to find high quality solutions for difficult supply chain management problems.
    Language: English
    Type: reportzib , doc-type:preprint
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  • 62
    Publication Date: 2023-02-06
    Language: English
    Type: article , doc-type:article
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  • 63
    Publication Date: 2023-03-20
    Description: We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and 1.45 billion citations on 254 subjects from 1981 to 2020. We proposed the Article’s Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.
    Language: English
    Type: article , doc-type:article
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  • 64
    Publication Date: 2023-03-28
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 65
    Publication Date: 2023-04-17
    Language: English
    Type: article , doc-type:article
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  • 66
    Publication Date: 2023-04-26
    Description: Reaction coordinates (RCs) are indicators of hidden, low-dimensional mechanisms that govern the long-term behavior of high-dimensional stochastic processes. We present a novel and general variational characterization of optimal RCs and provide conditions for their existence. Optimal RCs are minimizers of a certain loss function, and reduced models based on them guarantee a good approximation of the statistical long-term properties of the original high-dimensional process. We show that for slow-fast systems, metastable systems, and other systems with known good RCs, the novel theory reproduces previous insight. Remarkably, for reversible systems, the numerical effort required to evaluate the loss function scales only with the variability of the underlying, low-dimensional mechanism, and not with that of the full system. The theory provided lays the foundation for an efficient and data-sparse computation of RCs via modern machine learning techniques.
    Language: English
    Type: article , doc-type:article
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  • 67
    Publication Date: 2023-04-26
    Description: Tackling societal challenges relating to sustainability requires both an understanding of the underlying complex socio-ecological systems and participation of scientists as well as relevant stakeholders, such as practice experts, decision makers, and citizens. This paper introduces the Decision Theatre Triangle, a method which combines empirical information, mathematical modelling and simulation, and a format for dialogue between scientists and stakeholders. While it builds on previous Decision Theatre work, the new structuring into these three elements emphasizes what is needed for setting up a Decision Theatre for a given challenge. Based on experience with a specific example – sustainable mobility in Germany – it is argued that agent-based models are particularly suitable for Decision Theatres and that the method is useful not only for decision support but also for science communication and co-creation of a deeper knowledge of the system under discussion. As a step towards facilitating a broader use of the Decision Theatre Triangle method, the paper then sketches research needs for each of its three elements, with a focus on mathematical modelling and simulation.
    Language: English
    Type: article , doc-type:article
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  • 68
    Publication Date: 2023-04-26
    Description: The remarkably complex skeletal systems of the sea stars (Echinodermata, Asteroidea), consisting of hundreds to thousands of individual elements (ossicles), have intrigued investigators for more than 150 years. While the general features and structural diversity of isolated asteroid ossicles have been well documented in the literature, the task of mapping the spatial organization of these constituent skeletal elements in a whole-animal context represents an incredibly laborious process, and as such, has remained largely unexplored. To address this unmet need, particularly in the context of understanding structure-function relationships in these complex skeletal systems, we present an integrated approach that combines micro-computed tomography, semi-automated ossicle segmentation, data visualization tools, and the production of additively manufactured tangible models to reveal biologically relevant structural data that can be rapidly analyzed in an intuitive manner. In the present study, we demonstrate this high-throughput workflow by segmenting and analyzing entire skeletal systems of the giant knobby star, Pisaster giganteus, at four different stages of growth. The in-depth analysis, presented herein, provides a fundamental understanding of the three-dimensional skeletal architecture of the sea star body wall, the process of skeletal maturation during growth, and the relationship between skeletal organization and morphological characteristics of individual ossicles. The widespread implementation of this approach for investigating other species, subspecies, and growth series has the potential to fundamentally improve our understanding of asteroid skeletal architecture and biodiversity in relation to mobility, feeding habits, and environmental specialization in this fascinating group of echinoderms.
    Language: English
    Type: article , doc-type:article
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  • 69
    Publication Date: 2023-04-26
    Description: For over ten years, the constraint integer programming framework SCIP has been extended by capabilities for the solution of convex and nonconvex mixed-integer nonlinear programs (MINLPs). With the recently published version~8.0, these capabilities have been largely reworked and extended. This paper discusses the motivations for recent changes and provides an overview of features that are particular to MINLP solving in SCIP. Further, difficulties in benchmarking global MINLP solvers are discussed and a comparison with several state-of-the-art global MINLP solvers is provided.
    Language: English
    Type: reportzib , doc-type:preprint
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  • 70
    Publication Date: 2023-05-04
    Language: English
    Type: article , doc-type:article
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  • 71
    Publication Date: 2023-04-20
    Language: English
    Type: article , doc-type:article
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  • 72
    Publication Date: 2023-04-19
    Description: Existing planning approaches for onshore wind farm siting and network integration often do not meet minimum cost solutions or social and environmental considerations. In this paper, we develop an approach for the multi-objective optimization of turbine locations and their network connection using a Quota Steiner tree problem. Applying a novel transformation on a known directed cut formulation, reduction techniques, and heuristics, we design an exact solver that makes large problem instances solvable and outperforms generic MIP solvers. Although our case studies in selected regions of Germany show large trade-offs between the objective criteria of cost and landscape impact, small burdens on one criterion can significantly improve the other criteria. In addition, we demonstrate that contrary to many approaches for exclusive turbine siting, network integration must be simultaneously optimized in order to avoid excessive costs or landscape impacts in the course of a wind farm project. Our novel problem formulation and the developed solver can assist planners in decision making and help optimize wind farms in large regions in the future.
    Language: English
    Type: reportzib , doc-type:preprint
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  • 73
    Publication Date: 2023-05-31
    Description: Das vorliegende Statuspapier beschreibt ein Konzept zur weitergehenden Abwasserbehandlung für die Bewertung von Aufbereitungsverfahren, sowohl in einer Pilotphase zur Auswahl von Verfah- rensoptionen als auch für die Bewertung großtechnischer Anlagen.
    Language: German
    Type: book , doc-type:book
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  • 74
    Publication Date: 2023-06-12
    Language: English
    Type: article , doc-type:article
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  • 75
    Publication Date: 2023-06-12
    Description: This thesis considers the transient gas network control optimization problem for on-shore pipeline-based transmission networks with numerous gas routing options. As input, the problem is given the network's topology, its initial state, and future demands at the boundaries of the network, which prescribe the gas flow exchange and potentially the pressure values. The task is to find a set of future control measures for all the active, i.e., controllable, elements in the network that minimizes a combination of different penalty functions. The problem is examined in the context of a decision support tool for gas network dispatchers. This results in detailed models featuring a diverse set of constraints, large and challenging real-world instances, and demanding time limit requirements. All these factors further complicate the problem, which is already difficult to solve in theory due to the inherent combination of non-linear and combinatorial aspects. Our contributions concern different steps of the process of solving the problem. Regarding the model formulation, we investigate the validity of two common approximations of the gas flow description in transport pipes: neglecting the inertia term and assuming a friction term that linearly depends on the gas flow and the pressure. For both, we examine if they can be applied under real-world conditions by evaluating a large amount of historical state data of the network of our project partner, the gas network operator Open Grid Europe. While we can confirm that it is reasonable to ignore the influence of the inertia term, the friction term linearization leads to significant errors and, as a consequence, cannot be used for describing the general gas flow behavior in transport pipes. As another topic of this thesis, we introduce the target value concept as a more realistic approach to express control actions of dispatchers regarding regulators and compressor stations. Here, we derive the mechanisms defined for target values based on the gas flow principles in pipes and develop a mixed-integer programming model capturing their behavior. The accuracy of this model is demonstrated in comparison to a target-value-based industry-standard simulator. Furthermore, we present two heuristics for the transient gas network control optimization problem featuring target values that are based on approximative models for the target-value-based control and determine the final decisions in a post-processing step. To compare the performance of the two heuristics with the approach of directly solving the corresponding model, we evaluate them on a set of artificially created test instances. Finally, we develop problem-specific algorithms for two variants of the described problem. One considers the control optimization for a single network station, which represents a local operation site featuring a large number of active elements. The used transient model is very detailed and includes a sophisticated representation of the compressor stations. Based on the shortness of the pipes in the station, the corresponding algorithm finds valid solutions by solving a series of stationary model variants as well as a transient rolling horizon approach. As the second variant, we consider the problem on the entire network but assume an approximative model representing the control capabilities of network stations. Aside from a new description of the compression capabilities, we introduce an algorithm that uses a combination of sequential mixed-integer programming, two heuristics based on reduced time horizons, and a specialized dynamic branch-and-bound node limit to determine promising values for the binary variables of the model. Complete solutions for the problem are obtained by fixing the binary values and solving the remaining non-linear program. Both algorithms are investigated in extensive empirical studies based on real-world instances of the corresponding model variants.
    Description: Diese Arbeit behandelt das Optimierungsproblem der transienten Gasnetzwerksteuerung von Fernleitungsnetzen auf dem Festland mit einer großen Anzahl möglicher Gastransportrouten. Die Eingabedaten bestehen aus der Netzwerktopologie, dem Anfangszustand des Netzes und zukünftigen Vorgaben an den Randknoten des Netzes, welche den Gaseinfluss und Gasausfluss sowie eine potenzielle Vorgabe von Druckwerten umfassen. Gegeben diese Daten besteht die Aufgabe besteht darin, eine Menge an zukünftigen Steuerungsentscheidungen für alle aktiven, also steuerbaren, Elemente des Netzes zu finden, sodass eine Kombination von Straffunktionen minimiert wird. Das Problem wird in dieser Arbeit im Rahmen der Erstellung eines entscheidungsunterstützenden Systems für Dispatcher betrachtet, welche das Gasnetz steuern. Dies resultiert in einer detaillierten Modellierung mit einer Vielzahl von Nebenbedingungen, großen und herausfordernden realistischen Instanzen sowie anspruchsvollen Vorgaben zur maximalen Laufzeit. Diese Eigenschaften erhöhen die Komplexität des Problems, welches bereits in der Theorie auf Grund der inhärenten Kombination von nichtlinearen und kombinatorischen Aspekten schwierig zu lösen ist. Die Beiträge dieser Arbeit betreffen verschiedene Schritte des Prozesses zur Lösung des Problems. Bezüglich der Modellformulierung werden zwei übliche Approximationen der Gasflussbeschreibung in Fernleitungsrohren auf Validität überprüft: die Vernachlässigung des Trägheitsterms und die Annahme einer linearisierten Beschreibung des Reibungsterms. Für beide Approximationen wird untersucht, ob sie für reale Gasflussbedingungen zulässig sind. Dazu wird eine große Anzahl historischer Netzzustandsdaten des Gasnetzbetreibers Open Grid Europe ausgewertet. Während bestätigt werden kann, dass eine Vernachlässigung des Trägheitsterms unter Realbedingungen angemessen ist, führt die Linearisierung des Reibungsterms zu signifikanten Fehlern und kann daher nicht für die allgemeine Beschreibung des Gasflusses in Fernleitungsrohren verwendet werden. In einem weiteren Teil dieser Arbeit wird das Konzept der Sollwerte eingeführt. Mit diesen ist eine realistischere Beschreibung der Steuerungsbefehle möglich, welche den Dispatchern für Regler und Verdichterstationen zur Verfügung stehen. Der Sollwertmechanismus wird basierend auf den Gasflussprinzipien in Rohrleitungen hergeleitet, um anschließend ein gemischt-ganzzahliges Programm zu entwickeln, welches das entsprechende Verhalten erzeugt. Die Präzision dieses Modells wird durch einen Vergleich mit einem Simulator von Industriestandard sichergestellt, welcher auf Sollwerten basiert. Außerdem werden zwei Heuristiken für das Optimierungsproblem der transienten Gasnetzwerksteuerung mit Sollwertmodellierung vorgestellt. Diese basieren auf approximativen Modellen für die Sollwertsteuerung und ermitteln die letztendlichen Steuerungsentscheidungen in einer nachgelagerten Routine. Basierend auf künstlich erzeugten Testinstanzen werden die Heuristiken schließlich mit dem direkten Lösen des entsprechenden Modells verglichen. Zudem werden in dieser Arbeit problemspezifische Algorithmen für zwei Varianten des beschriebenen Optimierungsproblems entwickelt. Die erste Variante betrachtet das Gasnetzwerksteuerungsproblem beschränkt auf eine einzelne Netzstation, die lokale Betriebsstellen darstellen und über eine Vielzahl an aktiven Steuerungselementen verfügen. Das entsprechende transiente Modell ist sehr detailliert und beinhaltet eine differenzierte Beschreibung der Verdichterstationen. Der problemspezifische Algorithmus basiert auf der Kürze der Rohre innerhalb der Station und findet zulässige Lösungen durch das Lösen von stationären Varianten des Modells sowie der Nutzung eines transienten Rolling-Horizon Ansatzes. In der zweiten Problemvariante wird das gesamte Gasnetz betrachtet, wobei eine vereinfachte Modellierung der Steuerungsmöglichkeiten innerhalb von Netzstationen angenommen wird. Neben einer neuen Beschreibung der Verdichtungsmöglichkeiten einer Station wird ebenfalls ein problemspezifischer Algorithmus entwickelt. Dieser erstellt aussichtsreiche Werte für die Binärvariablen und nutzt dafür eine Kombination aus sequenzieller gemischt-ganzzahliger Programmierung, zwei auf verkürzten Zeithorizonten basierenden Heuristiken und eine spezialisierte dynamische Obergrenze für die Anzahl der Branch-and-Bound-Knoten. Diese Teillösungen werden durch eine Fixierung der binären Variablen und das anschließende Lösen des restlichen nichtlinearen Programms komplettiert. Die Güte beider Algorithmen wird in umfangreichen empirischen Experimenten untersucht, welche reale Instanzen der jeweiligen Problemvarianten betrachten.
    Language: English
    Type: doctoralthesis , doc-type:doctoralThesis
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  • 76
    Publication Date: 2023-06-16
    Description: 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.
    Description: 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.
    Language: English
    Type: masterthesis , doc-type:masterThesis
    Format: application/pdf
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  • 77
    Publication Date: 2023-06-14
    Description: The Periodic Event Scheduling Problem (PESP) is the central mathematical tool for periodic timetable optimization in public transport. PESP can be formulated in several ways as a mixed-integer linear program with typically general integer variables. We investigate the split closure of these formulations and show that split inequalities are identical with the recently introduced flip inequalities. While split inequalities are a general mixed-integer programming technique, flip inequalities are defined in purely combinatorial terms, namely cycles and arc sets of the digraph underlying the PESP instance. It is known that flip inequalities can be separated in pseudo-polynomial time. We prove that this is best possible unless P $=$ NP, but also observe that the complexity becomes linear-time if the cycle defining the flip inequality is fixed. Moreover, introducing mixed-integer-compatible maps, we compare the split closures of different formulations, and show that reformulation or binarization by subdivision do not lead to stronger split closures. Finally, we estimate computationally how much of the optimality gap of the instances of the benchmark library PESPlib can be closed exclusively by split cuts, and provide better dual bounds for five instances.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 78
    Publication Date: 2023-07-17
    Language: English
    Type: article , doc-type:article
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  • 79
  • 80
    Publication Date: 2023-07-17
    Language: English
    Type: researchdata , doc-type:ResearchData
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  • 81
    Publication Date: 2023-07-06
    Description: Solving high-dimensional partial differential equations is a recurrent challenge in economics, science and engineering. In recent years, a great number of computational approaches have been developed, most of them relying on a combination of Monte Carlo sampling and deep learning based approximation. For elliptic and parabolic problems, existing methods can broadly be classified into those resting on reformulations in terms of backward stochastic differential equations (BSDEs) and those aiming to minimize a regression-type L2-error (physics-informed neural networks, PINNs). In this paper, we review the literature and suggest a methodology based on the novel diffusion loss that interpolates between BSDEs and PINNs. Our contribution opens the door towards a unified understanding of numerical approaches for high-dimensional PDEs, as well as for implementations that combine the strengths of BSDEs and PINNs. The diffusion loss furthermore bears close similarities to (least squares) temporal difference objectives found in reinforcement learning. We also discuss eigenvalue problems and perform extensive numerical studies, including calculations of the ground state for nonlinear Schr ¨odinger operators and committor functions relevant in molecular dynamics.
    Language: English
    Type: article , doc-type:article
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  • 82
    Publication Date: 2023-07-06
    Description: Die europaische Gasinfrastruktur wird disruptiv in ein zukunftiges dekarbonisiertes Energiesystem verändert; ein Prozess, der angesichts der jüngsten politischen Situation beschleunigt werden muss. Mit einem wachsenden Wasserstoffmarkt wird der pipelinebasierte Transport unter Nutzung der bestehenden Erdgasinfrastruktur wirtschaftlich sinnvoll, trägt zur Erhöhung der öffentlichen Akzeptanz bei und beschleunigt den Umstellungsprozess. In diesem Beitrag wird die maximal technisch machbare Einspeisung von Wasserstoff in das bestehende deutsche Erdgastransportnetz hinsichtlich regulatorischer Grenzwerte der Gasqualität analysiert. Die Analyse erfolgt auf Basis eines transienten Tracking-Modells, das auf dem allgemeinen Pooling-Problem einschließlich Linepack aufbaut. Es zeigt sich, dass das Gasnetz auch bei strengen Grenzwerten gen ̈ugend Kapazität bietet, um für einen großen Teil der bis 2030 geplanten Erzeugungskapazität für grünen Wasserstoff als garantierter Abnehmer zu dienen.
    Description: The European gas infrastructure is being disruptively transformed into a future decarbonised energy system; a process that needs to be accelerated given the recent political situation. With a growing hydrogen market, pipeline-based transport using the existing natural gas infrastructure becomes economically viable, helps to increase public acceptance and accelerates the transition process. In this paper, the maximum technically feasible feed-in of hydrogen into the existing German natural gas transport network is analysed with regard to regulatory limits of gas quality. Analysis is based on a transient tracking model that builds on the general pooling problem including linepack. It is shown that even with strict limits, the gas grid offers sufficient capacity to serve as a guaranteed customer for a large part of the green hydrogen generation capacity planned until 2030.
    Language: German
    Type: article , doc-type:article
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  • 83
    Publication Date: 2023-07-06
    Description: Using the recently proposed maximal quadratic-free sets and the well-known monoidal strengthening procedure, we show how to improve inter- section cuts for quadratically-constrained optimization problems by exploiting integrality requirements. We provide an explicit construction that allows an efficient implementation of the strengthened cuts along with computational results showing their improvements over the standard intersection cuts. We also show that, in our setting, there is unique lifting which implies that our strengthening procedure is generating the best possible cut coefficients for the integer variables.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 84
    Publication Date: 2023-07-06
    Description: Die europäische Gasinfrastruktur wird disruptiv in ein zukünftiges dekarbonisiertes Energiesystem verändert; ein Prozess, der angesichts der jüngsten politischen Situation beschleunigt werden muss. Mit einem wachsenden Wasserstoffmarkt wird der pipelinebasierte Transport unter Nutzung der bestehenden Erdgasinfrastruktur wirtschaftlich sinnvoll, trägt zur Erhöhung der öffentlichen Akzeptanz bei und beschleunigt den Umstellungsprozess. In diesem Fachbeitrag wird die maximal technisch machbare Einspeisung von Wasserstoff in das bestehende deutsche Erdgastransportnetz hinsichtlich regulatorischer Grenzwerte der Gasqualität analysiert. Die Analyse erfolgt auf Basis eines transienten Tracking-Modells, das auf dem allgemeinen Pooling-Problem einschließlich Linepack aufbaut. Es zeigt sich, dass das Gasnetz auch bei strengen Grenzwerten genügend Kapazität bietet, um für einen großen Teil der bis 2030 geplanten Erzeugungskapazität für grünen Wasserstoff als garantierter Abnehmer zu dienen.
    Language: German
    Type: article , doc-type:article
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  • 85
    Publication Date: 2023-07-06
    Description: In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types at arbitrary time points early in the year with a single trained model (progressive intra-season classification). Such early season predictions are of practical relevance for instance for yield forecasts or the modeling of agricultural water balances, therefore being important for the public as well as the private sector. Furthermore, we improve the mechanism of combining different data sources for the prediction task, allowing for both optical and radar data as inputs (multi-modal data fusion) without the need for temporal interpolation. We can demonstrate the effectiveness of our approach on an extensive data set from three federal states of Germany reaching an average F1 score of 0.92 using data of a complete growing season to predict the eight most important crop types and an F1 score above 0.8 when doing early classification at least one month before harvest time. In carefully chosen experiments, we can show that our model generalizes well in time and space.
    Language: English
    Type: article , doc-type:article
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  • 86
    Publication Date: 2023-07-06
    Description: Recently, a series of papers proposed deep learning-based approaches to sample from unnormalized target densities using controlled diffusion processes. In this work, we identify these approaches as special cases of the Schrödinger bridge problem, seeking the most likely stochastic evolution between a given prior distribution and the specified target. We further generalize this framework by introducing a variational formulation based on divergences between path space measures of time-reversed diffusion processes. This abstract perspective leads to practical losses that can be optimized by gradient-based algorithms and includes previous objectives as special cases. At the same time, it allows us to consider divergences other than the reverse Kullback-Leibler divergence that is known to suffer from mode collapse. In particular, we propose the so-called log-variance loss, which exhibits favorable numerical properties and leads to significantly improved performance across all considered approaches.
    Language: English
    Type: article , doc-type:article
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  • 87
    facet.materialart.
    Unknown
    Publication Date: 2023-07-06
    Description: This Package implements a variation of the Voronoi Graph Traversal algorithm by Polianskii and Pokorny [1]. It constructs a Voronoi Diagram from a set of points by performing a random walk on the graph of the vertices of the diagram. Unlike many other Voronoi implementations this algorithm is not limited to 2 or 3 dimensions and promises good performance even in higher dimensions.
    Language: English
    Type: software , doc-type:Other
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  • 88
    Publication Date: 2023-07-06
    Description: The optical inspection of the surfaces of diode lasers, especially the p-sides and facets, is an essential part of the quality control in the laser fabrication procedure. With reliable, fast, and flexible optical inspection processes, it is possible to identify and eliminate defects, accelerate device selection, reduce production costs, and shorten the cycle time for product development. Due to a vast range of rapidly changing designs, structures, and coatings, however, it is impossible to realize a practical inspection with conventional software. In this work, we therefore suggest a deep learning based defect detection algorithm that builds on a Faster Regional Convolutional Neural Network (Faster R-CNN) as a core component. While for related, more general object detection problems, the application of such models is straightforward, it turns out that our task exhibits some additional challenges. On the one hand, a sophisticated pre- and postprocessing of the data has to be deployed to make the application of the deep learning model feasible. On the other hand, we find that creating labeled training data is not a trivial task in our scenario, and one has to be extra careful with model evaluation. We can demonstrate in multiple empirical assessments that our algorithm can detect defects in diode lasers accurately and reliably in most cases. We analyze the results of our production-ready pipeline in detail, discuss its limitations and provide some proposals for further improvements.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 89
    Publication Date: 2023-06-19
    Language: English
    Type: masterthesis , doc-type:masterThesis
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  • 90
    Publication Date: 2023-08-02
    Description: The Feasibility Pump (FP) is one of the best-known primal heuristics for mixed-integer programming (MIP): more than 15 papers suggested various modifications of all of its steps. So far, no variant considered information across multiple iterations, but all instead maintained the principle to optimize towards a single reference integer point. In this paper, we evaluate the usage of multiple reference vectors in all stages of the FP algorithm. In particular, we use LP-feasible vectors obtained during the main loop to tighten the variable domains before entering the computationally expensive enumeration stage. Moreover, we consider multiple integer reference vectors to explore further optimizing directions and introduce alternative objective scaling terms to balance the contributions of the distance functions and the original MIP objective. Our computational experiments demonstrate that the new method can improve performance on general MIP test sets. In detail, our modifications provide a 29.3% solution quality improvement and 4.0% running time improvement in an embedded setting, needing 16.0% fewer iterations over a large test set of MIP instances. In addition, the method’s success rate increases considerably within the first few iterations. In a standalone setting, we also observe a moderate performance improvement, which makes our version of FP suitable for the two main use-cases of the algorithm.
    Language: English
    Type: article , doc-type:article
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  • 91
    Publication Date: 2023-08-02
    Description: Der DWA-Themenband beschreibt ein Konzept zur weitergehenden Abwasserbehandlung für die Bewertung von Aufbereitungsverfahren, sowohl in einer Pilotphase zur Auswahl von Verfahrensoptionen als auch für die Bewertung großtechnischer Anlagen. Emissionsseitig basiert das Konzept auf bereits regulatorisch definierten Parametern wie anorganischen Stickstoff-Verbindungen oder Phosphat sowie auf neuen noch nicht in der Abwasserverordnung regulierten Parametern. Die immissionsseitige Betrachtung erfolgt auf Basis der rechtlich durch die Europäische Wasserrahmenrichtlinie und andere Anforderungen bindenden Instrumente. Hierfür werden spezifische Vorgehensweisen vorgeschlagen. Anhand zweier ausgewählter Praxisbeispiele wird deutlich, dass es zur Bewertung der Verfahrensoptionen an einem Standort dienlich ist, ausgewählte Reduktionen bzw. Entfernungen von Stoffen, Organismen und Effekten zu bestimmen.
    Language: German
    Type: incollection , doc-type:Other
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  • 92
    Publication Date: 2023-08-02
    Description: We present a fully computer-assisted proof system for solving a particular family of problems in Extremal Combinatorics. Existing techniques using Flag Algebras have proven powerful in the past, but have so far lacked a computational counterpart to derive matching constructive bounds. We demonstrate that common search heuristics are capable of finding constructions far beyond the reach of human intuition. Additionally, the most obvious downside of such heuristics, namely a missing guarantee of global optimality, can often be fully eliminated in this case through lower bounds and stability results coming from the Flag Algebra approach. To illustrate the potential of this approach, we study two related and well-known problems in Extremal Graph Theory that go back to questions of Erdős from the 60s. Most notably, we present the first major improvement in the upper bound of the Ramsey multiplicity of the complete graph on 4 vertices in 25 years, precisely determine the first off-diagonal Ramsey multiplicity number, and settle the minimum number of independent sets of size four in graphs with clique number strictly less than five.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 93
    Publication Date: 2023-08-02
    Description: Fibrotic tissue is one of the main risk factors for cardiac arrhythmias. It is therefore a key component in computational studies. In this work, we compare the monodomain equation to two eikonal models for cardiac electrophysiology in the presence of fibrosis. We show that discontinuities in the conductivity field, due to the presence of fibrosis, introduce a delay in the activation times. The monodomain equation and eikonal-diffusion model correctly capture these delays, contrarily to the classical eikonal equation. Importantly, a coarse space discretization of the monodomain equation amplifies these delays, even after accounting for numerical error in conduction velocity. The numerical discretization may also introduce artificial conduction blocks and hence increase propagation complexity. Therefore, some care is required when comparing eikonal models to the discretized monodomain equation.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 94
    Publication Date: 2023-08-02
    Description: The maximum-cut problem is one of the fundamental problems in combinatorial optimization. With the advent of quantum computers, both the maximum-cut and the equivalent quadratic unconstrained binary optimization problem have experienced much interest in recent years. This article aims to advance the state of the art in the exact solution of both problems—by using mathematical programming techniques. The main focus lies on sparse problem instances, although also dense ones can be solved. We enhance several algorithmic components such as reduction techniques and cutting-plane separation algorithms, and combine them in an exact branch-and-cut solver. Furthermore, we provide a parallel implementation. The new solver is shown to significantly outperform existing state-of-the-art software for sparse maximum-cut and quadratic unconstrained binary optimization instances. Furthermore, we improve the best known bounds for several instances from the 7th DIMACS Challenge and the QPLIB, and solve some of them (for the first time) to optimality.
    Language: English
    Type: article , doc-type:article
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  • 95
    Publication Date: 2023-08-02
    Description: Cutting plane selection is a subroutine used in all modern mixed-integer linear programming solvers with the goal of selecting a subset of generated cuts that induce optimal solver performance. These solvers have millions of parameter combinations, and so are excellent candidates for parameter tuning. Cut selection scoring rules are usually weighted sums of different measurements, where the weights are parameters. We present a parametric family of mixed-integer linear programs together with infinitely many family-wide valid cuts. Some of these cuts can induce integer optimal solutions directly after being applied, while others fail to do so even if an infinite amount are applied. We show for a specific cut selection rule, that any finite grid search of the parameter space will always miss all parameter values, which select integer optimal inducing cuts in an infinite amount of our problems. We propose a variation on the design of existing graph convolutional neural networks, adapting them to learn cut selection rule parameters. We present a reinforcement learning framework for selecting cuts, and train our design using said framework over MIPLIB 2017 and a neural network verification data set. Our framework and design show that adaptive cut selection does substantially improve performance over a diverse set of instances, but that finding a single function describing such a rule is difficult. Code for reproducing all experiments is available at https://github.com/Opt-Mucca/Adaptive-Cutsel-MILP.
    Language: English
    Type: article , doc-type:article
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  • 96
    Publication Date: 2023-08-03
    Description: Concrete plays a central role as the standard building material in civil engineering. Experimental characterization of the concrete microstructure and a description of failure mechanisms are important to understand the concrete’s mechanical properties. Computed tomography is a powerful source of information as it yields 3d images of concrete specimens. However, complete visual inspection is often infeasible due to very large image sizes. Hence, automatic methods for crack detection and segmentation are needed. A region-growing algorithm and a 3d U-Net showed promising results in a previous study. Cracks in normal concrete and high-performance concrete that were initiated via tensile tests were investigated. Here, the methods are validated on a more diverse set of concrete types and crack characteristics. Adequate adaptions of the methods are necessary to deal with the complex crack structures. The segmentation results are assessed qualitatively and compared to those of a template matching algorithm which is well-established in industry.
    Language: English
    Type: article , doc-type:article
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  • 97
    Publication Date: 2023-08-03
    Description: Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the user’s trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Thus, we conducted two perceptual studies to determine the suitability of the line variables blur, dashing, grayscale, sketchiness, and width for distinguishing several values in molecular visualizations. While our work was motivated by uncertainty visualization, our techniques and study results also apply to other types of scalar data.
    Language: English
    Type: article , doc-type:article
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  • 98
    Publication Date: 2023-08-01
    Description: It has recently been shown that ISTA, an unaccelerated optimization method, presents sparse updates for the ℓ1-regularized undirected personalized PageRank problem (Fountoulakis et al., 2019), leading to cheap iteration complexity and providing the same guarantees as the approximate personalized PageRank algorithm (APPR) (Andersen et al., 2006). In this work, we design an accelerated optimization algorithm for this problem that also performs sparse updates, providing an affirmative answer to the COLT 2022 open question of Fountoulakis and Yang (2022). Acceleration provides a reduced dependence on the condition number, while the dependence on the sparsity in our updates differs from the ISTA approach. Further, we design another algorithm by using conjugate directions to achieve an exact solution while exploiting sparsity. Both algorithms lead to faster convergence for certain parameter regimes. Our findings apply beyond PageRank and work for any quadratic objective whose Hessian is a positive-definite 푀-matrix.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 99
    Publication Date: 2023-08-01
    Language: English
    Type: article , doc-type:article
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  • 100
    Publication Date: 2023-08-01
    Language: English
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