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Finding reliable approximations to the quantum many-body problem is one of the central challenges of modern physics. Elemental to this endeavor is the development of advanced numerical techniques pushing the limits of what is tractable. One such recently proposed numerical technique are neural quantum states. This new type of wavefunction based Ans\"atze utilizes the expressivity of neural networks to tackle fundamentally challenging problems, such as the Mott transition. In this paper we aim to gauge the universalness of one representative of neural network Ans\"atze, the hidden-fermion slater determinant approach. To this end, we study five different fermionic models each displaying volume law scaling of the entanglement entropy. For these, we correlate the effectiveness of the Ansatz with different complexity measures. Each measure indicates a different complexity in the absence of which a conventional Ansatz becomes efficient. We provide evidence that whenever one of the measures indicates proximity to a parameter region in which a conventional approach would work reliable, the neural network approach also works reliable and efficient. This highlights the great potential, but also challenges for neural network approaches: Finding suitable points in theory space around which to construct the Ansatz in order to be able to efficiently treat models unsuitable for their current designs., Comment: 20 pages, 25 figures

The quantum dynamics of correlated fermionic or bosonic many-body systems following external excitation can be successfully studied using nonequilibrium Green functions (NEGF) or reduced density matrix methods. Approximations are introduced via a proper choice of the many-particle selfenergy or decoupling of the BBGKY-hierarchy, respectively. These approximations are based on Feynman's diagram approaches or on cluster expansions into single-particle and correlation operators. In a recent paper [E. Schroedter, J.-P. Joost, and M. Bonitz, Cond. Matt. Phys. \textbf{25}, 23401 (2022)] we have presented a different approach where, instead of equations of motion for the many-particle NEGF (or density operators), equations for the correlation functions of fluctuations are analyzed. In particular, we derived the stochastic GW and polarization approximations that are closely related to the nonequilibrium GW approximation. Here, we extend this approach to the computation of two-time observables depending on the specific ordering of the underlying operators. In particular, we apply this extension to the calculation of the density correlation function and dynamic structure factor of correlated Hubbard clusters in and out of equilbrium.

In-air microfluidics became a new method for technical production processes with ultra-high throughput formerly performed in micro channels. Direct Numerical Simulations (DNS) provide a valuable contribution for the fundamental understanding of multiphase flow and later application design. This chapter presents a feasibility study with first DNS results of droplet-jet collisions of immiscible liquids using the in-house software Free Surface 3D (FS3D). Two cases were investigated with a setup comparable to experiments by Baumgartner et al. [1], where a droplet chain of a glycerol solution hits a jet of silicon oil which encapsulates the droplets. The droplets' shapes present are observed to be more complex than comprehensible from the two-dimensional images from the experiments. Thus, DNS with FS3D can provide additional information like the surface area or the velocity contributions in order to find analytical models of such collision processes in the future. Simulations of such increasingly complex systems require constant improvement of the numerical solver regarding the code's performance. Thus, the red-black Gauss-Seidel smoother in the multi-grid solver, the iterative red-black scheme to compute the viscous forces as well as the momentum advection method were enhanced with a cache- and memory usage optimization. An overall performance gain of up to 33% was obtained for a representative test case., Comment: This is a preprint of a chapter which will be published in the conference proceedings High Performance Computing in Science and Engineering '22, edited by W. E. Nagel, D. H. Kr\"oner and M. M. Resch, Springer reproduced with permission of Springer Nature Switzerland AG. (Changelog V2: expected publisher and editor added)

Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity measures or good representations for the traffic scenarios. In this work, an expert-knowledge aided representation learning for traffic scenarios is presented. The latent space so formed is used for successful clustering and novel scenario type detection. Expert-knowledge is used to define objectives that the latent representations of traffic scenarios shall fulfill. It is presented, how the network architecture and loss is designed from these objectives, thereby incorporating expert-knowledge. An automatic mining strategy for traffic scenarios is presented, such that no manual labeling is required. Results show the performance advantage compared to baseline methods. Additionally, extensive analysis of the latent space is performed., Comment: Copyright 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Representation learning in recent years has been addressed with self-supervised learning methods. The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view prediction. Augmentation is one of the key components in cross-view self-supervised learning frameworks to learn visual representations. This paper presents ExAgt, a novel method to include expert knowledge for augmenting traffic scenarios, to improve the learnt representations without any human annotation. The expert-guided augmentations are generated in an automated fashion based on the infrastructure, the interactions between the EGO and the traffic participants and an ideal sensor model. The ExAgt method is applied in two state-of-the-art cross-view prediction methods and the representations learnt are tested in downstream tasks like classification and clustering. Results show that the ExAgt method improves representation learning compared to using only standard augmentations and it provides a better representation space stability. The code is available at https://github.com/lab176344/ExAgt., Comment: Accepted as a conference paper in ITSC 2022, Macau, China

We investigate the time-resolved photocurrent response of CdSe quantum dot (QD) thin films sensitized with zinc beta-tetraaminophthalocyanine (Zn4APc) on three different substrates, namely silicon with 230 nm SiO2 dielectric, glass as well as polyimide. While Si/SiO2(230 nm) is not suitable for any transient photocurrent characterization due to an interfering photocurrent response of the buried silicon, we find that polyimide substrates invoke the larger optical bandwidth with 85 kHz vs. 67 kHz for the same quantum dot thin film on glass. Upon evaluation of the transient photocurrent, we find that the photoresponse of the CdSe quantum dot films can be described as a combination of carrier recombination and fast trapping within 2.7 ns, followed by slower multiple trapping events. The latter are less pronounced on polyimide, which leads to the higher bandwidth. We show that all devices are RC-time limited and that improvements of the photoresistance are the key to further increasing the bandwidth., Comment: 33 pages, 17 figures

Maintenance scheduling is a complex decision-making problem in the production domain, where a number of maintenance tasks and resources has to be assigned and scheduled to production entities in order to prevent unplanned production downtime. Intelligent maintenance strategies are required that are able to adapt to the dynamics and different conditions of production systems. The paper introduces a deep reinforcement learning approach for condition-oriented maintenance scheduling in flow line systems. Different policies are learned, analyzed and evaluated against a benchmark scheduling heuristic based on reward modelling. The evaluation of the learned policies shows that reinforcement learning based maintenance strategies meet the requirements of the presented use case and are suitable for maintenance scheduling in the shop floor., Comment: Accepted at the IEEE International Conference on Industrial Informatics (INDIN) 2021

Traffic scenario categorisation is an essential component of automated driving, for e.\,g., in motion planning algorithms and their validation. Finding new relevant scenarios without handcrafted steps reduce the required resources for the development of autonomous driving dramatically. In this work, a method is proposed to address this challenge by introducing a clustering technique based on a novel data-adaptive similarity measure, called Random Forest Activation Pattern (RFAP) similarity. The RFAP similarity is generated using a tree encoding scheme in a Random Forest algorithm. The clustering method proposed in this work takes into account that there are labelled scenarios available and the information from the labelled scenarios can help to guide the clustering of unlabelled scenarios. It consists of three steps. First, a self-supervised Convolutional Neural Network~(CNN) is trained on all available traffic scenarios using a defined self-supervised objective. Second, the CNN is fine-tuned for classification of the labelled scenarios. Third, using the labelled and unlabelled scenarios an iterative optimisation procedure is performed for clustering. In the third step at each epoch of the iterative optimisation, the CNN is used as a feature generator for an unsupervised Random Forest. The trained forest, in turn, provides the RFAP similarity to adapt iteratively the feature generation process implemented by the CNN. Extensive experiments and ablation studies have been done on the highD dataset. The proposed method shows superior performance compared to baseline clustering techniques., Comment: Accepted for IEEE Intelligent Vehicles 2021

Detecting unknown and untested scenarios is crucial for scenario-based testing. Scenario-based testing is considered to be a possible approach to validate autonomous vehicles. A traffic scenario consists of multiple components, with infrastructure being one of it. In this work, a method to detect novel traffic scenarios based on their infrastructure images is presented. An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection. The triplet training of the network is based on the connectivity graphs of the infrastructure. By using the proposed architecture, expert-knowledge is used to shape the latent space such that it incorporates a pre-defined similarity in the neighborhood relationships of an autoencoder. An ablation study on the architecture is highlighting the importance of the triplet autoencoder combination. The best performing architecture is based on vision transformers, a convolution-free attention-based network. The presented method outperforms other state-of-the-art outlier detection approaches., Comment: Copyright 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

A novel unsupervised outlier score, which can be embedded into graph based dimensionality reduction techniques, is presented in this work. The score uses the directed nearest neighbor graphs of those techniques. Hence, the same measure of similarity that is used to project the data into lower dimensions, is also utilized to determine the outlier score. The outlier score is realized through a weighted normalized entropy of the similarities. This score is applied to road infrastructure images. The aim is to identify newly observed infrastructures given a pre-collected base dataset. Detecting unknown scenarios is a key for accelerated validation of autonomous vehicles. The results show the high potential of the proposed technique. To validate the generalization capabilities of the outlier score, it is additionally applied to various real world datasets. The overall average performance in identifying outliers using the proposed methods is higher compared to state-of-the-art methods. In order to generate the infrastructure images, an openDRIVE parsing and plotting tool for Matlab is developed as part of this work. This tool and the implementation of the entropy based outlier score in combination with Uniform Manifold Approximation and Projection are made publicly available., Comment: Copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper. The procedure yields an unsupervised machine learning method. The algorithm generates a proximity matrix which contains a similarity measure. This matrix is then reordered with hierarchical clustering to achieve a graphically interpretable representation. It is shown how the resulting proximity matrix can be visually interpreted and how the variation of the methods' metaparameter reveals different insights into the data. The proposed method is able to cluster data from any data source. To demonstrate the methods' potential, multiple features derived from a traffic simulation are used in this paper. The knowledge of traffic scenario clusters is crucial to accelerate the validation process. The clue of the method is that scenario templates can be generated automatically from actual traffic situations. These templates can be employed in all stages of the development process. The results prove that the procedure is well suited for an automatic categorization of traffic scenarios. Diverse other applications can benefit from this work., Comment: Copyright 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

The goal of this paper is to provide a method, which is able to find categories of traffic scenarios automatically. The architecture consists of three main components: A microscopic traffic simulation, a clustering technique and a classification technique for the operational phase. The developed simulation tool models each vehicle separately, while maintaining the dependencies between each other. The clustering approach consists of a modified unsupervised Random Forest algorithm to find a data adaptive similarity measure between all scenarios. As part of this, the path proximity, a novel technique to determine a similarity based on the Random Forest algorithm is presented. In the second part of the clustering, the similarities are used to define a set of clusters. In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase. A thresholding technique is described to ensure a certain confidence level for the class assignment. The method is applied for highway scenarios. The results show that the proposed method is an excellent approach to automatically categorize traffic scenarios, which is particularly relevant for testing autonomous vehicle functionality., Comment: Copyright 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

This work provides a comprehensive analysis on naturalistic driving behavior for highways based on the highD data set. Two thematic fields are considered. First, some macroscopic and microscopic traffic statistics are provided. These include the traffic flow rate and the traffic density, as well as velocity, acceleration and distance distributions. Additionally, the dependencies to each other are examined and compared to related work. The second part investigates the distributions of criticality measures. The Time-To-Collision, Time-Headway and a third measure, which couples both, are analyzed. These measures are also combined with other indicators. Scenarios, in which these measures reach a critical level, are separately discussed. The results are compared to related work as well. The two main contributions of this work can be stated as follows. First, the analysis on the criticality measures can be used to find suitable thresholds for rare traffic scenarios. Second, the statistics provided in this work can also be utilized for traffic modeling, for example in simulation environments.