arXiv subjects

Christian Mayer

Publications and source records attributed to Christian Mayer.

12 recordsLinked to original sources

arXiv2026

OptiQU: Coordinated Multi-Level Voltage and Reactive Power Control for Enhanced Voltage Quality and Secure Grid Operation

Modern low-voltage (LV) distribution grids face rising shares of photovoltaic generation and high-power loads such as heat pumps and electric vehicle charging stations. Due to high simultaneity, voltage constraints often become binding before thermal limits, triggering costly conventional grid reinforcement measures. Existing voltage and reactive power control in LV grids - e.g., fixed cos($\phi$) or Q(V) control of distributed generators, on-load tap-changing distribution transformers, and line voltage regulators - is typically applied locally and independently, leaving reactive power flexibility potential unused. This paper presents OptiQU, a coordinated voltage and reactive power control concept for medium-voltage (MV) and LV distribution grids, combining centralised optimisation with decentralised local control and fallback strategies. The approach coordinates operational targets and setpoints across MV and LV (e.g., DER reactive power and substation equipment) to mitigate voltage violations and curtailment and to increase hosting capacity, while enabling robust operation under limited communication. The concepts are being evaluated using representative MV/LV models in simulation and lab environments and will be validated in field tests with two German DSOs. Based on existing research, the coordinated approach is expected to increase the exploitable flexibility for upstream voltage and reactive power control. The planned evaluation will quantify this potential and investigate trade-offs between performance, communication effort, and resilience.

eess.SY↗

arXiv2025

Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge

The automated analysis of the aortic vessel tree (AVT) from computed tomography angiography (CTA) holds immense clinical potential, but its development has been impeded by a lack of shared, high-quality data. We launched the SEG.A. challenge to catalyze progress in this field by introducing a large, publicly available, multi-institutional dataset for AVT segmentation. The challenge benchmarked automated algorithms on a hidden test set, with subsequent optional tasks in surface meshing for computational simulations. Our findings reveal a clear convergence on deep learning methodologies, with 3D U-Net architectures dominating the top submissions. A key result was that an ensemble of the highest-ranking algorithms significantly outperformed individual models, highlighting the benefits of model fusion. Performance was strongly linked to algorithmic design, particularly the use of customized post-processing steps, and the characteristics of the training data. This initiative not only establishes a new performance benchmark but also provides a lasting resource to drive future innovation toward robust, clinically translatable tools.

cs.CV↗

arXiv2021

Automated Essay Scoring Using Transformer Models

Automated essay scoring (AES) is gaining increasing attention in the education sector as it significantly reduces the burden of manual scoring and allows ad hoc feedback for learners. Natural language processing based on machine learning has been shown to be particularly suitable for text classification and AES. While many machine-learning approaches for AES still rely on a bag-of-words (BOW) approach, we consider a transformer-based approach in this paper, compare its performance to a logistic regression model based on the BOW approach and discuss their differences. The analysis is based on 2,088 email responses to a problem-solving task, that were manually labeled in terms of politeness. Both transformer models considered in that analysis outperformed without any hyper-parameter tuning the regression-based model. We argue that for AES tasks such as politeness classification, the transformer-based approach has significant advantages, while a BOW approach suffers from not taking word order into account and reducing the words to their stem. Further, we show how such models can help increase the accuracy of human raters, and we provide a detailed instruction on how to implement transformer-based models for one's own purpose.

cs.CL↗

arXiv2020

Diffusion and viscosity of non-entangled polyelectrolytes

We report chain self-diffusion and viscosity data for sodium polystyrene sulfonate (NaPSS) in semidilute salt-free aqueous solutions measured by pulsed field gradient NMR and rotational rheometry respectively. The observed concentration dependence of $\eta$ and $D$ are consistent with the Rouse-Zimm scaling model with a concentration dependent monomeric friction coefficient. The concentration dependence of the monomeric friction coefficient exceeds that expected from free-volume models of diffusion, and its origin remains unclear. Correlation blobs and dilute chains with equivalent end-to-end distances exhibit nearly equal friction coefficients, in agreement with scaling. The viscosity and diffusion data are combined using the Rouse model to calculate the chain dimensions of NaPSS in salt-free solution, these agree quantitatively with SANS measurements.

cond-mat.soft↗

arXiv2018

HYPE: Massive Hypergraph Partitioning with Neighborhood Expansion

Many important real-world applications-such as social networks or distributed data bases-can be modeled as hypergraphs. In such a model, vertices represent entities-such as users or data records-whereas hyperedges model a group membership of the vertices-such as the authorship in a specific topic or the membership of a data record in a specific replicated shard. To optimize such applications, we need an efficient and effective solution to the NP-hard balanced k-way hypergraph partitioning problem. However, existing hypergraph partitioners that scale to very large graphs do not effectively exploit the hypergraph structure when performing the partitioning decisions. We propose HYPE, a hypergraph partitionier that exploits the neighborhood relations between vertices in the hypergraph using an efficient implementation of neighborhood expansion. HYPE improves partitioning quality by up to 95% and reduces runtime by up to 39% compared to streaming partitioning.

cs.DC↗

arXiv2018

ADWISE: Adaptive Window-based Streaming Edge Partitioning for High-Speed Graph Processing

In recent years, the graph partitioning problem gained importance as a mandatory preprocessing step for distributed graph processing on very large graphs. Existing graph partitioning algorithms minimize partitioning latency by assigning individual graph edges to partitions in a streaming manner --- at the cost of reduced partitioning quality. However, we argue that the mere minimization of partitioning latency is not the optimal design choice in terms of minimizing total graph analysis latency, i.e., the sum of partitioning and processing latency. Instead, for complex and long-running graph processing algorithms that run on very large graphs, it is beneficial to invest more time into graph partitioning to reach a higher partitioning quality --- which drastically reduces graph processing latency. In this paper, we propose ADWISE, a novel window-based streaming partitioning algorithm that increases the partitioning quality by always choosing the best edge from a set of edges for assignment to a partition. In doing so, ADWISE controls the partitioning latency by adapting the window size dynamically at run-time. Our evaluations show that ADWISE can reach the sweet spot between graph partitioning latency and graph processing latency, reducing the total latency of partitioning plus processing by up to 23-47 percent compared to the state-of-the-art.

cs.DC↗

arXiv2018

Q-Graph: Preserving Query Locality in Multi-Query Graph Processing

Arising user-centric graph applications such as route planning and personalized social network analysis have initiated a shift of paradigms in modern graph processing systems towards multi-query analysis, i.e., processing multiple graph queries in parallel on a shared graph. These applications generate a dynamic number of localized queries around query hotspots such as popular urban areas. However, existing graph processing systems are not yet tailored towards these properties: The employed methods for graph partitioning and synchronization management disregard query locality and dynamism which leads to high query latency. To this end, we propose the system Q-Graph for multi-query graph analysis that considers query locality on three levels. (i) The query-aware graph partitioning algorithm Q-cut maximizes query locality to reduce communication overhead. (ii) The method for synchronization management, called hybrid barrier synchronization, allows for full exploitation of local queries spanning only a subset of partitions. (iii) Both methods adapt at runtime to changing query workloads in order to maintain and exploit locality. Our experiments show that Q-cut reduces average query latency by up to 57 percent compared to static query-agnostic partitioning algorithms.

cs.DB↗

arXiv2017

The TensorFlow Partitioning and Scheduling Problem: It's the Critical Path!

State-of-the-art data flow systems such as TensorFlow impose iterative calculations on large graphs that need to be partitioned on heterogeneous devices such as CPUs, GPUs, and TPUs. However, partitioning can not be viewed in isolation. Each device has to select the next graph vertex to be executed, i.e., perform local scheduling decisions. Both problems, partitioning and scheduling, are NP-complete by themselves but have to be solved in combination in order to minimize overall execution time of an iteration. In this paper, we propose several heuristic strategies to solve the partitioning and scheduling problem in TensorFlow. We simulate the performance of the proposed strategies in heterogeneous environments with communication-intensive workloads that are common to TensorFlow. Our findings indicate that the best partitioning and scheduling heuristics are those that focus on minimizing the execution time of the critical path in the graph. Those strategies provide a speed-up of up to 4 times in comparison to strategies that are agnostic to the critical path, such as hash-based partitioning and FIFO scheduling.

cs.DC↗

arXiv2017

StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.

cs.DC↗

arXiv2008

Multiple glass transitions in star polymer mixtures: Insights from theory and simulations

The glass transition in binary mixtures of star polymers is studied by mode coupling theory and extensive molecular dynamics computer simulations. In particular, we have explored vitrification in the parameter space of size asymmetry $δ$ and concentration $ρ_2$ of the small star polymers at fixed concentration of the large ones. Depending on the choice of parameters, three different glassy states are identified: a single glass of big polymers at low $δ$ and low $ρ_2$, a double glass at high $δ$ and low $ρ_2$, and a novel double glass at high $ρ_2$ and high $δ$ which is characterized by a strong localization of the small particles. At low $δ$ and high $ρ_2$ there is a competition between vitrification and phase separation. Centered in the $(δ, ρ_2)$-plane, a liquid lake shows up revealing reentrant glass formation. We compare the behavior of the dynamical density correlators with the predictions of the theory and find remarkable agreement between the two.

cond-mat.soft↗

arXiv2006

Collapse of Telechelic Star Polymers to Water-Melon Structures

Conformational properties of star-shaped polymer aggregates that carry attractive end-groups, called telechelic star polymers, are investigated by simulation and analytical variational theory. We focus on the case of low telechelic star polymer functionalities, $f \leq 5$, a condition which allows aggregation of all attractive monomers on one site. We establish the functionality- and polymerization-number dependence of the transition temperature from the "star burst'' to the "water melon" macroparticle structure. Extensions to telechelic stars featuring partially collapsed configurations are also discussed.

cond-mat.soft↗

arXiv2004

Equilibrium properties of highly asymmetric star-polymer mixtures

We employ effective interaction potentials to study the equilibrium structure and phase behavior of highly asymmetric mixtures of star polymers. We consider in particular the influence of the addition of a component with a small number of arms and a small size on a concentrated solution of large stars with a high functionality. By employing liquid integral equation theories we examine the evolution of the correlation functions of the big stars upon addition of the small ones, finding a loss of structure that can be attributed to a weakening of the repulsions between the large stars due to the presence of the small ones. We analyze this phenomenon be means of a generalized depletion mechanism which is supported by computer simulations. By applying thermodynamic perturbation theory we draw the phase diagram of the asymmetric mixture, finding that the addition of small stars melts the crystal formed by the big ones. A systematic comparison between the two- and effective one-component descriptions of the mixture that corroborates the reliability of the generalized depletion picture is also carried out.

cond-mat.soft↗