Publications at the Faculty of Computer Science and Automation since 2015

Results: 1905
Created on: Wed, 27 Mar 2024 23:30:23 +0100 in 0.0639 sec


Jing, Ying; Numssen, Ole; Weise, Konstantin; Kalloch, Benjamin; Buchberger, Lena; Haueisen, Jens; Hartwigsen, Gesa; Knösche, Thomas R.
Modeling the effects of transcranial magnetic stimulation on spatial attention. - In: Physics in medicine and biology, ISSN 1361-6560, Bd. 68 (2023), 21, 214001, S. 1-16

Objectives. Transcranial magnetic stimulation (TMS) has been widely used to modulate brain activity in healthy and diseased brains, but the underlying mechanisms are not fully understood. Previous research leveraged biophysical modeling of the induced electric field (E-field) to map causal structure-function relationships in the primary motor cortex. This study aims at transferring this localization approach to spatial attention, which helps to understand the TMS effects on cognitive functions, and may ultimately optimize stimulation schemes. Approach. Thirty right-handed healthy participants underwent a functional magnetic imaging (fMRI) experiment, and seventeen of them participated in a TMS experiment. The individual fMRI activation peak within the right inferior parietal lobule (rIPL) during a Posner-like attention task defined the center target for TMS. Thereafter, participants underwent 500 Posner task trials. During each trial, a 5-pulse burst of 10 Hz repetitive TMS (rTMS) was given over the rIPL to modulate attentional processing. The TMS-induced E-fields for every cortical target were correlated with the behavioral modulation to identify relevant cortical regions for attentional orientation and reorientation. Main results. We did not observe a robust correlation between E-field strength and behavioral outcomes, highlighting the challenges of transferring the localization method to cognitive functions with high neural response variability and complex network interactions. Nevertheless, TMS selectively inhibited attentional reorienting in five out of seventeen subjects, resulting in task-specific behavioral impairments. The BOLD-measured neuronal activity and TMS-evoked neuronal effects showed different patterns, which emphasizes the principal distinction between the neural activity being correlated with (or maybe even caused by) particular paradigms, and the activity of neural populations exerting a causal influence on the behavioral outcome. Significance. This study is the first to explore the mechanisms of TMS-induced attentional modulation through electrical field modeling. Our findings highlight the complexity of cognitive functions and provide a basis for optimizing attentional stimulation protocols.



https://doi.org/10.1088/1361-6560/acff34
Köcher, Chris; Kuske, Dietrich
Forwards- and backwards-reachability for cooperating multi-pushdown systems. - In: Fundamentals of computation theory, (2023), S. 318-332

A cooperating multi-pushdown system consists of a tuple of pushdown systems that can delegate the execution of recursive procedures to sub-tuples; control returns to the calling tuple once all sub-tuples finished their task. This allows the concurrent execution since disjoint sub-tuples can perform their task independently. Because of the concrete form of recursive descent into sub-tuples, the content of the multi-pushdown does not form an arbitrary tuple of words, but can be understood as a Mazurkiewicz trace. For such systems, we prove that the backwards reachability relation efficiently preserves recognizability, generalizing a result and proof technique by Bouajjani et al. for single-pushdown systems. While this preservation does not hold for the forwards reachability relation, we can show that it efficiently preserves the rationality of a set of configurations; the proof of this latter result is inspired by the work by Finkel et al. It follows that the reachability relation is decidable for cooperating multi-pushdown systems in polynomial time and the same holds, e.g., for safety and liveness properties given by recognizable sets of configurations.



https://doi.org/10.1007/978-3-031-43587-4_23
Aganian, Dustin; Köhler, Mona; Stephan, Benedict; Eisenbach, Markus; Groß, Horst-Michael
Fusing hand and body skeletons for human action recognition in assembly. - In: Artificial Neural Networks and Machine Learning - ICANN 2023, (2023), S. 207-219

As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial. Cobots should be able to recognize human actions to assist with assembly tasks and act autonomously. To achieve this, skeleton-based approaches are often used due to their ability to generalize across various people and environments. Although body skeleton approaches are widely used for action recognition, they may not be accurate enough for assembly actions where the worker’s fingers and hands play a significant role. To address this limitation, we propose a method in which less detailed body skeletons are combined with highly detailed hand skeletons. We investigate CNNs and transformers, the latter of which are particularly adept at extracting and combining important information from both skeleton types using attention. This paper demonstrates the effectiveness of our proposed approach in enhancing action recognition in assembly scenarios.



https://doi.org/10.1007/978-3-031-44207-0_18
Arnold, Oksana; Franke, Ronny; Jantke, Klaus P.; Knauf, Rainer; Schramm, Tanja; Wache, Hans-Holger
Thinking and chatting deontically - novel support of communication for learning and training with time travel prevention games. - In: Creative approaches to technology-enhanced learning for the workplace and higher education, (2023), S. 25-37

The authors’ key area of application is training for the prevention of accidents in the process technology industries. They run a professional training center with own 3D virtual environments. At TLIC 2021, four of the present authors delivered a contribution advocating planning of human training experiences as dynamically as managing some severely disturbed technical system back into a normal operation - such as an out of control chemical reactor - and enabling human trainees who failed to complete a risky task - thereby possibly ruining a (fortunately only virtual) technical installation - to virtually travel back in time to make good the damage. At TLIC 2022, they introduced cascades of gradually more intricate categories of time travel games. With every step from one category to the next, the deployed AI gets more powerful and effective in providing adaptive guidance of human trainees. The most advanced time travel games are those that allow for the dynamic modification of events experienced in the virtual past. In this way, the game system evolves over time and adapts to the needs of human trainees with emphasis on guidance for trainees who fail repeatedly. The extended team of authors presents a novel perspective at time travel prevention games that leads to a more human-centered adaptive guidance. Training is seen through the lens of deontic modal logic. The focus is on undesired events such as explosions, fire, health hazards due to toxic vapors, and the like. The game system’s AI is reasoning about necessity and possibility of such events. It offers to human trainees/players helpful chats about modalities of decisive events of training.



https://doi.org/10.1007/978-3-031-41637-8_3
Teutsch, Philipp; Käufer, Theo; Mäder, Patrick; Cierpka, Christian
Data-driven estimation of scalar quantities from planar velocity measurements by deep learning applied to temperature in thermal convection. - In: Experiments in fluids, ISSN 1432-1114, Bd. 64 (2023), 12, 191, S. 1-18

The measurement of the transport of scalar quantities within flows is oftentimes laborious, difficult or even unfeasible. On the other hand, velocity measurement techniques are very advanced and give high-resolution, high-fidelity experimental data. Hence, we explore the capabilities of a deep learning model to predict the scalar quantity, in our case temperature, from measured velocity data. Our method is purely data-driven and based on the u-net architecture and, therefore, well-suited for planar experimental data. We demonstrate the applicability of the u-net on experimental temperature and velocity data, measured in large aspect ratio Rayleigh-Bénard convection at Pr = 7.1 and Ra = 2 x 10^5, 4 x 10^5, 7 x 10^5. We conduct a hyper-parameter optimization and ablation study to ensure appropriate training convergence and test different architectural variations for the u-net. We test two application scenarios that are of interest to experimentalists. One, in which the u-net is trained with data of the same experimental run and one in which the u-net is trained on data of different Ra. Our analysis shows that the u-net can predict temperature fields similar to the measurement data and preserves typical spatial structure sizes. Moreover, the analysis of the heat transfer associated with the temperature showed good agreement when the u-net is trained with data of the same experimental run. The relative difference between measured and reconstructed local heat transfer of the system characterized by the Nusselt number Nu is between 0.3 and 14.1% depending on Ra. We conclude that deep learning has the potential to supplement measurements and can partially alleviate the expense of additional measurement of the scalar quantity.



https://doi.org/10.1007/s00348-023-03736-2
Mondal, Niladri; Block, Dimitri; Kroll, Björn; Klingler, Florian
Performance evaluation and application of real-time communication with 5G IIoT. - In: Kommunikation in der Automation, (2023), 9, insges. 10 S.

In communication systems, high data rates combined with low end-to-end latencies are prime necessities for allowing a wide variety of applications, e.g., streaming of video and data or in context of IoT Systems. In contrast, applications in Industry Automation require deterministic end-to-end latencies with guaranteed deadlines. In communication systems, data rates, reliability and the achievable end-to-end latency are often a trade-off (e.g., due to buffering of data, and overall systems-design). Further, most communication systems are optimized for high data rates only, yet, deterministic end-to-end latencies are required for most Industrial Use-Cases, which are still not considered well enough in research and standardization. In this paper we focus on low-latency communication, and, outline the importance of this research aspect. Consequently, we propose a novel Mini-Slot approach for 5G and beyond communication systems to tackle the problem of minimizing uplink- and downlink communication latencies in cellular networks under load. First evaluations of our approach in context of a feasibility study show promising results. As comparison in realistic experiments with Rel-15-based 5G Commercial off-the-shelf ( COTS ) hardware, a baseline scenario (unoptimized) shows a maximum latency up to 49.04 ms. In contrast to that, our novel mini-slot approach allows to lower the maximum end-to-end communication latency to 15.51 ms. This way, our mini-slot approach constitutes as enabler for low-latency communication by using Rel-15-based 5G COTS and User Equipment ( UE ) hardware for industrial use-cases, without the need to wait for further releases of 5G systems.



https://opendata.uni-halle.de//handle/1981185920/113598
Gärtner, Christoph; Rizk, Amr; Koldehofe, Boris; Guillaume, René; Kundel, Ralf; Steinmetz, Ralf
Demo: Flexibility-aware network management of time-sensitive flows. - In: SIGCOMM '23, (2023), S. 1176-1178

We investigate the application of a recently published metric for flexibility in the context of combined port queue schedules of network paths in Time-Sensitive Networks (TSN). TSN comprises a set of specifications for deterministic networking, including support for scheduled traffic with guaranteed deterministic end-to-end delays. Typically, scheduler resource allocation in TSN disregards flexibility of scheduler configurations. Essentially, the notion of flexibility of paths comprising multiple concatenated ports having each a TSN configuration is based on the number of possible embeddings, i.e., resource allocations, for a new flow of a given specification (size and delay deadline) along that path. This demonstration allows the user to define TSN schedules along network paths and, hence, illustrates the behavior and benefit of performing flexibility-aware TSN configuration.



https://doi.org/10.1145/3603269.3610869
Kremmer, Stephan; Manoiu, Roxana; Smok, Claudia; Klee, Sascha; Anastassiou, Gerasimos; Link, Dietmar; Stodtmeister, Richard
Tadalafil to lower retinal venous pressure - a new approach to treatment of primary open-angle glaucoma? :
Tadalafil zur Senkung des retinalen Venendrucks - ein neuer Ansatz in der Therapie des primären Offenwinkelglaukoms?. - In: Die Ophthalmologie, ISSN 2731-7218, Bd. 120 (2023), 10, S. 1045-1048

https://doi.org/10.1007/s00347-023-01813-9
Berkholz, Christoph; Nordström, Jakob
Near-optimal lower bounds on quantifier depth and Weisfeiler-Leman refinement steps. - In: Journal of the ACM, ISSN 1557-735X, Bd. 70 (2023), 5, 32, S. 32:1-32:32

We prove near-optimal tradeoffs for quantifier depth (also called quantifier rank) versus number of variables in first-order logic by exhibiting pairs of n-element structures that can be distinguished by a k-variable first-order sentence but where every such sentence requires quantifier depth at least nΩ (k/log k). Our tradeoffs also apply to first-order counting logic and, by the known connection to the k-dimensional Weisfeiler-Leman algorithm, imply near-optimal lower bounds on the number of refinement iterations. A key component in our proof is the hardness condensation technique introduced by Razborov in the context of proof complexity. We apply this method to reduce the domain size of relational structures while maintaining the minimal quantifier depth needed to distinguish them in finite variable logics.



https://doi.org/10.1145/3195257
Katal, Negin; Rzanny, Michael Carsten; Mäder, Patrick; Römermann, Christine; Wittich, Hans Christian; Boho, David; Musavi, Talie Sadat; Wäldchen, Jana
Bridging the gap: how to adopt opportunistic plant observations for phenology monitoring. - In: Frontiers in plant science, ISSN 1664-462X, Bd. 14 (2023), 1150956, S. 1-13

Plant phenology plays a vital role in assessing climate change. To monitor this, individual plants are traditionally visited and observed by trained volunteers organized in national or international networks - in Germany, for example, by the German Weather Service, DWD. However, their number of observers is continuously decreasing. In this study, we explore the feasibility of using opportunistically captured plant observations, collected via the plant identification app Flora Incognita to determine the onset of flowering and, based on that, create interpolation maps comparable to those of the DWD. Therefore, the opportunistic observations of 17 species collected in 2020 and 2021 were assigned to “Flora Incognita stations” based on location and altitude in order to mimic the network of stations forming the data basis for the interpolation conducted by the DWD. From the distribution of observations, the percentile representing onset of flowering date was calculated using a parametric bootstrapping approach and then interpolated following the same process as applied by the DWD. Our results show that for frequently observed, herbaceous and conspicuous species, the patterns of onset of flowering were similar and comparable between both data sources. We argue that a prominent flowering stage is crucial for accurately determining the onset of flowering from opportunistic plant observations, and we discuss additional factors, such as species distribution, location bias and societal events contributing to the differences among species and phenology data. In conclusion, our study demonstrates that the phenological monitoring of certain species can benefit from incorporating opportunistic plant observations. Furthermore, we highlight the potential to expand the taxonomic range of monitored species for phenological stage assessment through opportunistic plant observation data.



https://doi.org/10.3389/fpls.2023.1150956