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Political discourse on Twitter is a moving target: politicians continuously make statements about their positions. It is therefore crucial to track their discourse on social media to understand their ideological positions and goals. However, Twitter data is also challenging to work with since it is ambiguous and often dependent on social context, and consequently, recent work on political positioning has tended to focus strongly on manifestos (parties' electoral programs) rather than social media. In this paper, we extend recently proposed methods to predict pairwise positional similarities between parties from the manifesto case to the Twitter case, using hashtags as a signal to fine-tune text representations, without the need for manual annotation. We verify the efficacy of fine-tuning and conduct a series of experiments that assess the robustness of our method for low-resource scenarios. We find that our method yields stable positioning reflective of manifesto positioning, both in scenarios with all tweets of candidates across years available and when only smaller subsets from shorter time periods are available. This indicates that it is possible to reliably analyze the relative positioning of actors forgoing manual annotation, even in the noisier context of social media., Comment: 9 pages, accepted at EMNLP (Findings) 2024

While traditional earphones primarily offer private audio spaces, so-called "earables" emerged to offer a variety of sensing capabilities. Pioneering platforms like OpenEarable have introduced novel sensing platforms targeted at the ears, incorporating various sensors. The proximity of the ears to the eyes, brain, and facial muscles has also sparked investigation into sensing biopotentials. However, currently there is no platform available that is targeted at the ears to sense biopotentials. To address this gap, we introduce OpenEarable ExG - an open-source hardware platform designed to measure biopotentials in and around the ears. OpenEarable ExG can be freely configured and has up to 7 sensing channels. We initially validate OpenEarable ExG in a study with a left-right in-ear dual-electrode montage setup with 3 participants. Our results demonstrate the successful detection of smooth pursuit eye movements via Electrooculography (EOG), alpha brain activity via Electroencephalography (EEG), and jaw clenching via Electromyography (EMG). OpenEarable ExG is part of the OpenEarable initiative and is fully open-source under MIT license., Comment: 5 pages, 3 figures

Nonadiabatic coupling between electrons and molecular motion at metal surfaces leads to energy dissipation and dynamical steering effects during chemical surface dynamics. We present a theoretical approach to the scattering of molecules from metal surfaces that incorporates all nonadiabatic and quantum nuclear effects due to the coupling of the molecular degrees of freedom to the electrons in the metal. This is achieved with the hierarchical equations of motion (HEOM) approach combined with a matrix product state representation in twin space. The method is applied to the scattering of nitric oxide from Au(111), for which strongly nonadiabatic energy loss during scattering has been experimentally observed, thus presenting a significant theoretical challenge. Since the HEOM approach treats the molecule-surface coupling exactly, it captures the interplay between nonadiabatic and quantum nuclear effects. Finally, the data obtained by the HEOM approach is used as a rigorous benchmark to assess various mixed quantum-classical methods, from which we derive insights into the mechanisms of energy dissipation and the suitable working regimes of each method.

The reduced sensitivity of mean Southern Ocean zonal transport with respect to surface wind stress magnitude changes, known as eddy saturation, is studied in an idealised analytical model. The model is based on the assumption of a balance between surface wind stress forcing and bottom dissipation in the planetary geostrophic limit, coupled to the GEOMETRIC form of the Gent--McWilliams eddy parameterisation. The assumption of a linear stratification, together with an equation for the parameterised domain integrated total eddy energy, enables the formulation of a two component dynamical system, which reduces to the non-linear oscillator of Ambaum and Novak (Q. J. R. Meteorolog. Soc. 140(685), 2680--2684, 2014) in a Hamiltonian limit. The model suggests an intrinsic oscillatory time scale for the Southern Ocean, associated with a combination of mean shear erosion by eddies and eddy energy generation by the mean shear. For Southern Ocean parameters the model suggests that perturbing the system via stochastic wind forcing may lead to relatively large excursions in eddy energy.

Electrodeposition is a fundamental process in electrochemistry, and has applications in numerous industries, such as corrosion protection, decorative finishing, energy storage, catalysis, and electronics. While there is a long history of using electrodeposition, its application for controlled nanostructure growth is limited. The establishment of an atomic-scale understanding of the electrodeposition process and dynamics is crucial to enable the controlled fabrication of metal nanoparticles and other nanostructures. Significant advancements in molecular simulation capabilities and the electronic structure theory of electrified solid-liquid interfaces bring theory closer to realistic applications, but a gap remains between realistic applications, theoretical understanding of dynamics, and atomistic simulation. In this review we briefly summarize the current state-of-the-art computational techniques available for the simulation of electrodeposition and electrochemical growth on surfaces, and identify the remaining open challenges., Comment: 72 pages, 6 figures

Vehicles in public traffic that are equipped with Automated Driving Systems are subject to a number of expectations: Among other aspects, their behavior should be safe, conforming to the rules of the road and provide mobility to their users. This poses challenges for the developers of such systems: Developers are responsible for specifying this behavior, for example, in terms of requirements at system design time. As we will discuss in the article, this specification always involves the need for assumptions and trade-offs. As a result, insufficiencies in such a behavior specification can occur that can potentially lead to unsafe system behavior. In order to support the identification of specification insufficiencies, requirements and respective assumptions need to be made explicit. In this article, we propose the Semantic Norm Behavior Analysis as an ontology-based approach to specify the behavior for an Automated Driving System equipped vehicle. We use ontologies to formally represent specified behavior for a targeted operational environment, and to establish traceability between specified behavior and the addressed stakeholder needs. Furthermore, we illustrate the application of the Semantic Norm Behavior Analysis in a German legal context with two example scenarios and evaluate our results. Our evaluation shows that the explicit documentation of assumptions in the behavior specification supports both the identification of specification insufficiencies and their treatment. Therefore, this article provides requirements, terminology and an according methodology to facilitate ontology-based behavior specifications in automated driving., Comment: 24 pages, 12 figures, submitted for publication

The Department of Defense (DOD) offers education and training to foreign military personnel--known as international military students--at United States (U.S.) DOD sites. After a student killed 3 people at a base in Florida in 2019, questions were raised about personnel safety at DOD sites hosting students for training. The William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021 includes a provision for the Government Accountability Office (GAO) to review DOD's implementation of security vetting for international military students and accompanying family members. This report assesses the extent to which: (1) DOD has developed vetting procedures for international military students and addressed any implementation challenges; and (2) State and DOD have shared information related to students' training at DOD sites in the U.S. GAO reviewed documentation, analyzed vetting result data for October 1, 2019, through March 31, 2023, and interviewed DOD and State officials and stakeholders from six unified combatant commands and three U.S. embassies. Some stakeholders found opportunities for improvement, and GAO is making four recommendations, including for DOD to assess vetting implementation and take action to mitigate any factors affecting implementation as identified in its assessment, and for DOD and State to clarify roles and responsibilities for sharing information.

The magnetic properties of hole quantum dots in Ge are sensitive to their shape due to the interplay between strong spin-orbit coupling and confinement. We show that the split-off band, surrounding SiGe layers, and hole-hole interactions have a strong influence on calculations of the effective $g$ factor of a lithographic quantum dot in a Ge/SiGe heterostructure. Comparing predictions from a model including these effects to raw magnetospectroscopy data, we apply maximum-likelihood estimation to infer the shape of a quantum dot with up to four holes. We expect that methods like this will be useful in assessing qubit-to-qubit variability critical to further scaling quantum computing technologies based on spins in semiconductors., Comment: 9 pages, 6 figures

In the dynamics of atoms and molecules at metal surfaces, electron-hole pair excitations can play a crucial role. In the case of hyperthermal hydrogen atom scattering, they lead to nonadiabatic energy loss and highly inelastic scattering. Molecular dynamics with electronic friction simulations where friction is computed under an isotropic homogeneous electron gas approximation have previously shown good agreement with measured kinetic energy loss distributions, suggesting that this level of theoretical description is sufficient to describe nonadiabatic effects of atomic scattering. In this work, we show that similar agreement with room temperature experiments can also be achieved with friction derived from density functional theory linear response calculations. The apparent agreement of the homogeneous electron gas approximation with experiment arises from a fortuitous cancellation of errors where friction is overestimated close to the surface and the spin transition is neglected. Only for scattering at low temperatures can both approximations be distinguished and differences rationalised in terms of the number of bounces of the atom on the surface. We identify the signatures of nonadiabatic energy loss of different levels of theory, which future low-temperature scattering experiments will be able to measure., Comment: 30 pages, 8 figures, includes supplemental material

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Dynamics of coarse-grained particle systems derived via the Mori-Zwanzig projection formalism commonly take the form of a (generalized) Langevin equation with configuration-dependent friction and diffusion tensors. In this article, we introduce a class of equivariant representations of tensor-valued functions based on the Atomic Cluster Expansion (ACE) framework that allows for efficient learning of such configuration-dependent friction and diffusion tensors from data. Besides satisfying the correct equivariance properties with respect to the Euclidean group E(3), the resulting heat bath models satisfy a fluctuation-dissipation relation. Moreover, our models can be extended to include additional symmetries, such as momentum conservation, to preserve the hydrodynamic properties of the particle system. We demonstrate the capabilities of the model by constructing a model of configuration-dependent tensorial electronic friction calculated from first principles that arises during reactive molecular dynamics at metal surfaces.

A wide range of geophysical methods is used for the exploration of deep geothermal resources. It aimsat characterizing the deep fractured network and its capacity for fluid/heat extraction. This relieshowever on the capacity of geophysical techniques to 1) image the geometry of the fractured networkbut also 2) characterize the petro-physical properties of the fracture network and matrix. The challengeis however that the geophysical inverse problem is ill-posed and multi-scale.To overcome these challenges, we propose here to take a multi-physics and multi-scale approach of thegeophysical/petro-physical inverse problem. To do so, we are developing iteratively petro-physical andgeophysical models that can explain the observables at the different scales. In this paper, we report outthe results of the first iteration phase of this research project that consists in building an initial petrophysical model from well logs and prior geological knowledge, and single-domain inversion ofgeophysical data. We applied this methodology to the Upper Rhine Graben where a wealth ofknowledge and dataset on deep fractured formations are available.

Centrifugation of erythrocytes (aka Red Blood Cells, RBCs) in a self-forming Percoll gradient is a protocol often used as a way to sort RBCs by age. However, a pattern formation of discrete bands is systematically observed along the continuous density gradient. Although early studies mentioned that aggregation between cells might modify their spatial distribution, it is debated whether a population with continuous density distribution can form discrete bands. Here, we develop a continuity equation, considering the aggregation of cells with a continuous density distribution, which describes the macroscopic evolution of the RBC volume concentration in a density gradient. The numerical solutions demonstrate that the competition between iso-density distribution and aggregation is sufficient to create band patterns. Our model reproduces the temporal evolution observed in the conventional experimental protocol, but also predicts several types of bifurcation-like behaviors for the steady-state patterns in constant gradients, when the volume fraction and aggregation energy of the cells are varied. We threrefore discovered that the competition between RBC aggregation and iso-density distribution is a novel physical mechanism leading to pattern formation.

We propose to build a Flexible Stellarator Physics Facility to explore promising regions of the vast parameter space of disruption-free stellarator solutions for Fusion Pilot Plants (FPPs)., Comment: White paper submitted to FESAC subcommittee on Facilities, 8 pages

Evaporation of blood droplets and diluted blood samples is a topic of intensive research, as it is seen as a possible low-cost tool for diagnosis. So far, samples with volume fraction down to a few percents of Red Blood Cells (RBCs) have been studied, and those were reportedly dominated by a ``coffee-ring'' deposit. In this study, samples with lower volume fractions have been used in order to study the growth of the evaporative deposit from sessile droplets more in details. We observed that blood samples and salt solutions with less than 1\% volume fraction of RBCs are dominated by a central deposit. We characterized the growth process of this central deposit by evaporating elongated drops, and determined that it is consistent with the Kardar-Parisi-Zhang process in the presence of quenched disorder. Our results showed a sensitivity of this deposit size to the fibrinogen concentration and shape of the RBCs, meaning that this parameter could be used to develop a new and cost-effective clinical marker for inflammation and RBC deformation., Comment: 27 pages, 11 Figures

Acute Leukemia is the most common hematologic malignancy in children and adolescents. A key methodology in the diagnostic evaluation of this malignancy is immunophenotyping based on Multiparameter Flow Cytometry (FCM). However, this approach is manual, and thus time-consuming and subjective. To alleviate this situation, we propose in this paper the FCM-Former, a machine learning, self-attention based FCM-diagnostic tool, automating the immunophenotyping assessment in Childhood Acute Leukemia. The FCM-Former is trained in a supervised manner, by directly using flow cytometric data. Our FCM-Former achieves an accuracy of 96.5% assigning lineage to each sample among 960 cases of either acute B-cell, T-cell lymphoblastic, and acute myeloid leukemia (B-ALL, T-ALL, AML). To the best of our knowledge, the FCM-Former is the first work that automates the immunophenotyping assessment with FCM data in diagnosing pediatric Acute Leukemia., Comment: The paper has been accepted at IEEE EMBS 2024 (46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society)

The paper proves generalization results for a class of stochastic learning algorithms. The method applies whenever the algorithm generates an absolutely continuous distribution relative to some a-priori measure and the Radon Nikodym derivative has subgaussian concentration. Applications are bounds for the Gibbs algorithm and randomizations of stable deterministic algorithms as well as PAC-Bayesian bounds with data-dependent priors.

The foundational role of datasets in defining the capabilities of deep learning models has led to their rapid proliferation. At the same time, published research focusing on the process of dataset development for environment perception in automated driving has been scarce, thereby reducing the applicability of openly available datasets and impeding the development of effective environment perception systems. Sensor-based, mapless automated driving is one of the contexts where this limitation is evident. While leveraging real-time sensor data, instead of pre-defined HD maps promises enhanced adaptability and safety by effectively navigating unexpected environmental changes, it also increases the demands on the scope and complexity of the information provided by the perception system. To address these challenges, we propose a scenario- and capability-based approach to dataset development. Grounded in the principles of ISO 21448 (safety of the intended functionality, SOTIF), extended by ISO/TR 4804, our approach facilitates the structured derivation of dataset requirements. This not only aids in the development of meaningful new datasets but also enables the effective comparison of existing ones. Applying this methodology to a broad range of existing lane detection datasets, we identify significant limitations in current datasets, particularly in terms of real-world applicability, a lack of labeling of critical features, and an absence of comprehensive information for complex driving maneuvers., Comment: Accepted to be published at the 2024 35th IEEE Intelligent Vehicles Symposium (IV), Jeju Island, Korea, June 2 - 5, 2024

As simulation is increasingly used in scenario-based approaches to test Automated Driving Systems, the credibility of simulation results is a major concern. Arguably, credibility depends on the validity of the simulation setup and simulation models. When selecting appropriate simulation models, a trade-off must be made between validity, often connected to the model's fidelity, and cost of computation. However, due to the large number of test cases, expert-based methods to create sufficiently valid simulation setups seem infeasible. We propose using design contracts in order to semi-automatically compose simulation setups for given test cases from simulation models and to derive requirements for the simulation models, supporting separation of concerns between simulation model developers and users. Simulation model contracts represent their validity domains by capturing a validity guarantee and the associated operating conditions in an assumption. We then require the composition of the simulation model contracts to refine a test case contract. The latter contract captures the operating conditions of the test case in its assumption and validity requirements in its guarantee. Based on this idea, we present a framework that supports the compositional configuration of simulation setups based on the contracts and a method to derive runtime monitors for these simulation setups., Comment: 8 pages, 3 figures. Accepted to be published in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, Canada, September 24-27, 2024

Supervising the safe operation of automated vehicles is a key requirement in order to unleash their full potential in future transportation systems. In particular, previous publications have argued that SAE Level 4 vehicles should be aware of their capabilities at runtime to make appropriate behavioral decisions. In this paper, we present a framework that enables the implementation of an online capability monitor. We derive a graphical system model that captures the relationships between the quality of system elements across different architectural views. In an expert-driven approach, we parameterize Bayesian Networks based on this structure using Fuzzy Logic. Using the online monitor, we infer the quality of the system's capabilities based on technical measurements acquired at runtime., Comment: Accepted for 27th IEEE International Conference on Intelligent Transportation Systems

Automated driving systems require monitoring mechanisms to ensure safe operation, especially if system components degrade or fail. Their runtime self-representation plays a key role as it provides a-priori knowledge about the system's capabilities and limitations. In this paper, we propose a data-driven approach for deriving such a self-representation model for the motion controller of an automated vehicle. A conformalized prediction model is learned and allows estimating how operational conditions as well as potential degradations and failures of the vehicle's actuators impact motion control performance. During runtime behavior generation, our predictor can provide a heuristic for determining the admissible action space., Comment: Accepted for 27th IEEE International Conference on Intelligent Transportation Systems

The development and deployment of automated vehicles pose major challenges for manufacturers to this day. Whilst central questions, like the issue of ensuring a sufficient level of safety, remain unanswered, prototypes are increasingly finding their way into public traffic in urban areas. Although safety concepts for prototypes are addressed in literature, published work hardly contains any dedicated considerations on a systematic release for their operation. In this paper, we propose an incremental release process for public demonstrations of prototypes' automated driving functionality. We explicate release process requirements, derive process design decisions, and define stakeholder tasks. Furthermore, we reflect on practical insights gained through implementing the release process as part of the UNICAR$agil$ research project, in which four prototypes based on novel vehicle concepts were built and demonstrated to the public. One observation is the improved quality of internal risk communication, achieved by dismantling information asymmetries between stakeholders. Design conflicts are disclosed - providing a contribution to nurture transparency and, thereby, supporting a valid basis for release decisions. We argue that our release process meets two important requirements, as the results suggest its applicability to the domain of automated driving and its scalability to different vehicle concepts and organizational structures., Comment: Accepted for publication

Recommendation algorithms (RS) used by social media, like YouTube, significantly shape our information consumption across various domains, especially in healthcare. Hence, algorithmic auditing becomes crucial to uncover their potential bias and misinformation, particularly in the context of controversial topics like abortion. We introduce a simple yet effective sock puppet auditing approach to investigate how YouTube recommends abortion-related videos to individuals with different backgrounds. Our framework allows for efficient auditing of RS, regardless of the complexity of the underlying algorithms

DNA regulation and repair processes require direct interactions between proteins and DNA at specific sites. Local fluctuations of the sugar-phosphate backbones and bases of DNA (a form of DNA "breathing") play a central role in such processes. Here we review the development and application of novel spectroscopic methods and analyses - both at the ensemble and single-molecule levels - to study structural and dynamic properties of exciton-coupled cyanine and fluorescent nucleobase analogue dimer-labeled DNA constructs at key positions involved in protein-DNA complex assembly and function. The exciton-coupled dimer probes act as "sensors" of the local conformations adopted by the sugar-phosphate backbones and bases immediately surrounding the dimer probes. These methods can be used to study the mechanisms of protein binding and function at these sites., Comment: Preprint of invited book chapter

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows towards object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create "deep" modular interfaces that connect big-data workflows and electronic structure codes, and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; and in another, DFTB+ receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+, or to enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities., Comment: 12 pages, 5 figures, Journal of Chemical Physics special issue "modular and interoperable software for chemical physics"

Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations in reaction probabilities. This brings a particularly interesting challenge for machine learning interatomic potentials, which are becoming well-established tools to accelerate molecular dynamics simulations. We compare state-of-the-art machine learning interatomic potentials with a particular focus on their inference performance on CPUs and suitability for high throughput simulation of reactive chemistry at surfaces. The considered models include polarizable atom interaction neural networks (PaiNN), recursively embedded atom neural networks (REANN), the MACE equivariant graph neural network, and atomic cluster expansion potentials (ACE). The models are applied to a dataset on reactive molecular hydrogen scattering on low-index surface facets of copper. All models are assessed for their accuracy, time-to-solution, and ability to simulate reactive sticking probabilities as a function of the rovibrational initial state and kinetic incidence energy of the molecule. REANN and MACE models provide the best balance between accuracy and time-to-solution and can be considered the current state-of-the-art in gas-surface dynamics. PaiNN models require many features for the best accuracy, which causes significant losses in computational efficiency. ACE models provide the fastest time-to-solution, however, models trained on the existing dataset were not able to achieve sufficiently accurate predictions in all cases.

Robot-assisted surgery has advantages compared to conventional laparoscopic procedures, e.g., precise movement of the surgical instruments, improved dexterity, and high-resolution visualization of the surgical field. However, mechanical tissue properties may provide additional information, e.g., on the location of lesions or vessels. While elastographic imaging has been proposed, it is not readily available as an online modality during robot-assisted surgery. We propose modifying a da~Vinci surgical instrument to realize optical coherence elastography (OCE) for quantitative elasticity estimation. The modified da~Vinci instrument is equipped with piezoelectric elements for shear wave excitation and we employ fast optical coherence tomography (OCT) imaging to track propagating wave fields, which are directly related to biomechanical tissue properties. All high-voltage components are mounted at the proximal end outside the patient. We demonstrate that external excitation at the instrument shaft can effectively stimulate shear waves, even when considering damping. Comparing conventional and deep learning-based signal processing, resulting in mean absolute errors of 19.27 kPa and 6.29 kPa, respectively. These results illustrate that precise quantitative elasticity estimates can be obtained. We also demonstrate quantitative elasticity estimation on ex-vivo tissue samples of heart, liver and stomach, and show that the measurements can be used to distinguish soft and stiff tissue types.

Predicting the degradation processes of molecules over long timescales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic data. Here we showcase Kinetica.jl, a new software package for constructing large-scale chemical reaction networks in a fully-automated fashion by exploring chemical reaction space with a kinetics-driven algorithm; coupled to efficient machine-learning models of activation energies for sampled elementary reactions, we show how this approach readily enables generation and kinetic characterization of networks containing $\sim10^{3}$ chemical species and $10^{4}$ - $10^{5}$ reactions. Symbolic-numeric modelling of the generated reaction networks is used to allow for flexible, efficient computation of kinetic profiles under experimentally-realizable conditions such as continuously-variable temperature regimes, enabling direct connection between bottom-up reaction networks and experimental observations. Highly efficient propagation of long-timescale kinetic profiles is required for automated reaction network refinement and is enabled here by a new discrete kinetic approximation. The resulting Kinetica.jl simulation package therefore enables automated generation, characterization, and long-timescale modelling of complex chemical reaction systems. We demonstrate this for hydrocarbon pyrolysis simulated over timescales of seconds, using transient temperature profiles representing those of tubular flow reactor experiments., Comment: 56 pages, 13 figures

Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and efficient decision-making, which is often achieved using heuristics-aided methods such as genetic algorithms, auction-based methods, and bipartite graph matching methods. These methods often assume a form that lends better explainability compared to an end-to-end (learnt) neural network based policy for MRTA. However, deriving suitable heuristics can be tedious, risky and in some cases impractical if problems are too complex. This raises the question: can these heuristics be learned? To this end, this paper particularly develops a Graph Reinforcement Learning (GRL) framework to learn the heuristics or incentives for a bipartite graph matching approach to MRTA. Specifically a Capsule Attention policy model is used to learn how to weight task/robot pairings (edges) in the bipartite graph that connects the set of tasks to the set of robots. The original capsule attention network architecture is fundamentally modified by adding encoding of robots' state graph, and two Multihead Attention based decoders whose output are used to construct a LogNormal distribution matrix from which positive bigraph weights can be drawn. The performance of this new bigraph matching approach augmented with a GRL-derived incentive is found to be at par with the original bigraph matching approach that used expert-specified heuristics, with the former offering notable robustness benefits. During training, the learned incentive policy is found to get initially closer to the expert-specified incentive and then slightly deviate from its trend., Comment: This paper was accepted for presentation in proceedings of IEEE International Conference on Robotics and Automation 2024

Developers have to obtain a sound understanding of existing risk potentials already in the concept phase of driverless vehicles. Deductive as well as inductive SOTIF analyses of potential triggering conditions for hazardous behavior help to achieve this goal. In this regard, ISO 21448 suggests conducting a System-Theoretic Process Analysis (STPA). In this article, we introduce German terminology for SOTIF considerations and critically discuss STPA theory in the course of an example application, while also proposing methodological additions. -- -- Um bereits in der Konzeptphase autonomer Fahrzeuge einen fundierten Eindruck bestehender Risikopotenziale zu erhalten, werden im Zuge von deduktiven und induktiven SOTIF-Analysen m\"ogliche ausl\"osende Umst\"ande f\"ur gef\"ahrliches Verhalten untersucht. In diesem Zusammenhang wird in der ISO 21448 die Durchf\"uhrung einer systemtheoretischen Prozessanalyse (STPA) vorgeschlagen. In diesem Beitrag f\"uhren wir deutsche Terminologie f\"ur SOTIF-Betrachtungen ein und setzen uns im Zuge einer Anwendung kritisch mit der STPA-Theorie auseinander, wobei wir begleitend methodische Erg\"anzungen anregen., Comment: The final publication is available at www.degruyter.com, published in at - Automatisierungstechnik, in German