Technische Universität Darmstadt

About: Technische Universität Darmstadt is a education organization based out in Darmstadt, Germany. It is known for research contribution in the topics: Neutron & Finite element method. The organization has 17316 authors who have published 40619 publications receiving 937916 citations. The organization is also known as: Darmstadt University of Technology & University of Darmstadt.

Dislocation detection algorithm for atomistic simulations

Abstract: We present a novel computational method that makes it possible to directly extract dislocation lines and their associated Burgers vectors from three-dimensional atomistic simulations. The on-the-fly dislocation detection algorithm is based on a fully automated Burgers circuit analysis, which locates dislocation cores and determines their Burgers vector. Through a subsequent vectorization step, the transition from the atomistic system to a discrete dislocation representation is achieved. Using a parallelized implementation of the algorithm, the dislocation analysis can be efficiently performed on the fly within a molecular dynamics simulation. This enables the visualization and investigation of dislocation processes occurring on sub-picosecond time scales, whose observation is otherwise impeded by the presence of other crystal defects or simply by the huge amount of data produced by large-scale atomistic simulations. The presented method is able to identify individual segments as well as networks of perfect, partial and twinning dislocations. The dislocation density can be directly determined and even more sophisticated information is made accessible by our dislocation analysis, including dislocation reactions and junctions, as well as stacking fault and twin boundary densities.

Epigenetic upregulation of FKBP5 by aging and stress contributes to NF-κB-driven inflammation and cardiovascular risk.

Abstract: Aging and psychosocial stress are associated with increased inflammation and disease risk, but the underlying molecular mechanisms are unclear. Because both aging and stress are also associated with lasting epigenetic changes, a plausible hypothesis is that stress along the lifespan could confer disease risk through epigenetic effects on molecules involved in inflammatory processes. Here, by combining large-scale analyses in human cohorts with experiments in cells, we report that FKBP5, a protein implicated in stress physiology, contributes to these relations. Across independent human cohorts (total n > 3,000), aging synergized with stress-related phenotypes, measured with childhood trauma and major depression questionnaires, to epigenetically up-regulate FKBP5 expression. These age/stress-related epigenetic effects were recapitulated in a cellular model of replicative senescence, whereby we exposed replicating human fibroblasts to stress (glucocorticoid) hormones. Unbiased genome-wide analyses in human blood linked higher FKBP5 mRNA with a proinflammatory profile and altered NF-κB–related gene networks. Accordingly, experiments in immune cells showed that higher FKBP5 promotes inflammation by strengthening the interactions of NF-κB regulatory kinases, whereas opposing FKBP5 either by genetic deletion (CRISPR/Cas9-mediated) or selective pharmacological inhibition prevented the effects on NF-κB. Further, the age/stress-related epigenetic signature enhanced FKBP5 response to NF-κB through a positive feedback loop and was present in individuals with a history of acute myocardial infarction, a disease state linked to peripheral inflammation. These findings suggest that aging/stress-driven FKBP5–NF-κB signaling mediates inflammation, potentially contributing to cardiovascular risk, and may thus point to novel biomarker and treatment possibilities.

MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

Abstract: The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pre-training. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pre-trained multilingual model to a new language. MAD-X outperforms the state of the art in cross lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering. Our code and adapters are available at AdapterHub.ml.

Determination of O[H] and CO Coverage and Adsorption Sites on PtRu Electrodes in an Operating PEM Fuel Cell

Abstract: A special in situ PEM fuel cell has been developed to allow X-ray absorption measurements during real fuel cell operation. Variations in both the coverage of O[H] (O[H] indicates O and/or OH) and CO (applying a novel ΔμL3 = μL3(V) − μL3(ref) difference technique), as well as in the geometric (EXAFS) and electronic (atomic XAFS) structure of the anode catalyst, are monitored as a function of the current. In hydrogen, the NPt-Ru coordination number increases much slower than the NPt-Pt with increasing current, indicating a more reluctant reduction of the surface Pt atoms near the hydrous Ru oxide islands. In methanol, both O[H] and CO adsorption are separately visible with the Δμ technique and reveal a drop in CO and an increase in OH coverage in the range of 65−90 mA/cm2. With increasing OH coverage, the Pt−O coordination number and the AXAFS intensity increase. The data allow the direct observation of the preignition and ignition regions for OH formation and CO oxidation, during the methanol fuel cell ope...

D\"IoT: A Federated Self-learning Anomaly Detection System for IoT

Abstract: IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DIoT, an autonomous self-learning distributed system for detecting compromised IoT devices effectively. In contrast to prior work, DIoT uses a novel self-learning approach to classify devices into device types and build normal communication profiles for each of these that can subsequently be used to detect anomalous deviations in communication patterns. DIoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, DIoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that DIoT is highly effective (95.6% detection rate) and fast (~257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. DIoT reported no false alarms when evaluated in a real-world smart home deployment setting.