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Publication500 GHz plasmonic Mach-Zehnder modulator enabling sub-THz microwave photonics( 2018)
;Burla, Maurizio ;Hoessbacher, Claudia ;Heni, Wolfgang ;Haffner, Christian ;Fedoryshyn, Yuriy ;Werner, Dominik ;Watanabe, Tatsuhiko ;Massler, Hermann ;Elder, Delwin ;Dalton, LarryLeuthold, JuergBroadband electro-optic intensity modulators are essential to convert electrical signals to the optical domain. The growing interest in THz wireless applications demands modulators with frequency responses to the sub-THz range, high power handling and very low nonlinear distortions, simultaneously. However, a modulator with all those characteristics has not been demonstrated to date. Here we experimentally demonstrate that plasmonic modulators do not trade off any performance parameter, featuring - at the same time - a short length of 10s of micrometers, record-high flatfrequency response beyond 500 GHz, high power handling and high linearity, and we use them to create a sub-THz radio-over-fiber analog optical link. These devices have the potential to become a new tool in the general field of microwave photonics, making the sub-THz range accessible to e.g. 5G wireless communications, antenna remoting, IoT, sensing, and more. -
PublicationA causal model of safety assurance for machine learning( 2022)This paper proposes a framework based on a causal model of safety upon which e ective safety assurance cases for ML-based applications can be developed. In doing so, we build upon established principles of safety engineering as well as previous work on structuring assurance arguments for ML. The paper de nes four categories of safety case evidence and a structured analysis approach within which these evidences can be e ectively combined. Where appropriate, abstract formalisations of these contributions are used to illustrate the causalities they evaluate, their contributions to the safety argument and desirable properties of the evidences. Based on the proposed framework, progress in this area is re-evaluated and a set of future research directions proposed in order for tangible progress in this eld to be made.
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PublicationA Comparison of Methods for Player Clustering via Behavioral Telemetry( 2014)
;Drachen, AndersThe analysis of user behavior in digital games has been aided by the introduction of user telemetry in game development, which provides unprecedented access to quantitative data on user behavior from the installed game clients of the entire population of players. Player behavior telemetry datasets can be exceptionally complex, with features recorded for a varying population of users over a temporal segment that can reach years in duration. Categorization of behaviors, whether through descriptive methods (e.g. segmention) or unsupervised/supervised learning techniques, is valuable for finding patterns in the behavioral data, and developing profiles that are actionable to game developers. There are numerous methods for unsupervised clustering of user behavior, e.g. k-means/c-means, Non-negative Matrix Factorization, or Principal Component Analysis. Although all yield behavior categorizations, interpretation of the resulting categories in terms of actual play behavior can be difficult if not impossible. In this paper, a range of unsupervised techniques are applied together with Archetypal Analysis to develop behavioral clusters from playtime data of 70,014 World of Warcraft players, covering a five year interval. The techniques are evaluated with respect to their ability to develop actionable behavioral profiles from the dataset. -
PublicationA Dimension-adaptive Combination Technique for Uncertainty Quantification( 2022-04-12)
;Seidler, UtaWe present an adaptive algorithm for the computation of quantities of interest involving the solution of a stochastic elliptic PDE where the diffusion coefficient is parametrized by means of a Karhunen-Loève expansion. The approximation of the equivalent parametric problem requires a restriction of the countably infinite-dimensional parameter space to a finite-dimensional parameter set, a spatial discretization and an approximation in the parametric variables. We consider a sparse grid approach between these approximation directions in order to reduce the computational effort and propose a dimension-adaptive combination technique. In addition, a sparse grid quadrature for the high-dimensional parametric approximation is employed and simultaneously balanced with the spatial and stochastic approximation. Our adaptive algorithm constructs a sparse grid approximation based on the benefit-cost ratio such that the regularity and thus the decay of the Karhunen-Loève coefficients is not required beforehand. The decay is detected and exploited as the algorithm adjusts to the anisotropy in the parametric variables. We include numerical examples for the Darcy problem with a lognormal permeability field, which illustrate a good performance of the algorithm: For sufficiently smooth random fields, we essentially recover the rate of the spatial discretization as asymptotic convergence rate with respect to the computational cost. -
PublicationA fluorescent nanosensor paint reveals the heterogeneity of dopamine release from neurons at individual release sites( 2021)
;Elizarova, Sofia ;Chouaib, Abed ;Shaib, Ali ;Mann, Florian ;Brose, NilsDaniel, James A.The neurotransmitter dopamine is released from discrete axonal structures called varicosities. Its release is essential in behaviour and is critically implicated in prevalent neuropsychiatric diseases. Existing dopamine detection methods are not able to detect and distinguish discrete dopamine release events from multiple varicosities. This prevents an understanding of how dopamine release is regulated across populations of discrete varicosities. Using a near infrared fluorescent (980 nm) dopamine nanosensor 'paint' (AndromeDA), we show that action potential-evoked dopamine release is highly heterogeneous across release sites and also requires molecular priming. Using AndromeDA, we visualize dopamine release at up to 100 dopaminergic varicosities simultaneously within a single imaging field with high temporal resolution (15 images/s). We find that 'hotspots' of dopamine release are highly heterogeneous and are detected at only ~17% of all varicosities. In neurons lacking Munc13 proteins, which prime synaptic vesicles, dopamine release is abolished during electrical stimulation, demonstrating that dopamine release requires vesicle priming. In summary, AndromeDA reveals the spatiotemporal organization of dopamine release. -
PublicationA local input-to-state stability result w.r.t. attractors of nonlinear reaction-diffusion equations( 2019)
;Dashkovskiy, Sergey ;Kapustyan, Oleksiy V.We establish the local input-to-state stability of a large class of disturbed nonlinear reaction-diffusion equations w.r.t. the global attractor of the respective undisturbed system. -
PublicationA neural code for egocentric spatial maps in the human medial temporal lobe( 2020)
;Kunz, Lukas ;Brandt, Armin ;Staresina, Bernhard P. ;Reifenstein, Eric T. ;Weidemann, Christoph T. ;Herweg, Nora A. ;Tsitsiklis, Melina ;Kempter, Richard ;Kahana, Michael J. ;Schulze-Bonhage, AndreasJacobs, JoshuaSpatial navigation relies on neural systems that encode spatial information relative to the external world or relative to the navigating organism. Ever since the proposal of cognitive maps, the neuroscience of spatial navigation has focused on allocentric (world-referenced) representations such as place cells. Here, using single-neuron recordings during virtual navigation, we reveal a neural code for egocentric (self-centered) spatial information in humans: ""anchor cells"" represent egocentric directions towards proximal ""anchor points"" located in the environmental center or periphery. Anchor cells were abundant in parahippocampal cortex, supported full vectorial representations of egocentric space, and were integrated into a neural memory network. Anchor cells may thus facilitate egocentric navigation strategies, assist in transforming percepts into allocentric spatial representations, and may underlie the first-person perspective in episodic memories. -
PublicationA novel method for functional testing of ankle braces based on a modified prosthetic foot testing machine( 2022)
;Nguyen, Thanh-Duc ;Czapka, PhilipThe aim of this study is to investigate the feasibility of a new dynamic testing method for ankle braces, to quantify the mechanical stability of different orthotic designs. A mechanical, artificial foot model has been designed, considering the anatomy and biomechanics of the human foot. Sensors were built into the ankle joint, based on medical and technical principles. Together with an actuator, they were used as a test bench for validating ankle braces. For a first feasibility study, the influence of five different types of ankle orthoses for selected movement sequences was investigated. -
PublicationA Quantum Optimization Case Study for a Transport Robot Scheduling Problem( 2023)
;Leib, Dominik ;Jäger, Sven ;Jones, Caitlin Isobel ;Awasthi, Abhishek ;Niederle, Astrid ElisaWe present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-the-art classical solver in solving a transport robot scheduling problem. This problem originates from an industrially relevant real-world scenario. We provide three different models for our problem following different design philosophies. In our benchmark, we focus on the solution quality and end-to-end runtime of different model and solver combinations. We find promising results for the digital annealer and some opportunities for the hybrid quantum annealer in direct comparison with Gurobi. Our study provides insights into the workflow for solving an application-oriented optimization problem with different strategies, and can be useful for evaluating the strengths and weaknesses of different approaches. -
PublicationA Ray Tracing Technique for the Navigation on a Non-convex Pareto Front( 2020)A new interactive approach to navigate on approximations of in general non-convex but connected Pareto fronts is introduced. Given a finite number of precalculated representative Pareto-efficient solutions, an adapted Delaunay triangulation is generated. Based on interpolation and ray tracing techniques, real time navigation in the vicinity of Pareto-optimal solutions is made possible.
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PublicationA Survey on Deep Learning Techniques for Action Anticipation( 2023-09-29T14:07:56Z)
;Zhong, Zeyun ;Gall, JürgenThe ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numerous methods have been introduced for action anticipation in recent years, with deep learning-based approaches being particularly popular. In this work, we review the recent advances of action anticipation algorithms with a particular focus on daily-living scenarios. Additionally, we classify these methods according to their primary contributions and summarize them in tabular form, allowing readers to grasp the details at a glance. Furthermore, we delve into the common evaluation metrics and datasets used for action anticipation and provide future directions with systematical discussions. -
PublicationA time-resolved meta-analysis of consensus gene expression profiles during human T-cell activationBackground: The coordinated transcriptional regulation of activated T-cells is based on the complex dynamic behavior of signaling networks. Given an external stimulus, T-cell gene expression is characterized by impulse and sustained patterns over the course. Here, we analyzed the temporal pattern of activation across different T-cell populations to develop consensus gene signatures for T-cell activation. Methods: We applied a meta-analysis of anti-CD3/CD28 induced CD4+ T-cell activation kinetics of publicly available transcriptomewide time series using a random effects model. We used non-negative matrix factorization, an unsupervised deconvolution method, to infer changes in biological patterns over time. For verification and to further map a wider variety of the T-cell landscape, we performed a time series of transcriptome-wide RNA sequencing on activated blood T-cells. Lastly, we matched the identified consensus biomarker signatures to single-cell RNA sequencing (scRNA-Seq) data of autologous anti-CD19 chimeric antigen receptor (CAR) T-cells from 24 patients with large B cell lymphoma (LBCL) to characterize activation status of the cell product before infusion. Results: We identified time-resolved gene expression profiles comprising 521 genes of up to 10 disjunct time points during activation and different polarization conditions. The gene signatures include central transcriptional regulators of T-cell activation, representing successive waves as well as sustained patterns of induction. They cover early, intermediate, and late response expression rates across multiple T-cell populations, thus defining consensus biomarker signatures for T-cell activation. Intermediate and late response activation signatures in CAR T-cell infusion products were correlated to immune effector cell-associated neurotoxicity syndrome. Conclusion: In conclusion, we describe temporally resolved gene expression patterns across T-cell populations. These biomarker signatures are a valuable source for e.g., monitoring transcriptional changes during T-cell activation with a reasonable number of genes, annotating T-cell states in single-cell transcriptome studies or assessing dysregulated functions of human T-cell immunity.
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PublicationAccelerating Deep Tech Innovations( 2023-01-09)
;Latz, Tim ;Hedemann, MoritzReufsteck, Till FelixSystemic innovation success in Europe lags behind its expectations as Europe spends most funding in basic research and development (R&D) while commercialization takes place in other regions such as the US or China. Therefore, this whitepaper aims to enhance the technology transfer for Deep Tech innovations in Europe through developing a framework for actors in Deep Tech ecosystems based on a case study analysis. For this, 25 actors from science and industry in three Deep Tech areas are analyzed – lithium-ion battery cells, semiconductors and electrolyzers. Based on a cross-industrial synthesis, recommendations for actions are derived building on the individual actors’ strengths and weaknesses. As Deep Tech is considered a disruptive technology with a high degree of uncertainty regarding its actual feasibility, investments in such technologies are associated with high risks but great economic and societal opportunities. To benefit from such high-risk investments, it is mandatory that sufficient support is provided from research all the way to commercialization. This technology transfer includes both public funding of fundamental research and private funding for scaling and commercializing innovations. In addition to insufficient risk capital in basic research, however, a lack of consistent funding on the path from research to commercialization can be observed in Europe. This gap phenomenon between public and private funding is often referred to as the Valley of Death. To foster the technology transfer for Deep Tech innovations, three needs for action are derived from the analyzed case studies: First, collaboration between actors should be strengthened to deliver Deep Tech innovations more effectively. Furthermore, a sufficient environment needs to be created in which the individual actors’ strengths complement each other optimally. Finally, protection spaces for intellectual property must be created to avoid legal disputes from the outset. By following these recommendations and taking the individual profiles of actors into account, the Valley of Death can be minimized. -
PublicationACES - A revolution for risk management in the automotive industry?(Fraunhofer IML, 2021)
;Sardesai, SaskiaKlink, PhilippAutomotive industry products are undergoing a dramatic transformation into self-driving, connected and digitalized vehicles powered by alternative drive technologies, particularly electric ones. In addition, shared mobility has given rise to new business models in which products are not used solely by the buyer's friends and family. All these changes are captured by the acronym "ACES", which stands for autonomous driving, connectivity, electrification, and shared mobility. The developments induced by ACES will change not only the nature and appearance of road traffic but also the premises on which the automotive industry operates, particularly for OEMs and their suppliers. Tomorrow's mobility solutions will still have to be manufactured; however, the production supply chain will change. The growing digitalization and electrification of motor vehicles will require new and different suppliers who will occupy an entirely different position within the supplier market than established automotive suppliers. The changes will affect not just OEMs or their first- and second-tier suppliers. The industry's transformation will trickle all the way down to the raw materials markets. For example, competition for rare earths has intensified since these materials are needed not only for many ACES innovations but also by manufacturers in industries such as consumer electronics. In this environment, supply chain risk management is particularly important. Strategy risks and issues have to be addressed before awarding contracts to existing suppliers or establishing new, potentially unknown suppliers for ACES-related components and technologies. This demands new approaches to qualitative and quantitative risk identification, analysis, and evaluation. Operating risks must be managed better, too. For example, OEMs have to compete with other industries for components, particularly for those needed to digitalize and electrify the products. It should be noted that competing sectors such as consumer electronics sometimes buy much larger volumes of components from suppliers than the automotive industry does. As a result, responses to risks that affect multiple sectors may not have the same effect as they would in established supplier networks that only serve the automotive industry. This whitepaper aims to take an initial look at these aspects of risk management as ACES trends continue to unfold. It draws on interviews with automotive industry experts and scholars as well as an initial industry analysis of the supplier market conducted by Dun & Bradstreet. In addition to touching on theoretical risk management approaches, the whitepaper highlights the implications of ACES trends for the automotive industry and particularly for OEMs and identifies new supplier risks emanating from these trends. It also presents initial data-supported information from the industry analysis. The industry analysis provides an initial glimpse into the changing supplier industry as well as the changes that took place between 2010 and 2020. In addition, it shows that the supplier market began differentiating during this period, with some suppliers specializing in technologies relevant to ACES trends. -
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PublicationAirway Basal Cells show a dedifferentiated KRT17highPhenotype and promote Fibrosis in Idiopathic Pulmonary Fibrosis( 2020)
;Schupp, Jonas Christian ;Kayser, Gian ;Terwolbeck, Oliver ;Engelhard, Peggy ;Adams, Taylor Sterling ;Zweigerdt, Robert ;Kempf, Henning ;Lienenklaus, Stefan ;Garrels, Wiebke ;Nazarenko, Irina ;Jonigk, Danny ;Wygrecka, Malgorzata ;Klatt, Denise ;Schambach, Axel ;Kaminski, NaftaliIdiopathic pulmonary fibrosis (IPF) is a fatal disease with limited treatment options. In this study we focus on the profibrotic properties of airway basal cells (ABC) obtained from patients with IPF (IPF-ABC). Single cell RNA sequencing of bronchial brushes revealed extensive reprogramming of IPF-ABC towards a KRT17high PTENlow dedifferentiated cell type. In the 3D organoid model, compared to ABC obtained from healthy volunteers, IPF-ABC give rise to more bronchospheres, de novo bronchial structures resembling lung developmental processes, induce fibroblast proliferation and extracellular matrix deposition in co-culture. Intratracheal application of IPF-ABC into minimally injured lungs of Rag2-/- or NRG mice causes severe fibrosis, remodeling of the alveolar compartment, and formation of honeycomb cyst-like structures. Connectivity MAP analysis of scRNA seq of bronchial brushings suggested that gene expression changes in IPF-ABC can be reversed by SRC inhibition. After demonstrating enhanced SRC expression and activity in these cells, and in IPF lungs, we tested the effects of saracatinib, a potent SRC inhibitor previously studied in humans. We demonstrated that saracatinib modified in-vitro and in-vivo the profibrotic changes observed in our 3D culture system and novel mouse xenograft model. -
PublicationAmino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability( 2023)
;Busche, Svenja ;Harris, Danielle MM ;Zimmermann, Johannes ;Oumari, Mhmd ;Frank, Derk ;Bang, Corinna ;Rosenstiel, Philip ;Schreiber, Stefan ;Frey, Norbert ;Franke, Andre ;Aden, KonradWaschina, SilvioAmino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and function of the human gut microbiome remain poorly understood. This study performed the first systematic analysis of the distribution of amino acid auxotrophies in the human gut microbiome using a combined metabolomic, metagenomic, and metabolic modeling approach. Results showed that amino acid auxotrophies are ubiquitous in the colon microbiome, with tryptophan auxotrophy being the most common. Auxotrophy frequencies were found to be higher for those amino acids, that are also essential to the human host. Moreover, a higher overall abundance of auxotrophies was associated with greater microbiome diversity and stability, and the distribution of auxotrophs was found to be related to the human host’s metabolome, including trimethylamine oxide, small aromatic acids, and secondary bile acids. Thus, our results suggest that amino acid auxotrophies are important factors that contribute to microbiome ecology and host-microbiome metabolic interactions. -
PublicationAmplification by stimulated emission of nitrogen vacancy centres in a diamond-loaded fibre cavity( 2020)
;Nair, Sarath Raman ;Rogers, Lachlan J. ;Roberts, Reece P. ;Abe, Hiroshi ;Ohshima, Takeshi ;Yatsui, Takashi ;Greentree, Andrew D.Volz, ThomasLaser-threshold magetometry using the negatively charged nitrogen-vacancy (NV−) centre in diamond as a gain medium has been proposed as a technique to dramatically enhance the sensitivity of room-temperature magnetometry. We experimentally explore a diamond-loaded open tunable fibre-cavity system as a potential contender for the realization of lasing with NV− centres. We observe amplification of the transmission of a cavity-resonant seed laser at 721 nm when the cavity is pumped at 532 nm, and attribute this to stimulated emission. Changes in the intensity of spontaneously emitted photons accompany the amplification, and a qualitative model including stimulated emission and ionisation dynamics of the NV− centre captures the dynamics in the experiment very well. These results highlight important considerations in the realization of an NV− laser in diamond. -
PublicationAn adaptive discretization method solving semi-infinite optimization problems with quadratic rate of convergence( 2019)Semi-infinite programming can be used to model a large variety of complex optimization problems. The simple description of such problems comes at a price: semi-infinite problems are often harder to solve than finite nonlinear problems. In this paper we combine a classical adaptive discretization method developed by Blankenship and Falk and techniques regarding a semi-infinite optimization problem as a bi-level optimization problem. We develop a new adaptive discretization method which combines the advantages of both techniques and exhibits a quadratic rate of convergence. We further show that a limit of the iterates is a stationary point, if the iterates are stationary points of the approximate problems.
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PublicationAn Axiomatic Perspective on the Performance Effects of End-Host Path Selection( 2021)
;Scherrer, Simon ;Legner, Markus ;Perrig, AdrianIn various contexts of networking research, end-host path selection has recently regained momentum as a design principle. While such path selection has the potential to increase performance and security of networks, there is a prominent concern that it could also lead to network instability (i.e., flow-volume oscillation) if paths are selected in a greedy, load-adaptive fashion. However, the extent and the impact vectors of instability caused by path selection are rarely concretized or quantified, which is essential to discuss the merits and drawbacks of end-host path selection. In this work, we investigate the effect of end-host path selection on various metrics of networks both qualitatively and quantitatively. To achieve general and fundamental insights, we leverage the recently introduced axiomatic perspective on congestion control and adapt it to accommodate joint algorithms for path selection and congestion control, i.e., multi-path congestion-control protocols. Using this approach, we identify equilibria of the multi-path congestion-control dynamics and analytically characterize these equilibria with respect to important metrics of interest in networks (the 'axioms') such as efficiency, fairness, and loss avoidance. Moreover, we analyze how these axiomatic ratings for a general network change compared to a scenario without path selection, thereby obtaining an interpretable and quantititative formalization of the performance impact of end-host path-selection. Finally, we show that there is a fundamental trade-off in multi-path congestion-control protocol design between efficiency, stability, and loss avoidance on one side and fairness and responsiveness on the other side.