Showing papers by "University of Paderborn published in 2021"

Aleatoric and epistemic uncertainty in machine learning : an introduction to concepts and methods

Abstract: The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.

Knowledge Graphs

Abstract: In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI

Abstract: An increasing number of firms introduce service robots, such as physical robots and virtual chatbots, to provide services to customers. While some firms use robots that resemble human beings by looking and acting humanlike to increase customers’ use intention of this technology, others employ machinelike robots to avoid uncanny valley effects, assuming that very humanlike robots may induce feelings of eeriness. There is no consensus in the service literature regarding whether customers’ anthropomorphism of robots facilitates or constrains their use intention. The present meta-analysis synthesizes data from 11,053 individuals interacting with service robots reported in 108 independent samples. The study synthesizes previous research to clarify this issue and enhance understanding of the construct. We develop a comprehensive model to investigate relationships between anthropomorphism and its antecedents and consequences. Customer traits and predispositions (e.g., computer anxiety), sociodemographics (e.g., gender), and robot design features (e.g., physical, nonphysical) are identified as triggers of anthropomorphism. Robot characteristics (e.g., intelligence) and functional characteristics (e.g., usefulness) are identified as important mediators, although relational characteristics (e.g., rapport) receive less support as mediators. The findings clarify contextual circumstances in which anthropomorphism impacts customer intention to use a robot. The moderator analysis indicates that the impact depends on robot type (i.e., robot gender) and service type (i.e., possession-processing service, mental stimulus-processing service). Based on these findings, we develop a comprehensive agenda for future research on service robots in marketing.

Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors

Abstract: The performance of model predictive controllers (MPC) strongly depends on the quality of their models. In the field of electric drive control, white-box (WB) modeling approaches derived from first-order physical principles are most common. This procedure typically does not cover parasitic effects and often comes with parameter deviations. These issues are particularly crucial in the domain of self-commissioning drives where a hand-tailored, accurate WB plant model is not available. In order to compensate for such modeling errors and, consequently, to improve the control performance during transients and steady state, this article proposes a data-driven, real-time capable recursive least squares estimation method for the current control of a permanent magnet synchronous motor. Following this machine learning approach, the effect of the flux linkage and voltage harmonics due to the winding scheme can also be taken into account through suitable feature engineering. Moreover, a compensating scheme for the interlocking time of the inverter is proposed. The resulting algorithm is investigated using the well-known finite-control-set MPC (FCS-MPC) in the rotor-oriented coordinate system. The extensive experimental results show the superior performance of the presented scheme compared to a FCS-MPC-based on a state-of-the-art WB motor model using look-up tables for adressing (cross-)saturation.

Unraveling the interaction between doxorubicin and DNA origami nanostructures for customizable chemotherapeutic drug release

Abstract: Doxorubicin (DOX) is a common drug in cancer chemotherapy, and its high DNA-binding affinity can be harnessed in preparing DOX-loaded DNA nanostructures for targeted delivery and therapeutics. Although DOX has been widely studied, the existing literature of DOX-loaded DNA-carriers remains limited and incoherent. Here, based on an in-depth spectroscopic analysis, we characterize and optimize the DOX loading into different 2D and 3D scaffolded DNA origami nanostructures (DONs). In our experimental conditions, all DONs show similar DOX binding capacities (one DOX molecule per two to three base pairs), and the binding equilibrium is reached within seconds, remarkably faster than previously acknowledged. To characterize drug release profiles, DON degradation and DOX release from the complexes upon DNase I digestion was studied. For the employed DONs, the relative doses (DOX molecules released per unit time) may vary by two orders of magnitude depending on the DON superstructure. In addition, we identify DOX aggregation mechanisms and spectral changes linked to pH, magnesium, and DOX concentration. These features have been largely ignored in experimenting with DNA nanostructures, but are probably the major sources of the incoherence of the experimental results so far. Therefore, we believe this work can act as a guide to tailoring the release profiles and developing better drug delivery systems based on DNA-carriers.

Visualization of dynamic polaronic strain fields in hybrid lead halide perovskites.

Abstract: Excitation localization involving dynamic nanoscale distortions is a central aspect of photocatalysis1, quantum materials2 and molecular optoelectronics3. Experimental characterization of such distortions requires techniques sensitive to the formation of point-defect-like local structural rearrangements in real time. Here, we visualize excitation-induced strain fields in a prototypical member of the lead halide perovskites4 via femtosecond resolution diffuse X-ray scattering measurements. This enables momentum-resolved phonon spectroscopy of the locally distorted structure and reveals radially expanding nanometre-scale strain fields associated with the formation and relaxation of polarons in photoexcited perovskites. Quantitative estimates of the magnitude and shape of this polaronic distortion are obtained, providing direct insights into the dynamic structural distortions that occur in these materials5–9. Optical pump–probe reflection spectroscopy corroborates these results and shows how these large polaronic distortions transiently modify the carrier effective mass, providing a unified picture of the coupled structural and electronic dynamics that underlie the optoelectronic functionality of the hybrid perovskites. Diffuse X-ray scattering with femtosecond resolution shows the formation and relaxation of polaronic distortions in halide perovskites. These structural changes are also quantified and correlated to transient changes in carrier effective mass.

Optical secret sharing with cascaded metasurface holography

Abstract: Secret sharing is a well-established cryptographic primitive for storing highly sensitive information like encryption keys for encoded data. It describes the problem of splitting a secret into different shares, without revealing any information about the secret to its shareholders. Here, we demonstrate an all-optical solution for secret sharing based on metasurface holography. In our concept, metasurface holograms are used as spatially separable shares that carry an encrypted message in form of a holographic image. Two of these shares can be recombined by bringing them close together. Light passing through this stack of metasurfaces accumulates the phase shift of both holograms and can optically reconstruct the secret with high fidelity. On the other hand, the holograms generated by the single metasurfaces can be used for identifying each shareholder. Furthermore, we demonstrate that the inherent translational alignment sensitivity between the two stacked metasurface holograms can be used for spatial multiplexing, which can be further extended to realize optical rulers.

Can Rotational Fluxes Impede the Tendency Toward Spatial Homogeneity in Nutrient Taxis(-Stokes) Systems?

Abstract: We consider the spatially 2D version of the model $$\\begin{equation*} \\qquad\\quad\\left\\{ \\begin{array}{@{}rcll} n_t + u\\cdot\ abla n &=& \\Delta n - \ abla \\cdot \\big(nS(x,n,c) \\cdot \ abla c \\big), \\qquad &\\qquad x\\in \\Omega, \\ t>0, \\\\ c_t + u\\cdot \ abla c &=& \\Delta c - n f(c), \\qquad &\\qquad x\\in \\Omega, \\ t>0, \\\\ u_t &=& \\Delta u + \ abla P + n\ abla\\phi, \\qquad \ abla\\cdot u=0, \\qquad &\\qquad x\\in \\Omega, \\ t>0, \\end{array} \\right. \\qquad \\qquad (\\star) \\end{equation*}$$for nutrient taxis processes, possibly interacting with liquid environments. Here the particular focus is on the situation when the chemotactic sensitivity $S$ is not a scalar function but rather attains general values in ${\\mathbb{R}}^{2\\times 2}$, thus accounting for rotational flux components in accordance with experimental findings and recent modeling approaches. Reflecting significant new challenges that mainly stem from apparent loss of energy-like structures, especially for initial data with large size, the knowledge on ($\\star$) so far seems essentially restricted to results on global existence of certain generalized solutions with possibly quite poor boundedness and regularity properties; widely unaddressed seem aspects related to possible effects of such non-diagonal taxis mechanisms on the qualitative solution behavior, especially with regard to the fundamental question whether spatial structures may thereby be supported. The present work answers the latter in the negative in the following sense: under the assumptions that the initial data $(n_0,c_0,u_0)$ and the parameter functions $S$, $f$, and $\\phi$ are sufficiently smooth, and that $S$ is bounded and $f$ is positive on $(0,\\infty )$ with $f(0)=0$, it is shown that any nontrivial of these solutions eventually becomes smooth and satisfies $$\\begin{equation*} n(\\cdot,t)\\to - \\int_\\Omega n_0, \\quad c(\\cdot,t)\\to 0 \\quad \\text{and} \\quad u(\\cdot,t)\\to 0 \\qquad \\text{as} \\ t\\to\\infty, \\end{equation*}$$uniformly with respect to $x\\in \\Omega$. By not requiring any smallness condition on the initial data, the latter seems new even in the corresponding fluid-free version obtained on letting $u\\equiv 0$ in ($\\star$).

How does spectator presence affect football? Home advantage remains in European top-class football matches played without spectators during the COVID-19 pandemic.

Abstract: The present paper investigates factors contributing to the home advantage, by using the exceptional opportunity to study professional football matches played in the absence of spectators due to the COVID-19 pandemic in 2020. More than 40,000 matches before and during the pandemic, including more than 1,000 professional matches without spectators across the main European football leagues, have been analyzed. Results support the notion of a crowd-induced referee bias as the increased sanctioning of away teams disappears in the absence of spectators with regard to fouls (p < .001), yellow cards (p < .001), and red cards (p < .05). Moreover, the match dominance of home teams decreases significantly as indicated by shots (p < .001) and shots on target (p < .01). In terms of the home advantage itself, surprisingly, only a non-significant decrease is found. While the present paper supports prior research with regard to a crowd-induced referee bias, spectators thus do not seem to be the main driving factor of the home advantage. Results from amateur football, being naturally played in absence of a crowd, provide further evidence that the home advantage is predominantly caused by factors not directly or indirectly attributable to a noteworthy number of spectators.

Estimating Electric Motor Temperatures With Deep Residual Machine Learning

Abstract: Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this article, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors. Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multihead architecture, lean models are presented that exhibit a strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented.

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

Abstract: The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.

Quantum dots as potential sources of strongly entangled photons: Perspectives and challenges for applications in quantum networks

Abstract: The generation and long-haul transmission of highly entangled photon pairs is a cornerstone of emerging photonic quantum technologies with key applications such as quantum key distribution and distributed quantum computing. However, a natural limit for the maximum transmission distance is inevitably set by attenuation in the medium. A network of quantum repeaters containing multiple sources of entangled photons would allow overcoming this limit. For this purpose, the requirements on the source's brightness and the photon pairs' degree of entanglement and indistinguishability are stringent. Despite the impressive progress made so far, a definitive scalable photon source fulfilling such requirements is still being sought after. Semiconductor quantum dots excel in this context as sub-Poissonian sources of polarization entangled photon pairs. In this work, we present the state-of-the-art set by GaAs based quantum dots and use them as a benchmark to discuss the challenges toward the realization of practical quantum networks.

Correction: Quantification of heterogeneous, irreversible lithium plating in extreme fast charging of lithium-ion batteries

Abstract: Realization of extreme fast charging (XFC, ≤15 minutes) of lithium-ion batteries is imperative for the widespread adoption of electric vehicles. However, dramatic capacity fading is associated with XFC, limiting its implementation. To quantitatively elucidate the effects of irreversible lithium plating and other degradation mechanisms on the cell capacity, it is important to understand the links between lithium plating and cell degradation at both the local and global (over the full cell) scales. Here, we study the nature of local lithium plating after hundreds of XFC cycles (charging C-rates ranging from 4C to 9C) in industrially-relevant pouch cells using spatially resolved X-ray diffraction. Our results reveal a spatial correlation at the mm scale between irreversible lithium plating on the anode, inactive lithiated graphite phases, and local state-of-charge of the cathode. In regions of plated lithium, additional lithium is locally and irreversibly trapped as lithiated graphite, contributing to the loss of lithium inventory (LLI) and to a local loss of active anode material. The total LLI in the cell from irreversibly plated lithium is linearly correlated to the capacity loss in the batteries after XFC cycling, with a non-zero offset originating from other parasitic side reactions. Finally, at the global (cell) scale, LLI drives the capacity fade, rather than electrode degradation. We anticipate that the understanding of lithium plating and other degradation mechanisms during XFC gained in this work will help lead to new approaches towards designing high-rate batteries in which irreversible lithium plating is minimized.

Nonlinear Imaging of Nanoscale Topological Corner States.

Abstract: Topological states of light represent counterintuitive optical modes localized at boundaries of finite-size optical structures that originate from the properties of the bulk. Being defined by bulk properties, such boundary states are insensitive to certain types of perturbations, thus naturally enhancing robustness of photonic circuitries. Conventionally, the N-dimensional bulk modes correspond to (N - 1)-dimensional boundary states. The higher-order bulk-boundary correspondence relates N-dimensional bulk to boundary states with dimensionality reduced by more than 1. A special interest lies in miniaturization of such higher-order topological states to the nanoscale. Here, we realize nanoscale topological corner states in metasurfaces with C6-symmetric honeycomb lattices. We directly observe nanoscale topology-empowered edge and corner localizations of light and enhancement of light-matter interactions via a nonlinear imaging technique. Control of light at the nanoscale empowered by topology may facilitate miniaturization and on-chip integration of classical and quantum photonic devices.

Integrated photon-pair sources with nonlinear optics

Abstract: Assisted by the rapid development of photonic integrated circuits, scalable and versatile chip-based quantum light sources with nonlinear optics are increasingly tangible for real-world applications. In this review, we introduce the basic concepts behind parametric photon pair sources and discuss the current state-of-the-art photon pair generation in detail but also highlight future perspectives in hybrid integration, novel waveguide structures, and on-chip multiplexing. The advances in near-deterministic integrated photon pair sources are deemed to pave the way for the realization of large-scale quantum photonic integrated circuits for applications, including quantum telecommunication, quantum sensing, quantum metrology, and photonic quantum computing.

Far-Field Automatic Speech Recognition

Abstract: The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase in attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile, it has entered the consumer market with digital home assistants with a spoken language interface being its most prominent application. Speech recorded at a distance is affected by various acoustic distortions, and consequently, quite different processing pipelines have emerged compared with ASR for close-talk speech. A signal enhancement front end for dereverberation, source separation, and acoustic beamforming is employed to clean up the speech, and the back-end ASR engine is robustified by multicondition training and adaptation. We will also describe the so-called end-to-end approach to ASR, which is a new promising architecture that has recently been extended to the far-field scenario. This tutorial article gives an account of the algorithms used to enable accurate speech recognition from a distance, and it will be seen that, although deep learning has a significant share in the technological breakthroughs, a clever combination with traditional signal processing can lead to surprisingly effective solutions.

Implementing The Abstract MAC Layer in Dynamic Networks

Abstract: Dynamicity is one of the most challenging, yet, key aspects of wireless networks. It can come in many guises, such as churn (node insertion/deletion) and node mobility. Although the study of dynamic networks has been popular in distributed computing domain, previous works considered only partial factors causing dynamicity. In this work, we propose a dynamic model that is comprehensive to include crucial dynamic factors on nodes and links. Our model defines dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a localized dynamic model suits distributed algorithm studies better than a global one. The proposed dynamic model makes use of the more realistic SINR model to describe wireless interference, instead of the oversimplified graph-based models adopted by most existing research. Under the proposed dynamic model, we develop an efficient distributed algorithm accomplishing local broadcast services in the abstract MAC layer that was first presented by Kuhn et al. [24] . Our solution paves the way for many new fast algorithms to solve high-level problems in dynamic networks, such as consensus, single-message broadcast, and multiple-message broadcast. Extensive simulation studies indicate that our algorithm exhibits good performance in realistic environments with dynamic network behaviors.

Interfacial Covalent Bonds Regulated Electron-Deficient 2D Black Phosphorus for Electrocatalytic Oxygen Reactions.

Abstract: Developing resource-abundant and sustainable metal-free bifunctional oxygen electrocatalysts is essential for the practical application of zinc–air batteries (ZABs). 2D black phosphorus (BP) with fully exposed atoms and active lone pair electrons can be promising for oxygen electrocatalysts, which, however, suffers from low catalytic activity and poor electrochemical stability. Herein, guided by density functional theory (DFT) calculations, an efficient metal-free electrocatalyst is demonstrated via covalently bonding BP nanosheets with graphitic carbon nitride (denoted BP-CN-c). The polarized PN covalent bonds in BP-CN-c can efficiently regulate the electron transfer from BP to graphitic carbon nitride and significantly promote the OOH* adsorption on phosphorus atoms. Impressively, the oxygen evolution reaction performance of BP-CN-c (overpotential of 350 mV at 10 mA cm−2, 90\% retention after 10 h operation) represents the state-of-the-art among the reported BP-based metal-free catalysts. Additionally, BP-CN-c exhibits a small half-wave overpotential of 390 mV for oxygen reduction reaction, representing the first bifunctional BP-based metal-free oxygen catalyst. Moreover, ZABs are assembled incorporating BP-CN-c cathodes, delivering a substantially higher peak power density (168.3 mW cm−2) than the Pt/C+RuO2-based ZABs (101.3 mW cm−2). The acquired insights into interfacial covalent bonds pave the way for the rational design of new and affordable metal-free catalysts.

Local energy estimates and global solvability in a three-dimensional chemotaxis-fluid system with prescribed signal on the boundary

Abstract: The chemotaxis-Stokes system{nt+u·∇n=Δn−∇·(n∇c),ct+u·∇c=Δc−nc,ut=Δu+∇P+n∇ϕ, ∇·u=0, (⋆)is considered in a bounded domain Ω⊂R3 with smooth boundary. The corresponding solution theory is quite wel...

Machine learning for human learners: opportunities, issues, tensions and threats

Abstract: Machine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.

AutoML for Multi-Label Classification: Overview and Empirical Evaluation

Abstract: Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.

Seven Paradoxes of Business Process Management in a Hyper-Connected World

Abstract: Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.

ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for ASR Integration

Abstract: We present ESPnet-SE, which is designed for the quick development of speech enhancement and speech separation systems in a single framework, along with the optional downstream speech recognition module. ESPnet-SE is a new project which integrates rich automatic speech recognition related models, resources and systems to support and validate the proposed front-end implementation (i.e. speech enhancement and separation). It is capable of processing both single-channel and multi-channel data, with various functionalities including dereverberation, denoising and source separation. We provide all-in-one recipes including data pre-processing, feature extraction, training and evaluation pipelines for a wide range of benchmark datasets. This paper describes the design of the toolkit, several important functionalities, especially the speech recognition integration, which differentiates ESPnet-SE from other open source toolkits, and experimental results with major benchmark datasets.

Session Resumption Protocols and Efficient Forward Security for TLS 1.3 0-RTT

Abstract: The TLS 1.3 0-RTT mode enables a client reconnecting to a server to send encrypted application-layer data in “0-RTT” (“zero round-trip time”), without the need for a prior interactive handshake. This fundamentally requires the server to reconstruct the previous session’s encryption secrets upon receipt of the client’s first message. The standard techniques to achieve this are Session Caches or, alternatively, Session Tickets. The former provides forward security and resistance against replay attacks, but requires a large amount of server-side storage. The latter requires negligible storage, but provides no forward security and is known to be vulnerable to replay attacks.

Exercise and sports after COVID-19 - Guidance from a clinical perspective

Abstract: SARS-CoV-2 infection has emerged as not only a pulmonary but also potentially multi-organ disease, which may cause long-term structural damage of different organ systems including the lung, heart, vasculature, brain, liver, kidney, or intestine. As a result, the current SARS-CoV-2/COVID-19 pandemic will eventually yield substantially increased numbers of chronically diseased patients worldwide, particularly suffering from pulmonary fibrosis, post-myocarditis, chronic heart failure, or chronic kidney disease. Exercise recommendations for rehabilitation are complex in these patients and should follow current guidelines including standards for pre-exercise medical examinations and individually tailored exercise prescription. It is of utmost importance to start exercise training at an early stage after COVID-19 infection, but at the same time paying attention to the physical barriers to ensure safe return to exercise. For exercise recommendations beyond rehabilitation programs particularly for leisure time and elite athletes, more precise advice is required including assessment of sports eligibility and specific return-to-sports exercise programs. Because of the current uncertainty of long-term course of SARS-CoV-2 infection or COVID disease, long-term follow-up seems to be necessary.