Showing papers by "University of Stuttgart published in 2020"

The Molpro quantum chemistry package.

Abstract: Molpro is a general purpose quantum chemistry software package with a long development history. It was originally focused on accurate wavefunction calculations for small molecules but now has many additional distinctive capabilities that include, inter alia, local correlation approximations combined with explicit correlation, highly efficient implementations of single-reference correlation methods, robust and efficient multireference methods for large molecules, projection embedding, and anharmonic vibrational spectra. In addition to conventional input-file specification of calculations, Molpro calculations can now be specified and analyzed via a new graphical user interface and through a Python framework.

High resolution temporal profiles in the Emissions Database for Global Atmospheric Research.

Abstract: Emissions into the atmosphere from human activities show marked temporal variations, from inter-annual to hourly levels. The consolidated practice of calculating yearly emissions follows the same temporal allocation of the underlying annual statistics. However, yearly emissions might not reflect heavy pollution episodes, seasonal trends, or any time-dependant atmospheric process. This study develops high-time resolution profiles for air pollutants and greenhouse gases co- emitted by anthropogenic sources in support of atmospheric modelling, Earth observation communities and decision makers. The key novelties of the Emissions Database for Global Atmospheric Research (EDGAR) temporal profiles are the development of (i) country/region- and sector- specific yearly profiles for all sources, (ii) time dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, (iii) country- specific weekly and daily profiles to represent hourly emissions, (iv) a flexible system to compute hourly emissions including input from different users. This work creates a harmonized emission temporal distribution to be applied to any emission database as input for atmospheric models, thus promoting homogeneity in inter-comparison exercises. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12052887

Bias in data-driven artificial intelligence systems—An introductory survey

Abstract: Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame. In this survey, we focus on data‐driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.

Beneficial Properties of Green Tea Catechins.

Abstract: Green tea (Camellia sinesis) is widely known for its anticancer and anti-inflammatory properties. Among the biologically active compounds contained in Camellia sinesis, the main antioxidant agents are catechins. Recent scientific research indicates that the number of hydroxyl groups and the presence of characteristic structural groups have a major impact on the antioxidant activity of catechins. The best source of these compounds is unfermented green tea. Depending on the type and origin of green tea leaves, their antioxidant properties may be uneven. Catechins exhibit the strong property of neutralizing reactive oxygen and nitrogen species. The group of green tea catechin derivatives includes: epicatechin, epigallocatechin, epicatechin gallate and epigallocatechin gallate. The last of these presents the most potent anti-inflammatory and anticancer potential. Notably, green tea catechins are widely described to be efficient in the prevention of lung cancer, breast cancer, esophageal cancer, stomach cancer, liver cancer and prostate cancer. The current review aims to summarize the potential anticancer effects and molecular signaling pathways of major green tea catechins. It needs to be clearly emphasized that green tea as well as green tea catechols cannot replace the standard chemotherapy. Nonetheless, their beneficial effects may support the standard anticancer approach.

Constraints on the shallow elastic and anelastic structure of Mars from InSight seismic data

Abstract: Mars’s seismic activity and noise have been monitored since January 2019 by the seismometer of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander. At night, Mars is extremely quiet; seismic noise is about 500 times lower than Earth’s microseismic noise at periods between 4 s and 30 s. The recorded seismic noise increases during the day due to ground deformations induced by convective atmospheric vortices and ground-transferred wind-generated lander noise. Here we constrain properties of the crust beneath InSight, using signals from atmospheric vortices and from the hammering of InSight’s Heat Flow and Physical Properties (HP3) instrument, as well as the three largest Marsquakes detected as of September 2019. From receiver function analysis, we infer that the uppermost 8–11 km of the crust is highly altered and/or fractured. We measure the crustal diffusivity and intrinsic attenuation using multiscattering analysis and find that seismic attenuation is about three times larger than on the Moon, which suggests that the crust contains small amounts of volatiles. The crust beneath the InSight lander on Mars is altered or fractured to 8–11 km depth and may bear volatiles, according to an analysis of seismic noise and wave scattering recorded by InSight’s seismometer.

Phase imaging with an untrained neural network.

Abstract: Most of the neural networks proposed so far for computational imaging (CI) in optics employ a supervised training strategy, and thus need a large training set to optimize their weights and biases. Setting aside the requirements of environmental and system stability during many hours of data acquisition, in many practical applications, it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training. Here, we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation. The most significant advantage of the resulting physics-enhanced deep neural network (PhysenNet) is that it can be used without training beforehand, thus eliminating the need for tens of thousands of labeled data. We take single-beam phase imaging as an example for demonstration. We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object, and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model. This opens up a new paradigm of neural network design, in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

Urban Digital Twins for Smart Cities and Citizens: The Case Study of Herrenberg, Germany

Abstract: Cities are complex systems connected to economic, ecological, and demographic conditions and change. They are also characterized by diverging perceptions and interests of citizens and stakeholders. Thus, in the arena of urban planning, we are in need of approaches that are able to cope not only with urban complexity but also allow for participatory and collaborative processes to empower citizens. This to create democratic cities. Connected to the field of smart cities and citizens, we present in this paper, the prototype of an urban digital twin for the 30,000-people town of Herrenberg in Germany. Urban digital twins are sophisticated data models allowing for collaborative processes. The herein presented prototype comprises (1) a 3D model of the built environment, (2) a street network model using the theory and method of space syntax, (3) an urban mobility simulation, (4) a wind flow simulation, and (5) a number of empirical quantitative and qualitative data using volunteered geographic information (VGI). In addition, the urban digital twin was implemented in a visualization platform for virtual reality and was presented to the general public during diverse public participatory processes, as well as in the framework of the “Morgenstadt Werkstatt” (Tomorrow’s Cities Workshop). The results of a survey indicated that this method and technology could significantly aid in participatory and collaborative processes. Further understanding of how urban digital twins support urban planners, urban designers, and the general public as a collaboration and communication tool and for decision support allows us to be more intentional when creating smart cities and sustainable cities with the help of digital twins. We conclude the paper with a discussion of the presented results and further research directions.

The atmosphere of Mars as observed by InSight

Abstract: The atmosphere of Mars is thin, although rich in dust aerosols, and covers a dry surface. As such, Mars provides an opportunity to expand our knowledge of atmospheres beyond that attainable from the atmosphere of the Earth. The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander is measuring Mars’s atmosphere with unprecedented continuity, accuracy and sampling frequency. Here we show that InSight unveils new atmospheric phenomena at Mars, especially in the higher-frequency range, and extends our understanding of Mars’s meteorology at all scales. InSight is uniquely sensitive to large-scale and regional weather and obtained detailed in situ coverage of a regional dust storm on Mars. Images have enabled high-altitude wind speeds to be measured and revealed airglow—faint emissions produced by photochemical reactions—in the middle atmosphere. InSight observations show a paradox of aeolian science on Mars: despite having the largest recorded Martian vortex activity and dust-devil tracks close to the lander, no visible dust devils have been seen. Meteorological measurements have produced a catalogue of atmospheric gravity waves, which included bores (soliton-like waves). From these measurements, we have discovered Martian infrasound and unexpected similarities between atmospheric turbulence on Earth and Mars. We suggest that the observations of Mars’s atmosphere by InSight will be key for prediction capabilities and future exploration.

Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There A Sustainable “Digital”?

Abstract: Digitalization plays a major role in contributing towards the United Nations Sustainable Development Goals Without transformation of existing businesses, both economic and environmental challenges of the future cannot be solved sustainably However, there is much confusion on interrelationships and terms dealing with digitization or digitalization: Digital business model, digital transformation, digital entrepreneurship How do these terms interrelate with and to digitalization, and how do they support firms to grow sustainably? To answer this question, we identified seven core digital-related terms based on a structured literature search within the management and economics domain, namely: Digital, Business Model, Digital Business Model, Digital Technology, Digital Innovation, Digital Transformation, and Digital Entrepreneurship Thereafter, we analyzed prior literature for deriving a common understanding and definition as a basis for interrelations within a conceptual framework Definitions were presented in a case study setup with twelve innovation and research and development (R&D) managers from various business units of a German high-tech company Based on these insights, we propose a conceptual framework on how Digital Readiness, Digital Technology, and Digital Business Models might sustainably relate to Innovation, moderated by a Digital Transformation Process With this approach, we aim to equip practitioners and researchers alike in handling and addressing change through digitalization sustainably

A Survey on Learning-Based Robotic Grasping

Abstract: This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation. Current trends and developments as well as various criteria for categorization of approaches are provided. Model-free approaches are attractive due to their generalization capabilities to novel objects, but are mostly limited to top-down grasps and do not allow a precise object placement which can limit their applicability. In contrast, model-based methods allow a precise placement and aim for an automatic configuration without any human intervention to enable a fast and easy deployment. Both approaches to robotic grasping and manipulation with and without object-specific knowledge are discussed. Due to the large amount of data required to train AI-based approaches, simulations are an attractive choice for robot learning. This article also gives an overview of techniques and achievements in transfers from simulations to the real world.

Cultivation and functional characterization of 79 planctomycetes uncovers their unique biology

Abstract: When it comes to the discovery and analysis of yet uncharted bacterial traits, pure cultures are essential as only these allow detailed morphological and physiological characterization as well as genetic manipulation. However, microbiologists are struggling to isolate and maintain the majority of bacterial strains, as mimicking their native environmental niches adequately can be a challenging task. Here, we report the diversity-driven cultivation, characterization and genome sequencing of 79 bacterial strains from all major taxonomic clades of the conspicuous bacterial phylum Planctomycetes. The samples were derived from different aquatic environments but close relatives could be isolated from geographically distinct regions and structurally diverse habitats, implying that ‘everything is everywhere’. With the discovery of lateral budding in ‘Kolteria novifilia’ and the capability of the members of the Saltatorellus clade to divide by binary fission as well as budding, we identified previously unknown modes of bacterial cell division. Alongside unobserved aspects of cell signalling and small-molecule production, our findings demonstrate that exploration beyond the well-established model organisms has the potential to increase our knowledge of bacterial diversity. We illustrate how ‘microbial dark matter’ can be accessed by cultivation techniques, expanding the organismic background for small-molecule research and drug-target detection. The combined use of genome sequencing, cultivation and phenotypic characterization of 79 globally distributed strains from the bacterial phylum Planctomycetes sheds light on their varied cell shapes, modes of cell division and extensive signalling and metabolic potential.

Atom-Tunneling in Chemistry

Abstract: Quantum mechanical tunneling of atoms is increasingly found to play an important role in many chemical transformations. Experimentally, atom-tunneling can be indirectly detected by temperature-independent rate constants at low temperature or by enhanced kinetic isotope effects. On the contrary, using computational investigations the influence of tunneling on the reaction rates can directly be monitored. The tunnel effect, for example, changes reaction paths and branching ratios, enables chemical reactions in an astrochemical environment that would be impossible by thermal transition, and influences biochemical processes.

Acoustic Holographic Cell Patterning in a Biocompatible Hydrogel

Abstract: Acoustophoresis is promising as a rapid, biocompatible, noncontact cell manipulation method, where cells are arranged along the nodes or antinodes of the acoustic field. Typically, the acoustic field is formed in a resonator, which results in highly symmetric regular patterns. However, arbitrary, nonsymmetrically shaped cell assemblies are necessary to obtain the irregular cellular arrangements found in biological tissues. It is shown that arbitrarily shaped cell patterns can be obtained from the complex acoustic field distribution defined by an acoustic hologram. Attenuation of the sound field induces localized acoustic streaming and the resultant convection flow gently delivers the suspended cells to the image plane where they form the designed pattern. It is shown that the process can be implemented in a biocompatible collagen solution, which can then undergo gelation to immobilize the cell pattern inside the viscoelastic matrix. The patterned cells exhibit F-actin-based protrusions, which indicate that the cells grow and thrive within the matrix. Cell viability assays and brightfield imaging after one week confirm cell survival and that the patterns persist. Acoustophoretic cell manipulation by holographic fields thus holds promise for noncontact, long-range, long-term cellular pattern formation, with a wide variety of potential applications in tissue engineering and mechanobiology.

Selective Targeting of TNF Receptors as a Novel Therapeutic Approach

Abstract: Tumor necrosis factor (TNF) is a central regulator of immunity. Due to its dominant pro-inflammatory effects, drugs that neutralize TNF were developed and are clinically used to treat inflammatory and autoimmune diseases, such as rheumatoid arthritis, inflammatory bowel disease and psoriasis. However, despite their clinical success the use of anti-TNF drugs is limited, in part due to unwanted, severe side effects and in some diseases its use even is contraindicative. With gaining knowledge about the signaling mechanisms of TNF and the differential role of the two TNF receptors (TNFR), alternative therapeutic concepts based on receptor selective intervention have led to the development of novel protein therapeutics targeting TNFR1 with antagonists and TNFR2 with agonists. These antibodies and bio-engineered ligands are currently in preclinical and early clinical stages of development. Preclinical data obtained in different disease models show that selective targeting of TNFRs has therapeutic potential and may be superior to global TNF blockade in several disease indications.

Thermodynamic Uncertainty Relation for Time-Dependent Driving

Abstract: Thermodynamic uncertainty relations yield a lower bound on entropy production in terms of the mean and fluctuations of a current. We derive their general form for systems under arbitrary time-dependent driving from arbitrary initial states and extend these relations beyond currents to state variables. The quality of the bound is discussed for various types of observables for an interacting pair of colloidal particles in a moving laser trap and for the dynamical unfolding of a small protein. Since the input for evaluating these bounds does not require specific knowledge of the system or its coupling to the time-dependent control, they should become widely applicable tools for thermodynamic inference in time-dependently driven systems.

A trajectory-based framework for data-driven system analysis and control.

Abstract: The vector space of all input-output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given that the respective input signal is persistently exciting. This fact, which was proven in the behavioral control framework, shows that a single measured trajectory can capture the full behavior of an LTI system and might therefore be used directly for system analysis and controller design, without explicitly identifying a model. In this paper, we translate the result from the behavioral context to the classical state-space control framework and we extend it to certain classes of nonlinear systems, which are linear in suitable input-output coordinates. Moreover, we show how this extension can be applied to the data-driven simulation problem, where we introduce kernel-methods to obtain a rich set of basis functions.

The CRYSTAL code, 1976-2020 and beyond, a long story.

Abstract: CRYSTAL is a periodic ab initio code that uses a Gaussian-type basis set to express crystalline orbitals (i.e., Bloch functions). The use of atom-centered basis functions allows treating 3D (crystals), 2D (slabs), 1D (polymers), and 0D (molecules) systems on the same grounds. In turn, all-electron calculations are inherently permitted along with pseudopotential strategies. A variety of density functionals are implemented, including global and range-separated hybrids of various natures and, as an extreme case, Hartree–Fock (HF). The cost for HF or hybrids is only about 3–5 times higher than when using the local density approximation or the generalized gradient approximation. Symmetry is fully exploited at all steps of the calculation. Many tools are available to modify the structure as given in input and simplify the construction of complicated objects, such as slabs, nanotubes, molecules, and clusters. Many tensorial properties can be evaluated by using a single input keyword: elastic, piezoelectric, photoelastic, dielectric, first and second hyperpolarizabilities, etc. The calculation of infrared and Raman spectra is available, and the intensities are computed analytically. Automated tools are available for the generation of the relevant configurations of solid solutions and/or disordered systems. Three versions of the code exist: serial, parallel, and massive-parallel. In the second one, the most relevant matrices are duplicated on each core, whereas in the third one, the Fock matrix is distributed for diagonalization. All the relevant vectors are dynamically allocated and deallocated after use, making the code very agile. CRYSTAL can be used efficiently on high performance computing machines up to thousands of cores.

Robust data-driven state-feedback design

Abstract: We consider the problem of designing robust state-feedback controllers for discrete-time linear time-invariant systems, based directly on measured data. The proposed design procedures require no model knowledge, but only a single open-loop data trajectory, which may be affected by noise. First, a data-driven characterization of the uncertain class of closed-loop matrices under state-feedback is derived. By considering this parametrization in the robust control framework, we design data-driven state-feedback gains with guarantees on stability and performance, containing, e.g., the ℋ ∞ -control problem as a special case. Further, we show how the proposed framework can be extended to take partial model knowledge into account. The validity of the proposed approach is illustrated via a numerical example.

Ultrafast vector imaging of plasmonic skyrmion dynamics with deep subwavelength resolution

Abstract: Plasmonic skyrmions are an optical manifestation of topological defects in a continuous vector field. Identifying them requires characterization of the vector structure of the electromagnetic near field on thin metal films. Here we introduce time-resolved vector microscopy that creates movies of the electric field vectors of surface plasmons with subfemtosecond time steps and a 10-nanometer spatial scale. We image complete time sequences of propagating surface plasmons as well as plasmonic skyrmions, resolving all vector components of the electric field and their time dynamics, thus demonstrating dynamic spin-momentum coupling as well as the time-varying skyrmion number. The ability to image linear optical effects in the spin and phase structures of light in the single-nanometer range will allow for entirely novel microscopy and metrology applications.

Mixed Precision DNNs: All you need is a good parametrization

Abstract: Efficient deep neural network (DNN) inference on mobile or embedded devices typically involves quantization of the network parameters and activations. In particular, mixed precision networks achieve better performance than networks with homogeneous bitwidth for the same size constraint. Since choosing the optimal bitwidths is not straight forward, training methods, which can learn them, are desirable. Differentiable quantization with straight-through gradients allows to learn the quantizer's parameters using gradient methods. We show that a suited parametrization of the quantizer is the key to achieve a stable training and a good final performance. Specifically, we propose to parametrize the quantizer with the step size and dynamic range. The bitwidth can then be inferred from them. Other parametrizations, which explicitly use the bitwidth, consistently perform worse. We confirm our findings with experiments on CIFAR-10 and ImageNet and we obtain mixed precision DNNs with learned quantization parameters, achieving state-of-the-art performance.

Flexible low-voltage high-frequency organic thin-film transistors.

Abstract: The primary driver for the development of organic thin-film transistors (TFTs) over the past few decades has been the prospect of electronics applications on unconventional substrates requiring low-temperature processing. A key requirement for many such applications is high-frequency switching or amplification at the low operating voltages provided by lithium-ion batteries (~3 V). To date, however, most organic-TFT technologies show limited dynamic performance unless high operating voltages are applied to mitigate high contact resistances and large parasitic capacitances. Here, we present flexible low-voltage organic TFTs with record static and dynamic performance, including contact resistance as small as 10 Ω·cm, on/off current ratios as large as 1010, subthreshold swing as small as 59 mV/decade, signal delays below 80 ns in inverters and ring oscillators, and transit frequencies as high as 21 MHz, all while using an inverted coplanar TFT structure that can be readily adapted to industry-standard lithographic techniques.