Austrian Institute of Technology

Quantum key distribution (QKD) is the first quantum information task to reach the level of mature technology, already fit for commercialization. It aims at the creation of a secret key between authorized partners connected by a quantum channel and a classical authenticated channel. The security of the key can in principle be guaranteed without putting any restriction on an eavesdropper's power. This article provides a concise up-to-date review of QKD, biased toward the practical side. Essential theoretical tools that have been developed to assess the security of the main experimental platforms are presented (discrete-variable, continuous-variable, and distributed-phase-reference protocols).

Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.

All plants are inhabited internally by diverse microbial communities comprising bacterial, archaeal, fungal, and protistic taxa. These microorganisms showing endophytic lifestyles play crucial roles in plant development, growth, fitness, and diversification. The increasing awareness of and information on endophytes provide insight into the complexity of the plant microbiome. The nature of plant-endophyte interactions ranges from mutualism to pathogenicity. This depends on a set of abiotic and biotic factors, including the genotypes of plants and microbes, environmental conditions, and the dynamic network of interactions within the plant biome. In this review, we address the concept of endophytism, considering the latest insights into evolution, plant ecosystem functioning, and multipartite interactions.

This paper describes a 128 times 128 pixel CMOS vision sensor. Each pixel independently and in continuous time quantizes local relative intensity changes to generate spike events. These events appear at the output of the sensor as an asynchronous stream of digital pixel addresses. These address-events signify scene reflectance change and have sub-millisecond timing precision. The output data rate depends on the dynamic content of the scene and is typically orders of magnitude lower than those of conventional frame-based imagers. By combining an active continuous-time front-end logarithmic photoreceptor with a self-timed switched-capacitor differencing circuit, the sensor achieves an array mismatch of 2.1% in relative intensity event threshold and a pixel bandwidth of 3 kHz under 1 klux scene illumination. Dynamic range is > 120 dB and chip power consumption is 23 mW. Event latency shows weak light dependency with a minimum of 15 mus at > 1 klux pixel illumination. The sensor is built in a 0.35 mum 4M2P process. It has 40times40 mum <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> pixels with 9.4% fill factor. By providing high pixel bandwidth, wide dynamic range, and precisely timed sparse digital output, this silicon retina provides an attractive combination of characteristics for low-latency dynamic vision under uncontrolled illumination with low post-processing requirements.

The Photodetector Array Camera and Spectrometer (PACS) is one of the three science instruments on ESA's far infrared and submillimetre observatory. It employs two Ge:Ga photoconductor arrays (stressed and unstressed) with 16×25 pixels, each, and two filled silicon bolometer arrays with 16×32 and 32×64 pixels, respectively, to perform integral-field spectroscopy and imaging photometry in the 60–210 <i>μ<i/>m wavelength regime. In photometry mode, it simultaneously images two bands, 60–85 <i>μ<i/>m or 85–125 <i>μ<i/>m and 125–210 <i>μ<i/>m, over a field of view of ~1.75'× 3.5', with close to Nyquist beam sampling in each band. In spectroscopy mode, it images a field of 47” × 47”, resolved into 5×5 pixels, with an instantaneous spectral coverage of ~1500 km s<sup>-1<sup/> and a spectral resolution of ~175 km s<sup>-1<sup/>. We summarise the design of the instrument, describe observing modes, calibration, and data analysis methods, and present our current assessment of the in-orbit performance of the instrument based on the performance verification tests. PACS is fully operational, and the achieved performance is close to or better than the pre-launch predictions.

The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term "microbiome." Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role. Video Abstract.

PURPOSE: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. THEORY AND METHODS: Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. RESULTS: The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. CONCLUSION: Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055-3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Plants have evolved with a plethora of microorganisms having important roles for plant growth and health. A considerable amount of information is now available on the structure and dynamics of plant microbiota as well as on the functional capacities of isolated community members. Due to the interesting functional potential of plant microbiota as well as due to current challenges in crop production there is an urgent need to bring microbial innovations into practice. Different approaches for microbiome improvement exist. On the one hand microbial strains or strain combinations can be applied, however, field success is often variable and improvement is urgently required. Smart, knowledge-driven selection of microorganisms is needed as well as the use of suitable delivery approaches and formulations. On the other hand, farming practices or the plant genotype can influence plant microbiota and thus functioning. Therefore, selection of appropriate farming practices and plant breeding leading to improved plant-microbiome interactions are avenues to increase the benefit of plant microbiota. In conclusion, different avenues making use of a new generation of inoculants as well as the application of microbiome-based agro-management practices and improved plant lines could lead to a better use of the plant microbiome. This paper reviews the importance and functionalities of the bacterial plant microbiome and discusses challenges and concepts in regard to the application of plant-associated bacteria.

Image restoration is a long-standing problem in low-level computer vision with many interesting applications. We describe a flexible learning framework based on the concept of nonlinear reaction diffusion models for various image restoration problems. By embodying recent improvements in nonlinear diffusion models, we propose a dynamic nonlinear reaction diffusion model with time-dependent parameters (i.e., linear filters and influence functions). In contrast to previous nonlinear diffusion models, all the parameters, including the filters and the influence functions, are simultaneously learned from training data through a loss based approach. We call this approach TNRD-Trainable Nonlinear Reaction Diffusion. The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force. We demonstrate its capabilities with three representative applications, Gaussian image denoising, single image super resolution and JPEG deblocking. Experiments show that our trained nonlinear diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for the tested applications. Our trained models preserve the structural simplicity of diffusion models and take only a small number of diffusion steps, thus are highly efficient. Moreover, they are also well-suited for parallel computation on GPUs, which makes the inference procedure extremely fast.

A secure communication network with quantum key distribution in a metropolitan area is reported. Six different QKD systems are integrated into a mesh-type network. GHz-clocked QKD links enable us to demonstrate the world-first secure TV conferencing over a distance of 45km. The network includes a commercial QKD product for long-term stable operation, and application interface to secure mobile phones. Detection of an eavesdropper, rerouting into a secure path, and key relay via trusted nodes are demonstrated in this network.

Biochars are increasingly used as soil amendment and for C sequestration in soils. The influence of feedstock differences and pyrolysis temperature on biochar characteristics has been widely studied. However, there is a lack of knowledge about the formation of potentially toxic compounds that remain in the biochars after pyrolysis. We investigated biochars from three feedstocks (wheat straw, poplar wood, and spruce wood) that were slowly pyrolyzed at 400, 460, and 525°C for 5 h (straw) and 10 h (woodchips), respectively. We characterized the biochars' pH, electrical conductivity, elemental composition (by dry combustion and X-ray fluorescence), surface area (by N adsorption), water-extractable major elements, and cation exchange capacity (CEC). We further conducted differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffractometry to obtain information on the biochars' molecular characteristics and mineralogical composition. We investigated trace metal content, total polycyclic aromatic hydrocarbon (PAH) content, and PAH composition in the biochars. The highest salt (4.92 mS cm) and ash (12.7%) contents were found in straw-derived biochars. The H/C ratios of biochars with highest treatment temperature (HTT) 525°C were 0.46 to 0.40. Surface areas were low but increased (1.8-56 m g) with increasing HTT, whereas CEC decreased (162-52 mmol kg) with increasing HTT. The results of DSC and FTIR suggested a loss of labile, aliphatic compounds during pyrolysis and the formation of more recalcitrant, aromatic constituents. X-ray diffractometry patterns indicated a mineralogical restructuring of biochars with increasing HTT. Water-extractable major and trace elements varied considerably with feedstock composition, with trace elements also affected by HTT. Total PAH contents (sum of EPA 16 PAHs) were highly variable with values up to 33.7 mg kg; irrespective of feedstock type, the composition of PAHs showed increasing dominance of naphthalene with increasing HTT. The results demonstrate that biochars are highly heterogeneous materials that, depending on feedstock and HTT, may be suitable for soil application by contributing to the nutrient status and adding recalcitrant C to the soil but also potentially pose ecotoxicological challenges.

Abstract Carbon lights up : A facile chemical method yields multicolor photoluminescent carbon dots derived from polymer/silica nanocomposites, which were prepared using surfactant‐modified silica spheres as carriers and resols (phenol/formaldehyde resins) as carbon precursor (see picture). The surface‐passivated carbon dots show good biocompatibility as potential bioimaging agents offering nanometer‐scale resolution. magnified image

In response to the 2013 Update of the European Strategy for Particle Physics, the Future Circular Collider (FCC) study was launched, as an international collaboration hosted by CERN. This study covers a highest-luminosity high-energy lepton collider (FCC-ee) and an energy-frontier hadron collider (FCC-hh), which could, successively, be installed in the same 100 km tunnel. The scientific capabilities of the integrated FCC programme would serve the worldwide community throughout the 21st century. The FCC study also investigates an LHC energy upgrade, using FCC-hh technology. This document constitutes the second volume of the FCC Conceptual Design Report, devoted to the electron-positron collider FCC-ee. After summarizing the physics discovery opportunities, it presents the accelerator design, performance reach, a staged operation scenario, the underlying technologies, civil engineering, technical infrastructure, and an implementation plan. FCC-ee can be built with today's technology. Most of the FCC-ee infrastructure could be reused for FCC-hh. Combining concepts from past and present lepton colliders and adding a few novel elements, the FCC-ee design promises outstandingly high luminosity. This will make the FCC-ee a unique precision instrument to study the heaviest known particles (Z, W and H bosons and the top quark), offering great direct and indirect sensitivity to new physics.

Roots are the primary site of interaction between plants and microorganisms. To meet food demands in changing climates, improved yields and stress resistance are increasingly important, stimulating efforts to identify factors that affect plant productivity. The role of bacterial endophytes that reside inside plants remains largely unexplored, because analysis of their specific functions is impeded by difficulties in cultivating most prokaryotes. Here, we present the first metagenomic approach to analyze an endophytic bacterial community resident inside roots of rice, one of the most important staple foods. Metagenome sequences were obtained from endophyte cells extracted from roots of field-grown plants. Putative functions were deduced from protein domains or similarity analyses of protein-encoding gene fragments, and allowed insights into the capacities of endophyte cells. This allowed us to predict traits and metabolic processes important for the endophytic lifestyle, suggesting that the endorhizosphere is an exclusive microhabitat requiring numerous adaptations. Prominent features included flagella, plant-polymer-degrading enzymes, protein secretion systems, iron acquisition and storage, quorum sensing, and detoxification of reactive oxygen species. Surprisingly, endophytes might be involved in the entire nitrogen cycle, as protein domains involved in N(2)-fixation, denitrification, and nitrification were detected and selected genes expressed. Our data suggest a high potential of the endophyte community for plant-growth promotion, improvement of plant stress resistance, biocontrol against pathogens, and bioremediation, regardless of their culturability.

Crystal Plasticity (CP) modeling is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in polycrystalline aggregates. However, when considering the increasingly complex microstructural composition of modern alloys and their exposure to—often harsh—environmental conditions, the focus in materials modeling has shifted towards incorporating more constitutive and internal variable details of the process history and environmental factors into these structure–property relations. Technologically important fields of application of enhanced CP models include phase transformations, hydrogen embrittlement, irradiation damage, fracture, and recrystallization. A number of niche tools, containing multi-physics extensions of the CP method, have been developed to address such topics. Such implementations, while being very useful from a scientific standpoint, are, however, designed for specific applications and substantial efforts are required to extend them into flexible multi-purpose tools for a general end-user community. With the Düsseldorf Advanced Material Simulation Kit (DAMASK) we, therefore, undertake the effort to provide an open, flexible, and easy to use implementation to the scientific community that is highly modular and allows the use and straightforward implementation of different types of constitutive laws and numerical solvers. The internal modular structure of DAMASK follows directly from the hierarchy inherent to the employed continuum description. The highest level handles the partitioning of the prescribed field values on a material point between its underlying microstructural constituents and the subsequent homogenization of the constitutive response of each constituent. The response of each microstructural constituent is determined, at the intermediate level, from the time integration of the underlying constitutive laws for elasticity, plasticity, damage, phase transformation, and heat generation among other coupled multi-physical processes of interest. Various constitutive laws based on evolving internal state variables can be implemented to provide this response at the lowest level. DAMASK already contains various CP-based models to describe metal plasticity as well as constitutive models to incorporate additional effects such as heat production and transfer, damage evolution, and athermal transformations. Furthermore, the implementation of additional constitutive laws and homogenization schemes, as well as the integration of a wide class of suitable boundary and initial value problem solvers, is inherently considered in its modular design.

The biomimetic CMOS dynamic vision and image sensor described in this paper is based on a QVGA (304×240) array of fully autonomous pixels containing event-based change detection and pulse-width-modulation (PWM) imaging circuitry. Exposure measurements are initiated and carried out locally by the individual pixel that has detected a change of brightness in its field-of-view. Pixels do not rely on external timing signals and independently and asynchronously request access to an (asynchronous arbitrated) output channel when they have new grayscale values to communicate. Pixels that are not stimulated visually do not produce output. The visual information acquired from the scene, temporal contrast and grayscale data, are communicated in the form of asynchronous address-events (AER), with the grayscale values being encoded in inter-event intervals. The pixel-autonomous and massively parallel operation ideally results in lossless video compression through complete temporal redundancy suppression at the pixel level. Compression factors depend on scene activity and peak at ~1000 for static scenes. Due to the time-based encoding of the illumination information, very high dynamic range - intra-scene DR of 143 dB static and 125 dB at 30 fps equivalent temporal resolution - is achieved. A novel time-domain correlated double sampling (TCDS) method yields array FPN of <;0.25% rms. SNR is >56 dB (9.3 bit) for >10 Lx illuminance.

Soil structure depends on the association between mineral soil particles (sand, silt, and clay) and organic matter, in which aggregates of different size and stability are formed. Although the chemistry of organic materials, total microbial biomass, and different enzyme activities in different soil particle size fractions have been well studied, little information is available on the structure of microbial populations in microhabitats. In this study, topsoil samples of different fertilizer treatments of a long-term field experiment were analyzed. Size fractions of 200 to 63 microm (fine sand fraction), 63 to 2 microm (silt fraction), and 2 to 0.1 microm (clay fraction) were obtained by a combination of low-energy sonication, wet sieving, and repeated centrifugation. Terminal restriction fragment length polymorphism analysis and cloning and sequencing of 16S rRNA genes were used to compare bacterial community structures in different particle size fractions. The microbial community structure was significantly affected by particle size, yielding higher diversity of microbes in small size fractions than in coarse size fractions. The higher biomass previously found in silt and clay fractions could be attributed to higher diversity rather than to better colonization of particular species. Low nutrient availability, protozoan grazing, and competition with fungal organisms may have been responsible for reduced diversities in larger size fractions. Furthermore, larger particle sizes were dominated by alpha-Proteobacteria, whereas high abundance and diversity of bacteria belonging to the Holophaga/Acidobacterium division were found in smaller size fractions. Although very contrasting organic amendments (green manure, animal manure, sewage sludge, and peat) were examined, our results demonstrated that the bacterial community structure was affected to a greater extent by the particle size fraction than by the kind of fertilizer applied. Therefore, our results demonstrate specific microbe-particle associations that are affected to only a small extent by external factors.

The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.

For nearly 20 years, the Test Feeder Working Group of the Distribution System Analysis Subcommittee has been developing openly available distribution test feeders for use by researchers. The purpose of these test feeders is to provide models of distribution systems that reflect the wide diversity in design and their various analytic challenges. Because of their utility and accessibility, the test feeders have been used for a wide range of research, some of which has been outside the original scope of intended uses. This paper provides an overview of the existing distribution feeder models and clarifies the specific analytic challenges that they were originally designed to examine. Additionally, this paper will provide guidance on which feeders are best suited for various types of analysis. The purpose of this paper is to provide the original intent of the Working Group and to provide the information necessary so that researchers may make an informed decision on which of the test feeders are most appropriate for their work.

Phytoextraction makes use of trace element-accumulating plants that concentrate the pollutants in their tissues. Pollutants can be then removed by harvesting plants. The success of phytoextraction depends on trace element availability to the roots and the ability of the plant to intercept, take up, and accumulate trace elements in shoots. Current phytoextraction practises either employ hyperaccumulators or fast-growing high biomass plants; the phytoextraction process may be enhanced by soil amendments that increase trace element availability in the soil. This review will focus on the role of plant-associated bacteria to enhance trace element availability in the rhizosphere. We report on the kind of bacteria typically found in association with trace element - tolerating or - accumulating plants and discuss how they can contribute to improve trace element uptake by plants and thus the efficiency and rate of phytoextraction. This enhanced trace element uptake can be attributed to a microbial modification of the absorptive properties of the roots such as increasing the root length and surface area and numbers of root hairs, or by increasing the plant availability of trace elements in the rhizosphere and the subsequent translocation to shoots via beneficial effects on plant growth, trace element complexation and alleviation of phytotoxicity. An analysis of data from literature shows that effects of bacterial inoculation on phytoextraction efficiency are currently inconsistent. Some key processes in plant-bacteria interactions and colonization by inoculated strains still need to be unravelled more in detail to allow full-scale application of bacteria assisted phytoremediation of trace element contaminated soils.