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With economic weight shifting toward net zero, now is the time for ECAs, Exim-Banks, and PRIs to lead. Despite previous success, aligning global economic governance to climate goals requires additional activities across export finance and investment insurance institutions. The new research project initiated by Oxford University, ClimateWorks Foundation, and Mission 2020 including other practitioners and academics from institutions such as Atradius DSB, Columbia University, EDC, FMO and Offenburg University focuses on reshaping future trade and investment governance in light of climate action. The idea of a ‘Berne Union Net Zero Club’ is an important item in a potential package of reforms. This can include realigning mandates and corporate strategies, principles of intervention, as well as ECA, Exim-Bank and PRI operating models in order to accelerate net zero transformation. Full transparency regarding Berne Union members’ activities would be an excellent starting point. We invite all interested parties in the sector to come together to chart our own path to net zero
Nowadays, the wide majority of Europeans uses smartphones. However, touch displays are still not accessible by everyone. Individuals with deafblindness, for example, often face difculties in accessing vision-based touchscreens. Moreover, they typically have few fnancial resources which increases the need for customizable, low-cost assistive devices. In this work-in-progress, we present four prototypes made from low-cost, every-day materials, that make modern pattern lock mechanisms more accessible to individuals with vision impairments or even with deafblindness. Two out of four prototypes turned out to be functional tactile overlays for accessing digital 4-by-4 grids that are regularly used to encode dynamic dot patterns. In future work, we will conduct a user study investigating whether these two prototypes can make dot-based pattern lock mechanisms more accessible for individuals with visual impairments or deafblindness.
Die Erfindung betrifft eine Schaltungsanordnung (10) für ein Kraftfahrzeug, mit einer Hochvolt-Batterie (12) zum Speichern von elektrischer Energie, mit wenigstens einer elektrischen Maschine (14) zum Antreiben des Kraftfahrzeugs, mit einem Stromrichter (16), mittels welchem von der Hochvolt-Batterie (12) bereitstellbare Hochvolt-Gleichspannung in Hochvolt-Wechselspannung zum Betreiben der elektrischen Maschine (14) umwandelbar ist, und mit einem Ladeanschluss (20) zum Bereitstellen von elektrischer Energie zum Laden der Hochvolt-Batterie (12), wobei der Stromrichter (16) als ein Drei-Stufen-Stromrichter ausgebildet ist und wenigstens eine einer Phase (u) der elektrischen Maschine (14) zugeordnete Schaltereinheit (46) aufweist, welche zwei in Reihe geschaltete Schaltergruppen (52, 54) umfasst, die jeweils zwei in Reihe geschaltete IGBTs (T11, T12, T13, T14) aufweisen, wobei zwischen den IGBTs (T11, T12) einer der Schaltergruppen (52, 54) ein Anschluss (64) angeordnet ist, welcher direkt mit einer Leitung (34) des Ladeanschlusses (20) elektrisch verbunden ist.
Electrolyte-gated thin-film transistors (EGTs) with indium oxide channel, and expected lifetime of three months, enable low-voltage operation (~1 V) in the field of printed electronics (PEs). The channel width of our printed EGTs is varied between 200 and 1000 μm, whereas a channel length between 10 and 100 μm is used. Due to the lack of uniform performance p-type metal oxide semiconductors, n-type EGTs and passive elements are used to design circuits. For logic gates, transistor-resistor logic has been employed so far, but depletion and enhancement-mode EGTs in a transistor-transistor logic boost the circuit performance in terms of delay and signal swing. In this article, the threshold voltage of the EGT, which determines the operation mode, is tuned through sizing of the EGTs channel geometry. The feasibility of both transistor operation modes is demonstrated for logic gates and ring oscillators. An inverter operating at a supply voltage of 1 V shows a maximum gain of 9.6 and a propagation delay time of 0.7 ms, which represents an improvement of ~ 2x for the gain and oscillation frequency, in comparison with the resistor-transistor logic design. Moreover, the power consumption is reduced by 6x.
Oxide semiconductors have the potential to increase the performance of inkjet printed microelectronic devices such as field-effect transistors (FETs), due to their high electron mobilities. Typical metal oxides are n-type semiconductors, while p-type oxides, although realizable, exhibit lower carriermobilities. Therefore, the circuit design based on oxide semiconductors is mostly in n-type logic only. Here we present an inkjet printed pn-diode based on p- and n-type oxide semiconductors.Copper oxide or nickel oxide is used as p-typesemiconductor whereas n-typesemiconductor is realized with indium oxide. Themeasurements show that the pn-diodes operate in the voltage window typical for printed electronics and the emission coefficient is 1.505 and 2.199 for the copper oxide based and nickel oxidebased pn-diode, respectively.Furthermore, a pn-diode model is developed and integrable into a circuit simulator.
Astronomical phenomena fascinate people from the very beginning of mankind up to today. In this paper the authors will present their experience with photography of astronomical events. The main focus will be on aurora borealis, comet Neowise, total lunar eclipses and how mobile devices open up new possibilities to observe the green flash. Our efforts were motivated by the great impact and high number of viewers of these events. Visitors from over a hundred countries watched our live broadcasts.
Furthermore, we report on our experiences with the photography of optical phenomena such as polar lights Fig. 1, comet Neowise with a Delta Aquariids meteor Fig. 11, and lunar eclipses Fig. 12.
The PHOTOPUR project aims to develop a photocatalytic process as a type of AOPs (Advanced Oxidation Processes) for the elimination of plant protection products (PPP) of the cleaning water used to wash sprayers. At INES a PV based energy supply for the photocatalytic cleaning system was developed within the framework of two bachelor theses and assembled as a demonstration unit. Then the system was step by step extended with further process automation features and pushed to a remote operating device. The final system is now available as a mobile unit mounted on a lab table. The latest step was the photocatalytic reactor module which completed the first PHOTOPUR prototype. The system is actually undergoing an intensive testing phase with performance checks at the consortium partners. First results give an overview about the successful operation.
In this work a method for the estimation of current slopes induced by inverters operating interior permanent magnet synchronous machines is presented. After the derivation of the estimation algorithm, the requirements for a suitable sensor setup in terms of accuracy, dynamic and electromagnetic interference are discussed. The boundary conditions for the estimation algorithm are presented with respect to application within high power traction systems. The estimation algorithm is implemented on a field programmable gateway array. This moving least-square algorithm offers the advantage that it is not dependent on vectors and therefore not every measured value has to be stored. The summation of all measured values leads to a significant reduction of the required storage units and thus decreases the hardware requirements. The algorithm is designed to be calculated within the dead time of the inverter. Appropriate countermeasures for disturbances and hardware restrictions are implemented. The results are discussed afterwards.
Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the G are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback of such models is the necessity of annotated data. In this work, we introduce an novel framework that benefits from two popular learning techniques, adversarial training and representation learning, and takes a step towards unsupervised conditional GANs. In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model. In this way, we break the traditional dependency between condition and label, substituting the latter by unsupervised features coming from the latent space. Finally, we show that this new technique is able to produce samples on demand keeping the quality of its supervised counterpart.
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.
The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator.
In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our Attribute Transfer Inpainting Generative Adversarial Network (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.
Wireless communication technologies play a major role to enable megatrends like Internet of Things (IoT) and Industry 4.0. The Narrowband Wireless WAN (NBWWAN) introduced to meet the long range and low power requirements of spatially distributed wireless communication use cases. These networks introduce additional challenges in testing because the network topology and RF characteristics become particularly complex and thus a multitude of different scenarios must be tested. This paper describes the infrastructure for automated testing of radio communication and for systematic measurements of the network performance of NBWWAN.
One of the main requirements of spatially distributed Internet of Things (IoT) solutions is to have networks with wider coverage to connect many low-power devices. Low-Power Wide-Area Networks (LPWAN) and Cellular IoT(cIOT) networks are promising candidates in this space. LPWAN approaches are based on enhanced physical layer (PHY) implementations to achieve long range such as LoRaWAN, SigFox, MIOTY. Narrowband versions of cellular network offer reduced bandwidth and, simplified node and network management mechanisms, such as Narrow Band IoT (NB-IoT) and Long-Term Evolution for Machines (LTE-M). Since the underlying use cases come with various requirements it is essential to perform a comparative analysis of competing technologies. This article provides systematic performance measurement and comparison of LPWAN and NB-IoT technologies in a unified testbed, also discusses the necessity of future fifth generation (5G) LPWAN solutions.