Improvement of short term precipitation forecasts in the Alpine Region using WRF with 3DVAR RADAR reflectivity and SYNOP assimilation

Die Prognose konvektiver Niederschlagsereignisse stellt aufgrund der Kleinskaligkeit der meteorologischen Ereignisse und der niemals optimalen Modellinitialisierung globaler Modelle noch immer ein grosses Problem in der numerischenWettervorhersage dar. Ziel dieser Arbeit ist die kurzfristige Vorhersage konvektiver Niederschlagsereignisse mittels 3DVAR Assimilation von RADAR-Reflektiviäten und Stationsdaten innerhalb eines mesoskaligen Modells (WRF -Weather and Research Forecasting) zu verbessern. Der Autor implementiert ein sog. RUC (Rapid Update Cycling) System um stündlich aktualisierte Analysen zu generieren, die als neue Startbedingungen des nächsten Vorhersagelaufes dienen. Da dem Autor kein vollständiges dreidimensionales Reflektivitätsfeld Mitteleuropas zur Verfügung steht wird ein Verfahren entwickelt, bei dem künstliche vertikale Radarprofile generiert werden, die im Anschluss mit dem vorhandenen 3D-RADAR aus Österreich und mit den aus Stationswerten generierten BOGUS RADAR-Reflektivitäten verschmolzen werden. Der Einfluss der Assimilation von RADAR-Reflektivitäten und aller verfügbarer SYNOP Stationsdaten auf die kurzfristige Prognose hängt vorwiegend von der Konfiguration und Analyse der Background Error Kovarianzen ab, die für die zeitliche und räumliche Ausbreitung der assimilierten Observationen ausschlaggebend sind. Anhand einer ausgewählten Fallstudie soll das Potenzial zur Prognose schadensträchtiger Unwettereignisse im Alpenraum aufgezeigt werden. Der Autor zeigt, dass bei entsprechender Konfiguration des Assimilations- und des Modellsetups eine signifikante Verbesserung der Kurzfristvorhersage möglich ist.The forecast of convective precipitation is still an unsolved issue of numerical weather prediction, because of the small scales of the involved meteorological processes and the never optimal model initialization of a global model. The goal of this work is to improve the short term forecast of convective precipitation by 3DVAR assimilation of radar reflectivities and station data within a mesoscale model (WRF -Weather and Research Forecasting). The author therefore implements a RUC (Rapid Update Cycling) system to generate hourly updated analysis, which are serving as new initial conditions for subsequent forecast runs. Due to the incompleteness of the available RADAR data in the research domain, the author develops a RADAR merging procedure where two dimensional RADAR data get expanded to three dimensional data by using artificial vertical profiles. Finally these data are merged with existing three dimensional radar over Austria and BOGUS radar data, which are created out of station observation precipitation. The impact of the assimilated RADAR reflectivities and all available European SYNOP stations on the analysis and the computed forecast depends critically on an optimal configuration of the background error covariances, which are the determining factor how an observation is spread in space and time in the model. A case study shows the improved capability to predict severe weather developments in the Alpine region. The author shows an improvement of the short term precipitation forecast skill scores over Austria due to a cycled 3DVAR assimiliation of radar and station data

DSG: An End-to-End Document Structure Generator

Information in industry, research, and the public sector is widely stored as rendered documents (e.g., PDF files, scans). Hence, to enable downstream tasks, systems are needed that map rendered documents onto a structured hierarchical format. However, existing systems for this task are limited by heuristics and are not end-to-end trainable. In this work, we introduce the Document Structure Generator (DSG), a novel system for document parsing that is fully end-to-end trainable. DSG combines a deep neural network for parsing (i) entities in documents (e.g., figures, text blocks, headers, etc.) and (ii) relations that capture the sequence and nested structure between entities. Unlike existing systems that rely on heuristics, our DSG is trained end-to-end, making it effective and flexible for real-world applications. We further contribute a new, large-scale dataset called E-Periodica comprising real-world magazines with complex document structures for evaluation. Our results demonstrate that our DSG outperforms commercial OCR tools and, on top of that, achieves state-of-the-art performance. To the best of our knowledge, our DSG system is the first end-to-end trainable system for hierarchical document parsing.Comment: Accepted at ICDM 202

Optimal Parameters for XMSS^MT

We introduce Multi Tree XMSS (XMSS^MT), a hash-based signature scheme that can be used to sign a virtually unlimited number of messages. It is provably forward and hence EU-CMA secure in the standard model and improves key and signature generation times compared to previous schemes. XMSS^MT has --- like all practical hash-based signature schemes --- a lot of parameters that control different trade-offs between security, runtimes and sizes. Using linear optimization, we show how to select provably optimal parameter sets for different use cases

Python FPGA Programming with Data-Centric Multi-Level Design

Although high-level synthesis (HLS) tools have significantly improved programmer productivity over hardware description languages, developing for FPGAs remains tedious and error prone. Programmers must learn and implement a large set of vendor-specific syntax, patterns, and tricks to optimize (or even successfully compile) their applications, while dealing with ever-changing toolflows from the FPGA vendors. We propose a new way to develop, optimize, and compile FPGA programs. The Data-Centric parallel programming (DaCe) framework allows applications to be defined by their dataflow and control flow through the Stateful DataFlow multiGraph (SDFG) representation, capturing the abstract program characteristics, and exposing a plethora of optimization opportunities. In this work, we show how extending SDFGs with multi-level Library Nodes incorporates both domain-specific and platform-specific optimizations into the design flow, enabling knowledge transfer across application domains and FPGA vendors. We present the HLS-based FPGA code generation backend of DaCe, and show how SDFGs are code generated for either FPGA vendor, emitting efficient HLS code that is structured and annotated to implement the desired architecture

Ordinos: A Verifiable Tally-Hiding E-Voting System

Modern electronic voting systems (e-voting systems) are designed to provide not only vote privacy but also (end-to-end) verifiability. Several verifiable e-voting systems have been proposed in the literature, with Helios being one of the most prominent ones. Almost all such systems, however, reveal not just the voting result but also the full tally, consisting of the exact number of votes per candidate or even all single votes. There are several situations where this is undesirable. For example, in elections with only a few voters (e.g., boardroom or jury votings), revealing the complete tally leads to a low privacy level, possibly deterring voters from voting for their actual preference. In other cases, revealing the complete tally might unnecessarily embarrass some candidates. Often, the voting result merely consists of a single winner or a ranking of candidates, so revealing only this information but not the complete tally is sufficient. This property is called tally-hiding and it offers completely new options for e-voting. In this paper, we propose the first provably secure end-to-end verifiable tally-hiding e-voting system, called Ordinos. We instantiated our system with suitable cryptographic primitives, including an MPC protocol for greater-than tests, implemented the system, and evaluated its performance, demonstrating its practicality. Moreover, our work provides a deeper understanding of tally-hiding in general, in particular in how far tally-hiding affects the levels of privacy and verifiability of e-voting systems

A minimal model of peptide binding predicts ensemble properties of serum antibodies

<p/> <p>Background</p> <p>The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings.</p> <p>Results</p> <p>Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones.</p> <p>Conclusions</p> <p>Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.</p

Extracellular invertase is involved in the regulation of clubroot disease in Arabidopsis thaliana

Clubroot disease of Brassicaceae is caused by an obligatebiotrophic protist,Plasmodiophora brassicae. During root galldevelopment, a strong sink for assimilates is developed. Amongother genes involved in sucrose and starch synthesis and degra-dation, the increased expression of invertases has been observedin a microarray experiment, and invertase and invertase inhibitorexpression was confirmed using promoter::GUS lines ofArabi-dopsis thaliana. A functional approach demonstrates that inver-tases are important for gall development. Different transgeniclines expressing an invertase inhibitor under the control of tworoot-specific promoters,Pyk10andCrypticT80, which results inthe reduction of invertase activity, showed clearly reduced clu-broot symptoms in root tissue with highest promoter expression,whereas hypocotyl galls developed normally. These resultspresent the first evidence that invertases are important factorsduring gall development, most probably in supplying sugars tothe pathogen. In addition, root-specific repression of invertaseactivity could be used as a tool to reduce clubroot symptoms

Weiterentwicklung des Analyseinstruments Renewbility: Renewbility II - Szenario für einen anspruchsvollen Klimaschutzbeitrag des Verkehrs

Das Projekt Renewbility II hatte zum Ziel den bestehenden Renewbility-Modellverbund weiterzuentwickeln und im Rahmen von Szenariobetrachtungen den möglichen Klimaschutzbeitrag des Verkehrssektors bis zum Jahr 2030 unter Mitwirkung unterschiedlichster gesellschaftlicher Akteure zu quantifizieren. Im Basisszenario werden bestehende Regulierungen im Verkehr berücksichtigt und bestehende Entwicklungen fortgeschrieben. Im Ergebnis können im Basisszenario bei deutlichen Effizienzsteigerungen, dem zunehmenden Einsatz alternativer Kraftstoffe im Verkehr und einer weiter zunehmenden Verkehrsnachfrage die Treibhausgasemissionen bis zum Jahr 2030 um 12 % gegenüber 2005 gesenkt werden. Im Klimaschutzszenario können mit deutlich ambitionierteren Maßnahmen die Treibhausgasemissionen im selben Zeitraum um 37 % reduziert werden bei gleichzeitiger Stärkung der deutschen Wirtschaftskraft und Stabilisierung des Staatshaushaltes. Neben einer weiteren Effizienzsteigerung und dem Einsatz von alternativen Antrieben und Kraftstoffen, trägt insbesondere die Verlagerung, aber auch die Vermeidung von Verkehren zur Emissionsminderung bei