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The Global Sanctions Data Base (GSDB): an update that includes the years of the Trump presidency
(2021)
This paper presents the current state of development and selected technological challenges in the application of ecologically and economically sustainable nets for aquaculture based on ongoing development projects. These aim at the development of a new material system of high-strength stainless steel wires as net material with environmentally compatible antifouling properties for nearshore and offshore aquacultures. Current plastic netting materials will be replaced with high-strength stainless steel to provide a more environmentally friendly system that can withstand more severe mechanical stresses (waves, storms, tides and predators). A new antifouling strategy is expected to solve current challenges, such as ecological damage (e.g., due to pollution from copper-containing antifouling substances or microplastics), high maintenance costs (e.g., cleaning and repairs), and shorter service life. Approaches for the next development steps are presented based on previous experience as well as calculation models based on this experience.
Cultural Mapping 4.0
(2021)
Cultural mapping aims to capture and visualize tangible and intangible cultural assets. This extend abstract proposes the consequent extension of analogue forms of cultural mapping using digital technologies, and its contribution is two-fold. First, the necessary theoretical basis is provided by a literature review of the still-young field of cultural mapping and the complementary disciplines of participatory mapping and digital story-mapping. Second, we propose a digitally enhanced Cultural Mapping 4.0 vision based on a case study from an ongoing research project in the Lake Constance region. Digital participatory mapping approaches are applied to capture data, and to validate and disseminate the results, story-mapping - a spatial form of digital storytelling - is used.
This paper examines the interdependencies of tourism, Buddhism and sustainability combining in-depth-interviews with Buddhism experts and non-participant observation in a mixed-method approach. The area under investigation is the Alpine region of Austria, Germany and Switzerland, since it is home to Asian and Western forms of Buddhism tourism alike. Results show that Buddhism tourism as a value-based activity on the one hand is not commercial, but since demand is rising, on the other hand tendencies towards more commercial forms can be observed. As a modest form of activity Buddhism tourism does not shape the landscape of the Alpine area and by its nature it incorporates sustainability.
Beim data-driven learning (DDL) werden Lernerinnen und Lerner angeleitet, sprachliche Muster mit Hilfe von Korpuswerkzeugen zu entdecken und eigene Korpusabfragen durchzuführen. Am Beispiel einer Unterrichtseinheit für den Wirtschaftsdeutsch-Unterricht wird der Einsatz von DDL erläutert. Es wird deutlich, welche Chancen korpuslinguistische Verfahren bieten, aber auch, welche Probleme beim DDL auftreten können. Vor allem für die Planung des Fachsprachenunter-richts können korpuslinguistische Analysen hilfreich sein: Zu nennen sind die Bedarfsermittlung, die Auswahl von Materialien, die Identifizierung von typischem Wortschatz und häufigen Mustern sowie die Erstellung von Übungsmaterialien. Das Praxisbeispiel, das auf andere Kontexte übertragen werden kann, illustriert, wie sich korpuslinguistische Verfahren und DDL auf die Unterrichtsplanung und -durchführung auswirken: Sprache wird als Datenmenge betrachtet; der Fokus liegt auf sprachlichen Mustern; Fragen nach der Korrektheit bzw. der Angemessenheit werden thematisiert.
Deep transformation models
(2021)
We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks it is predominantly used to just predict a single number. This ignores the non-deterministic character of most tasks. Especially if crucial decisions are based on the predictions, like in medical applications, it is essential to quantify the prediction uncertainty. The presented deep learning transformation model estimates the whole conditional probability distribution, which is the most thorough way to capture uncertainty about the outcome. We combine ideas from a statistical transformation model (most likely transformation) with recent transformation models from deep learning (normalizing flows) to predict complex outcome distributions. The core of the method is a parameterized transformation function which can be trained with the usual maximum likelihood framework using gradient descent. The method can be combined with existing deep learning architectures. For small machine learning benchmark datasets, we report state of the art performance for most dataset and partly even outperform it. Our method works for complex input data, which we demonstrate by employing a CNN architecture on image data.
In this article, the collection of classes of matrices presented in [J. Garloff, M. Adm, ad J. Titi, A survey of classes of matrices possessing the interval property and related properties, Reliab. Comput. 22:1-14, 2016] is continued. That is, given an interval of matrices with respect to a certain partial order, it is desired to know whether a special property of the entire matrix interval can be inferred from some of its element matrices lying on the vertices of the matrix interval. The interval property of some matrix classes found in the literature is presented, and the interval property of further matrix classes including the ultrametric, the conditionally positive semidefinite, and the infinitely divisible matrices is given for the first time. For the inverse M-matrices the cardinality of the required set of vertex matrices known so far is significantly reduced.
Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows
(2021)
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause huge forecast errors with traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus enables various applications in low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein-Polynomial Normalizing Flows where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities and also outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.
The State of Custom
(2021)
In our article, we engage with the anthropologist Gerd Spittler’s pathbreaking
article “Dispute settlement in the shadow of Leviathan” (1980) in which
he strives to integrate the existence of state courts (the eponymous Leviathan’s
shadow) in (post-)colonial Africa into the analysis on non-state court legal practices.
According to Spittler, it is because of undesirable characteristics inherent
in state courts that the disputing parties tended to rather involve mediators than
pursue a state court judgment. The less people liked state courts, the more likely
they were to (re-)turn to dispute settlement procedures. Now how has this situation
changed in the last four decades since its publication date? We relate his findings
to contemporary debates in legal anthropology that investigate the relationship
between disputing, law and the state. We also show through our own work in
Africa and Asia, particularly in Southern Ethiopia and Kyrgyzstan, in what ways
Spittler’s by now classical contribution to the field of legal anthropology in 1980
can be made fruitful for a contemporary anthropology of the state at a time when
not only (legal) anthropology has changed, but especially the way states deal with
putatively “customary” forms of dispute settlement.