Leibniz University of Hanover

About: Leibniz University of Hanover is a education organization based out in Hanover, Niedersachsen, Germany. It is known for research contribution in the topics: Finite element method & Population. The organization has 14283 authors who have published 29845 publications receiving 682152 citations.

Space-borne Bose–Einstein condensation for precision interferometry

Abstract: Owing to the low-gravity conditions in space, space-borne laboratories enable experiments with extended free-fall times. Because Bose–Einstein condensates have an extremely low expansion energy, space-borne atom interferometers based on Bose–Einstein condensation have the potential to have much greater sensitivity to inertial forces than do similar ground-based interferometers. On 23 January 2017, as part of the sounding-rocket mission MAIUS-1, we created Bose–Einstein condensates in space and conducted 110 experiments central to matter-wave interferometry, including laser cooling and trapping of atoms in the presence of the large accelerations experienced during launch. Here we report on experiments conducted during the six minutes of in-space flight in which we studied the phase transition from a thermal ensemble to a Bose–Einstein condensate and the collective dynamics of the resulting condensate. Our results provide insights into conducting cold-atom experiments in space, such as precision interferometry, and pave the way to miniaturizing cold-atom and photon-based quantum information concepts for satellite-based implementation. In addition, space-borne Bose–Einstein condensation opens up the possibility of quantum gas experiments in low-gravity conditions1,2. A Bose–Einstein condensate is created in space that has sufficient stability to enable its characteristic dynamics to be studied.

Reasoning and Ontologies for Personalized E-Learning in the Semantic Web.

Abstract: The challenge of the semantic web is the provision of distributed information with well-defined meaning, understandable for different parties. Particularly, applications should be able to provide individually optimized access to information by taking the individual needs and requirements of the users into account. In this paper we propose a framework for personalized e-Learning in the semantic web and show how the semantic web resource description formats can be utilized for automatic generation of hypertext structures from distributed metadata. Ontologies and metadata for three types of resources (domain, user, and observation) are investigated. We investigate a logic-based approach to educational hypermedia using TRIPLE, a rule and query language for the semantic web.

Bioavailability of long-chain omega-3 fatty acids.

Abstract: Supplements have reached a prominent role in improving the supply of long-chain omega-3 fatty acids, such as Eicosapentaenoic acid (EPA 20:5n-3) and Docosahexaenoic acid (DHA 22:6n-3). Similar to other nutrients, the availability of omega-3 fatty acids is highly variable and determined by numerous factors. However, the question of omega-3 fatty acids bioavailability has long been disregarded, which may have contributed to the neutral or negative results concerning their effects in several studies. This review provides an overview of the influence of chemical binding form (free fatty acids bound in ethylesters, triacylglycerides or phospholipids), matrix effects (capsule ingestion with concomitant intake of food, fat content in food) or galenic form (i.e. microencapsulation, emulsification) on the bioavailability of omega-3 fatty acids. There is a need to systematically investigate the bioavailability of omega-3 fatty acids formulations, which might be a key to designing more effective studies in the future.

Beyond the Required LISA Free-Fall Performance: New LISA Pathfinder Results down to 20 μHz.

Abstract: In the months since the publication of the first results, the noise performance of LISA Pathfinder has improved because of reduced Brownian noise due to the continued decrease in pressure around the test masses, from a better correction of noninertial effects, and from a better calibration of the electrostatic force actuation. In addition, the availability of numerous long noise measurement runs, during which no perturbation is purposely applied to the test masses, has allowed the measurement of noise with good statistics down to 20 μ Hz . The Letter presents the measured differential acceleration noise figure, which is at ( 1.74 ± 0.01 ) fm s − 2 / √ Hz above 2 mHz and ( 6 ± 1 ) × 10 fm s − 2 / √ Hz at 20 μ Hz , and discusses the physical sources for the measured noise. This performance provides an experimental benchmark demonstrating the ability to realize the low-frequency science potential of the LISA mission, recently selected by the European Space Agency.

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.