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"Befreiung vom Tabu"
(2007)
"Briefe wie gemahlt"
(2007)
Am 19. Dezember 1831 wurde der verdienstvolle Mikrobiologe Christian Gottfried Ehrenberg zum korrespondierenden Mitglied des Institut de France gewählt. Alexander von Humboldt, der Ehrenberg als Wissenschaftler hoch schätzte, hat sich energisch für diese Wahl eingesetzt. Ein Zeugnis seines Engagements ist ein Brief an den Mediziner Antoine Baron Portal (1742-1832), den er um seine Stimme bei der bevorstehenden Wahl bat. Die Geschichte der Wahl Ehrenbergs in Verbindung mit diesem Brief und die Gründe für Humboldts Interesse daran sollen hier kurz skizziert werden.
"Desertion in das Jenseits"
(2007)
"Die Toten reiten schnelle."
(2007)
Viele Briefe von und an Alexander von Humboldt enthalten Zitate aus klassischen Werken der Antike oder aus zeitgenössischen Gedichten, Dramen, Erzählungen. Der Aufsatz versucht anhand einiger Beispiele zu zeigen, dass es den Briefeschreibern weniger darum ging, die Empfänger mit gediegener Bildung zu beeindrucken, als darum, ihren Gedanken anschaulicher und prägnanter Gestalt zu verleihen, zumal in gebildeten Kreisen die Kenntnis der Klassiker und der zeitgenössischen Literatur sozusagen zur Grundausstattung an Bildung gehörte, mit der beinahe spielerisch umgegangen werden konnte.
Massive stars usually form groups such as OB associations. Their fast stellar winds sweep up collectively the surrounding insterstellar medium (ISM) to generate superbubbles. Observations suggest that superbubble evolution on the surrounding ISM can be very irregular. Numerical simulations considering these conditions could help to understand the evolution of these superbubbles and to clarify the dynamics of these objects as well as the difference between observed X-ray luminosities and the predicted ones by the standard model (Weaver et al. 1977).
We consider a class of ergodic Hamilton-Jacobi-Bellman (HJB) equations, related to large time asymptotics of non-smooth multiplicative functional of difusion processes. Under suitable ergodicity assumptions on the underlying difusion, we show existence of these asymptotics, and that they solve the related HJB equation in the viscosity sense.
This paper describes the proof calculus LD for clausal propositional logic, which is a linearized form of the well-known DPLL calculus extended by clause learning. It is motivated by the demand to model how current SAT solvers built on clause learning are working, while abstracting from decision heuristics and implementation details. The calculus is proved sound and terminating. Further, it is shown that both the original DPLL calculus and the conflict-directed backtracking calculus with clause learning, as it is implemented in many current SAT solvers, are complete and proof-confluent instances of the LD calculus.
This contribution presents a quantitative evaluation procedure for Information Retrieval models and the results of this procedure applied on the enhanced Topic-based Vector Space Model (eTVSM). Since the eTVSM is an ontology-based model, its effectiveness heavily depends on the quality of the underlaying ontology. Therefore the model has been tested with different ontologies to evaluate the impact of those ontologies on the effectiveness of the eTVSM. On the highest level of abstraction, the following results have been observed during our evaluation: First, the theoretically deduced statement that the eTVSM has a similar effecitivity like the classic Vector Space Model if a trivial ontology (every term is a concept and it is independet of any other concepts) is used has been approved. Second, we were able to show that the effectiveness of the eTVSM raises if an ontology is used which is only able to resolve synonyms. We were able to derive such kind of ontology automatically from the WordNet ontology. Third, we observed that more powerful ontologies automatically derived from the WordNet, dramatically dropped the effectiveness of the eTVSM model even clearly below the effectiveness level of the Vector Space Model. Fourth, we were able to show that a manually created and optimized ontology is able to raise the effectiveness of the eTVSM to a level which is clearly above the best effectiveness levels we have found in the literature for the Latent Semantic Index model with compareable document sets.
We present the results of Monte Carlo mass-loss predictions for massive stars covering a wide range of stellar parameters. We critically test our predictions against a range of observed massloss rates – in light of the recent discussions on wind clumping. We also present a model to compute the clumping-induced polarimetric variability of hot stars and we compare this with observations of Luminous Blue Variables, for which polarimetric variability is larger than for O and Wolf-Rayet stars. Luminous Blue Variables comprise an ideal testbed for studies of wind clumping and wind geometry, as well as for wind strength calculations, and we propose they may be direct supernova progenitors.