Global ETD Search

1	Assoziative Aktivität von Wörtern und ihr Einfluß auf die Lese- und Wiedererkennenszeit : zur Vorhersage experimenteller Zeitmeßwerte mit Hilfe eines assoziationstheoretischen Modells / Hagen, Bernd. Unknown Date (has links) Universiẗat, Diss., 1994--Paderborn.
2	Ortsaufgelöste NMR-Relaxometrie mit Hilfe des B-Gradienten eines Torusdetektors Stadler, Jörg. Unknown Date (has links) (PDF) Universiẗat, Diss., 2004--Bonn.
3	Timing and expectation of reward: a neuro-computational model of the afferents to the ventral tegmental area Vitay , Julien, Hamker, Fred H. 08 July 2014 (has links) (PDF) Neural activity in dopaminergic areas such as the ventral tegmental area is influenced by timing processes, in particular by the temporal expectation of rewards during Pavlovian conditioning. Receipt of a reward at the expected time allows to compute reward-prediction errors which can drive learning in motor or cognitive structures. Reciprocally, dopamine plays an important role in the timing of external events. Several models of the dopaminergic system exist, but the substrate of temporal learning is rather unclear. In this article, we propose a neuro-computational model of the afferent network to the ventral tegmental area, including the lateral hypothalamus, the pedunculopontine nucleus, the amygdala, the ventromedial prefrontal cortex, the ventral basal ganglia (including the nucleus accumbens and the ventral pallidum), as well as the lateral habenula and the rostromedial tegmental nucleus. Based on a plausible connectivity and realistic learning rules, this neuro-computational model reproduces several experimental observations, such as the progressive cancelation of dopaminergic bursts at reward delivery, the appearance of bursts at the onset of reward-predicting cues or the influence of reward magnitude on activity in the amygdala and ventral tegmental area. While associative learning occurs primarily in the amygdala, learning of the temporal relationship between the cue and the associated reward is implemented as a dopamine-modulated coincidence detection mechanism in the nucleus accumbens. Zeitmessung Klassische Konditionierung Basalganglien Dopamin Ventrale tegmentale Area Amygdala Technische Universität Chemnitz Publikationsfonds timing classical conditioning basal ganglia dopamine VTA amygdala Technische Universität Chemnitz Publication funds ddc:000 ddc:152 Zeitmessung Klassische Konditionierung Basalganglien Dopamin Ventrale tegmentale Area
4	Cinderella - Adaptive Online Partitioning of Irregularly Structured Data Herrmann, Kai, Voigt, Hannes, Lehner, Wolfgang 01 July 2021 (has links) In an increasing number of use cases, databases face the challenge of managing irregularly structured data. Irregularly structured data is characterized by a quickly evolving variety of entities without a common set of attributes. These entities do not show enough regularity to be captured in a traditional database schema. A common solution is to centralize the diverse entities in a universal table. Usually, this leads to a very sparse table. Although today's techniques allow efficient storage of sparse universal tables, query efficiency is still a problem. Queries that reference only a subset of attributes have to read the whole universal table including many irrelevant entities. One possible solution is to use a partitioning of the table, which allows pruning partitions of irrelevant entities before they are touched. Creating and maintaining such a partitioning manually is very laborious or even infeasible, due to the enormous complexity. Thus an autonomous solution is desirable. In this paper, we define the Online Partitioning Problem for irregularly structured data and present Cinderella. Cinderella is an autonomous online algorithm for horizontal partitioning of irregularly structured entities in universal tables. It is designed to keep its overhead low by incrementally assigning entities to partitions while they are touched anyway during modifications. The achieved partitioning allows queries that retrieve only entities with a subset of attributes easily pruning partitions of irrelevant entities. Cinderella increases the locality of queries and reduces query execution cost. info:eu-repo/classification/ddc/004 ddc:004
5	Timing and expectation of reward: a neuro-computational model of the afferents to the ventral tegmental area Vitay, Julien, Hamker, Fred H. January 2014 (has links) Neural activity in dopaminergic areas such as the ventral tegmental area is influenced by timing processes, in particular by the temporal expectation of rewards during Pavlovian conditioning. Receipt of a reward at the expected time allows to compute reward-prediction errors which can drive learning in motor or cognitive structures. Reciprocally, dopamine plays an important role in the timing of external events. Several models of the dopaminergic system exist, but the substrate of temporal learning is rather unclear. In this article, we propose a neuro-computational model of the afferent network to the ventral tegmental area, including the lateral hypothalamus, the pedunculopontine nucleus, the amygdala, the ventromedial prefrontal cortex, the ventral basal ganglia (including the nucleus accumbens and the ventral pallidum), as well as the lateral habenula and the rostromedial tegmental nucleus. Based on a plausible connectivity and realistic learning rules, this neuro-computational model reproduces several experimental observations, such as the progressive cancelation of dopaminergic bursts at reward delivery, the appearance of bursts at the onset of reward-predicting cues or the influence of reward magnitude on activity in the amygdala and ventral tegmental area. While associative learning occurs primarily in the amygdala, learning of the temporal relationship between the cue and the associated reward is implemented as a dopamine-modulated coincidence detection mechanism in the nucleus accumbens. info:eu-repo/classification/ddc/000 ddc:000 info:eu-repo/classification/ddc/152 ddc:152
6	Building a real data warehouse for market research Lehner, Wolfgang, Albrecht, J., Teschke, M., Kirsche, T. 08 April 2022 (has links) This paper reflects the results of the evaluation phase of building a data production system for the retail research division of the GfK, Europe's largest market research company. The application specific requirements like end-user needs or data volume are very different from data warehouses discussed in the literature, making it a real data warehouse. In a case study, these requirements are compared with state-of-the-art solutions offered by leading software vendors. Each of the common architectures (MOLAP, ROLAP, HOLAP) was represented by a product. The result of this comparison is that all systems have to be massively tailored to GfK's needs, especially to cope with meta data management or the maintenance of aggregations. info:eu-repo/classification/ddc/005 ddc:005
7	Building a real data warehouse for market research Lehner, Wolfgang, Albrecht, J., Teschke, M., Kirsche, T. 19 May 2022 (has links) This paper reflects the results of the evaluation phase of building a data production system for the retail research division of the GfK, Europe's largest market research company. The application specific requirements like end-user needs or data volume are very different from data warehouses discussed in the literature, making it a real data warehouse. In a case study, these requirements are compared with state-of-the-art solutions offered by leading software vendors. Each of the common architectures (MOLAP, ROLAP, HOLAP) was represented by a product. The result of this comparison is that all systems have to be massively tailored to GfK's needs, especially to cope with meta data management or the maintenance of aggregations. info:eu-repo/classification/ddc/005 ddc:005

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