@article{SchmidSchindelinCardonaetal.2010, author = {Schmid, Benjamin and Schindelin, Johannes and Cardona, Albert and Longair, Martin and Heisenberg, Martin}, title = {A high-level 3D visualization API for Java and ImageJ}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-67851}, year = {2010}, abstract = {Background: Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Results: Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Conclusions: Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de.}, subject = {Visualisierung}, language = {en} } @inproceedings{DaviesDewellHarvey2021, author = {Davies, Richard and Dewell, Nathan and Harvey, Carlo}, title = {A framework for interactive, autonomous and semantic dialogue generation in games}, series = {Proceedings of the 1st Games Technology Summit}, booktitle = {Proceedings of the 1st Games Technology Summit}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246023}, pages = {16-28}, year = {2021}, abstract = {Immersive virtual environments provide users with the opportunity to escape from the real world, but scripted dialogues can disrupt the presence within the world the user is trying to escape within. Both Non-Playable Character (NPC) to Player and NPC to NPC dialogue can be non-natural and the reliance on responding with pre-defined dialogue does not always meet the players emotional expectations or provide responses appropriate to the given context or world states. This paper investigates the application of Artificial Intelligence (AI) and Natural Language Processing to generate dynamic human-like responses within a themed virtual world. Each thematic has been analysed against humangenerated responses for the same seed and demonstrates invariance of rating across a range of model sizes, but shows an effect of theme and the size of the corpus used for fine-tuning the context for the game world.}, language = {en} } @article{BayerPruckner2023, author = {Bayer, Daniel and Pruckner, Marco}, title = {A digital twin of a local energy system based on real smart meter data}, series = {Energy Informatics}, volume = {6}, journal = {Energy Informatics}, doi = {10.1186/s42162-023-00263-6}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357456}, year = {2023}, abstract = {The steadily increasing usage of smart meters generates a valuable amount of high-resolution data about the individual energy consumption and production of local energy systems. Private households install more and more photovoltaic systems, battery storage and big consumers like heat pumps. Thus, our vision is to augment these collected smart meter time series of a complete system (e.g., a city, town or complex institutions like airports) with simulatively added previously named components. We, therefore, propose a novel digital twin of such an energy system based solely on a complete set of smart meter data including additional building data. Based on the additional geospatial data, the twin is intended to represent the addition of the abovementioned components as realistically as possible. Outputs of the twin can be used as a decision support for either system operators where to strengthen the system or for individual households where and how to install photovoltaic systems and batteries. Meanwhile, the first local energy system operators had such smart meter data of almost all residential consumers for several years. We acquire those of an exemplary operator and discuss a case study presenting some features of our digital twin and highlighting the value of the combination of smart meter and geospatial data.}, language = {en} } @article{StaigerCadotKooteretal.2012, author = {Staiger, Christine and Cadot, Sidney and Kooter, Raul and Dittrich, Marcus and M{\"u}ller, Tobias and Klau, Gunnar W. and Wessels, Lodewyk F. A.}, title = {A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer}, series = {PLoS One}, volume = {7}, journal = {PLoS One}, number = {4}, doi = {10.1371/journal.pone.0034796}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-131323}, pages = {e34796}, year = {2012}, abstract = {Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.}, language = {en} } @inproceedings{OPUS4-4233, title = {9. Fachgespr{\"a}ch Sensornetze der GI/ITG Fachgruppe Kommunikation und Verteilte Systeme}, editor = {Kolla, Reiner}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-51106}, year = {2010}, abstract = {J{\"a}hrliches Fachgespr{\"a}ch zu Sensornetzen der GI/ITG Fachgruppe Kommunikation und Verteilte Systeme, 16. - 17. September 2010, Universit{\"a}t W{\"u}rzburg}, subject = {Drahtloses Sensorsystem}, language = {mul} }