Perspectives on Bayesian Optimization for HCI

In this position paper we discuss optimization in the HCI domain based on our experiences with Bayesian methods for modeling and optimization of audio systems, including challenges related to evaluating, designing, and optimizing such interfaces. We outline and demonstrate how a combined Bayesian modeling and optimization approach provides a flexible framework for integrating various user and content attributes, while also supporting model-based optimization of HCI systems. Finally, we discuss current and future research direction and applications, such as inferring user needs and optimizing interfaces for computer assisted teaching

Perspectives on Bayesian Optimization for HCI

In this position paper we discuss optimization in the HCI domain based on our experiences with Bayesian methods for modeling and optimization of audio systems, including challenges related to evaluating, designing, and optimizing such interfaces. We outline and demonstrate how a combined Bayesian modeling and optimization approach provides a flexible framework for integrating various user and content attributes, while also supporting model-based optimization of HCI systems. Finally, we discuss current and future research direction and applications, such as inferring user needs and optimizing interfaces for computer assisted teaching

Design and Evaluation of 16S rRNA-Targeted Peptide Nucleic Acid Probes for Whole-Cell Detection of Members of the Genus Listeria

Six fluorescein-labeled peptide nucleic acid oligomers targeting Listeria-specific sequences on the 16S ribosomal subunit were evaluated for their abilities to hybridize to whole cells by fluorescence in situ hybridization (FISH). Four of these probes yielded weak or no fluorescent signals after hybridization and were not investigated further. The remaining two FISH-compatible probes, LisUn-3 and LisUn-11, were evaluated for their reactivities against 22 Listeria strains and 17 other bacterial strains belonging to 10 closely related genera. Hybridization with BacUni-1, a domain-specific eubacterial probe, was used as a positive control for target accessibility in both Listeria spp. and nontarget cells. RNase T1 treatment of select cell types was used to confirm that positive fluorescence responses were rRNA dependent and to examine the extent of nonspecific staining of nontarget cells. Both LisUn-3 and LisUn-11 yielded rapid, bright, and genus-specific hybridizations at probe concentrations of approximately 100 pmol ml−1. LisUn-11 was the brightest probe and stained all six Listeria species. LisUn-3 hybridized with all Listeria spp. except for L. grayi, for which it had two mismatched bases. A simple ethanolic fixation yielded superior results with Listeria spp. compared to fixation in 10% buffered formalin and was applicable to all cell types studied. This study highlights the advantages of peptide nucleic acid probes for FISH-based detection of gram-positive bacteria and provides new tools for the rapid detection of Listeria spp. These probes may be useful for the routine monitoring of food production environments in support of efforts to control L. monocytogenes.This article is published as B.F. Brehm-Stecher, Hyldig-Nielsen, J.J., and E.A. Johnson. Design and evaluation of 16S rRNA-targeted peptide nucleic acid probes for whole cell detection of the genus Listeria. Appl. Environ. Microbiol. 71: 5451-5457 (2005). Doi: 10.1128/AEM.71.9.5451-5457.2005. Posted with permission.</p

Fastaffinitaeten endlicher Gruppen

SIGLECopy held by FIZ Karlsruhe; available from UB/TIB Hannover / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Modeling expressed emotions in music using pairwise comparisons

We introduce a two-alternative forced-choice experimental paradigm to quantify expressed emotions in music using the two wellknown arousal and valence (AV) dimensions. In order to produce AV scores from the pairwise comparisons and to visualize the locations of excerpts in the AV space, we introduce a flexible Gaussian process (GP) framework which learns from the pairwise comparisons directly. A novel dataset is used to evaluate the proposed framework and learning curves show that the proposed framework needs relative few comparisons in order to achieve satisfactory performance. This is further supported by visualizing the learned locations of excerpts in the AV space. Finally, by examining the predictive performance of the user-specific models we show the importance of modeling subjects individually due to significant subjective differences

Hearing Aid Personalization

Modern digital hearing aids require and offer a great level of personalization. Today, this personalization is not performed based directly on what the user actually perceives, but on a hearing-care professional’s interpretation of what the user explains about what is perceived. In this paper, an interactive personalization system based on Gaussian process regression and active learning is proposed, which personalize the hearing aids based directly on what the user perceives. Preliminary results demonstrate a significant difference between a truly personalized setting obtained with the proposed system and a setting obtained by the current practice