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The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to 1H‐13C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR‐based metabolomic tools. [ABSTRACT FROM AUTHOR]

Decision processes in simultaneous lineups (an array of faces in which a 'suspect' face is displayed along with foil faces) were examined using eye tracking to capture the length and number of times that individual faces were visually analysed. The similarity of the lineup target face relative to the study face was manipulated, and face dwell times on the first visit and on return visits to the individual lineup faces were measured. On first visits, positively identified faces were examined for a longer duration compared to faces that were not identified. When no face was identified from the lineup, the suspect was visited for a longer duration compared to a foil face. On return visits, incorrectly identified faces were examined for a longer duration and visited more often compared to correctly identified faces. The results indicate that lineup decisions can be predicted by face dwell time and the number of visits made to faces. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper describes an online stereo visual simultaneous localization and mapping (SLAM) algorithm developed for the Learning Applied to Ground Robotics (LAGR) program. The Gamma-SLAM algorithm uses a Rao–Blackwellized particle filter to obtain a joint posterior over poses and maps: the pose distribution is estimated using a particle filter, and each particle has its own map that is obtained through exact filtering conditioned on the particle's pose. Visual odometry is used to provide good proposal distributions for the particle filter, and maps are represented using a Cartesian grid. Unlike previous grid-based SLAM algorithms, however, the Gamma-SLAM map maintains a posterior distribution over the elevation variance in each cell. This variance grid map can capture rocks, vegetation, and other objects that are typically found in unstructured environments but are not well modeled by traditional occupancy or elevation grid maps. The algorithm runs in real time on conventional processors and has been evaluated for both qualitative and quantitative accuracy in three outdoor environments over trajectories totaling 1,600 m in length. © 2008 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Presents a study which examined a method used for the automatic adjusting of parameters in ranked-output text retrieval systems to improve retrieval performance. Information on a ranked-output text retrieval system; Demonstration of the utility of the approach by using it to estimate a similarity measure; What experimental results using several collections indicated.

Proposes an indexing algorithm, called Metric Similarity Modeling (MSM), for representing documents based explicitly on semantic relationships between documents in addition to the individual term usage in documents. MSM application on standard text databases; Retrieval of semantically related documents; Improvement of document retrieval performance.