7449534
probeBase—an online resource for rRNA-targeted oligonucleotide probes: new features 2007
probeBase is a curated database of annotated rRNA-targeted oligonucleotide probes and supporting information. Rapid access to probe, microarray and reference data is achieved by powerful search tools and via different lists that are based on selected categories such as functional or taxonomic properties of the target organism(s) or the hybridization format (fluorescence in situ hybridization or microarray) in which the probes were applied. Additional information on probe coverage and specificity is available through direct submissions of probe sequences from probeBase to RDP-II and Greengenes, two major rRNA sequence databases. A freely editable user comments field for each probe entry allows any user to add, modify or remove information or to report errors in real-time. probeBase entries increased from 700 to more than 1200 during the past three years. Several options for submission of single probes or entire probe sets, even prior to publication of newly developed probes, should further contribute to keeping probeBase an up-to-date and useful resource. probeBase is freely accessible at . Email correspondence can be addressed to probebase@microbial-ecology.net.
47337297
probeBase: an online resource for rRNA-targeted oligonucleotide probes
Ribosomal RNA-(rRNA)-targeted oligonucleotide probes are widely used for culture-independent identification of microorganisms in environmental and clinical samples. ProbeBase is a comprehensive database containing more than 700 published rRNA-targeted oligonucleotide probe sequences (status August 2002) with supporting bibliographic and biological annotation that can be accessed through the internet at http://www.probebase.net. Each oligonucleotide probe entry contains information on target organisms, target molecule (small- or large-subunit rRNA) and position, G+C content, predicted melting temperature, molecular weight, necessity of competitor probes, and the reference that originally described the oligonucleotide probe, including a link to the respective abstract at PubMed. In addition, probes successfully used for fluorescence in situ hybridization (FISH) are highlighted and the recommended hybridization conditions are listed. ProbeBase also offers difference alignments for 16S rRNA-targeted probes by using the probe match tool of the ARB software and the latest small-subunit rRNA ARB database (release June 2002). The option to directly submit probe sequences to the probe match tool of the Ribosomal Database Project II (RDP-II) further allows one to extract supplementary information on probe specificities. The two main features of probeBase, 'search probeBase' and 'find probe set', help researchers to find suitable, published oligonucleotide probes for microorganisms of interest or for rRNA gene sequences submitted by the user. Furthermore, the 'search target site' option provides guidance for the development of new FISH probes.
206844132
Information based speaker verification
We discuss the conceptual and computational frameworks of information theory for decision making in speaker verification. The proposed approach departs from other conventional scoring models for speaker verification as the first approach takes into account the quantity of 'surprise' or information content. We compare the new approach with a widely used log-likelihood normalization method for speaker verification. Experimental results on a commercial speech corpus validates the theoretical foundation of the proposed method. Furthermore, we introduce the unique entropic measure of uncertainty in the verification scoring.
13925823
Context inference of users' social relationships and distributed policy management
Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the user's daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our approach uses rule-based data mining, Bayesian network inference, and user feedback to compute the probabilities of another user being in the specific social relationship with a user whose daily communication is logged by a mobile phone. In addition, a privacy mechanism is required to ensure the user's personal integrity and privacy when sharing this user's sensitive context data. Therefore, the derived social relations are used to define a user's policies for context access control, which grant the restricted context information scope depending on the user's current context. Finally, we propose a distributed architecture capable of managing this context information based upon these context access policies.
7586216
Linear Regression Models for Solvent Accessibility Prediction in Proteins
The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.
5705405
Robust face-voice based speaker identity verification using multilevel fusion
In this paper, we propose a robust multilevel fusion strategy involving cascaded multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motion-depth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3%, respectively, for biometric person authentication task.
13891540
MagicMeeting: A Collaborative Tangible Augmented Reality System
We describe an augmented reality (AR) system that allows multiple participants to interact with 2D and 3D data using tangible user interfaces. The system features face-to-face communication, collaborative viewing and manipulation of 3D models, and seamless access to 2D desktop applications within the shared 3D space. All virtual content, including 3D models and 2D desktop windows, is attached to tracked physical objects in order to leverage the efficiencies of natural two-handed manipulation. The presence of 2D desktop space within 3D facilitates data exchange between the two realms, enables control of 3D information by 2D applications, and generally increases productivity by providing access to familiar tools. We present a general concept for a collaborative tangible AR system, including a comprehensive set of interaction techniques, a distributed hardware setup, and a component-based software architecture that can be flexibly configured using XML. We show the validity of our concept with an implementation of an application scenario from the automotive industry.