Thermo-visual feature fusion for object tracking using multiple spatiogram trackers

In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework

On Probabilistic Applicative Bisimulation and Call-by-Value λ\lambda-Calculi (Long Version)

Probabilistic applicative bisimulation is a recently introduced coinductive methodology for program equivalence in a probabilistic, higher-order, setting. In this paper, the technique is applied to a typed, call-by-value, lambda-calculus. Surprisingly, the obtained relation coincides with context equivalence, contrary to what happens when call-by-name evaluation is considered. Even more surprisingly, full-abstraction only holds in a symmetric setting.Comment: 30 page

Cross-correlation of the 2XMMi catalogue with Data Release 7 of the Sloan Digital Sky Survey

The Survey Science Centre of the XMM-Newton satellite released the first incremental version of the 2XMM catalogue in August 2008 . With more than 220,000 X-ray sources, the 2XMMi was at that time the largest catalogue of X-ray sources ever published and thus constitutes an unprecedented resource for studying the high-energy properties of various classes of X-ray emitters such as AGN and stars. The advent of the 7th release of the Sloan Digital Sky Survey offers the opportunity to cross-match two major surveys and extend the spectral energy distribution of many 2XMMi sources towards the optical bands. We here present a cross-matching algorithm based on the classical likelihood ratio estimator. The method developed has the advantage of providing true probabilities of identifications without resorting to Monte-Carlo simulations. Over 30,000 2XMMi sources have SDSS counterparts with individual probabilities of identification higher than 90%. Using spectroscopic identifications from the SDSS DR7 catalogue supplemented by extraction from other catalogues, we build an identified sample from which the way the various classes of X-ray emitters gather in the multi dimensional parameter space can be analysed. We investigate two scientific use cases. In the first example we show how these multi-wavelength data can be used to search for new QSO2s. Although no specific range of observed properties allows us to identify Compton Thick QSO2s, we show that the prospects are much better for Compton Thin AGN2 and discuss several possible multi-parameter selection strategies. In a second example, we confirm the hardening of the mean X-ray spectrum with increasing X-ray luminosity on a sample of over 500 X-ray active stars and reveal that on average X-ray active M stars display bluer $g-r$ colour indexes than less active ones (abridged).Comment: Accepted for publication in A&A. The corresponding fits file can be downloaded from the XCat-DB home page (http://xcatdb.u-strasbg.fr/) (tools and data). The file also contains line information for all SDSS spectroscopic entries matching a 2XMM source. Results from the cross-correlation with the 2XMM DR3 are also available at the same location. 22 pages and 14 figure

Efficient error correction for next-generation sequencing of viral amplicons

<p>Abstract</p> <p>Background</p> <p>Next-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing.</p> <p>Results</p> <p>In this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones.</p> <p>Conclusions</p> <p>Both algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.</p> <p>The implementations of the algorithms and data sets used for their testing are available at: <url>http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm</url></p