Maximum-order Complexity and Correlation Measures

We estimate the maximum-order complexity of a binary sequence in terms of its correlation measures. Roughly speaking, we show that any sequence with small correlation measure up to a sufficiently large order $k$ cannot have very small maximum-order complexity

Unbiased taxonomic annotation of metagenomic samples

The classification of reads from a metagenomic sample using a reference taxonomy is usually based on first mapping the reads to the reference sequences and then classifying each read at a node under the lowest common ancestor of the candidate sequences in the reference taxonomy with the least classification error. However, this taxonomic annotation can be biased by an imbalanced taxonomy and also by the presence of multiple nodes in the taxonomy with the least classification error for a given read. In this article, we show that the Rand index is a better indicator of classification error than the often used area under thereceiver operating characteristic (ROC) curve andF-measure for both balanced and imbalanced reference taxonomies, and we also address the second source of bias by reducing the taxonomic annotation problem for a whole metagenomic sample to a set cover problem, for which a logarithmic approximation can be obtained in linear time and an exact solution can be obtained by integer linear programming. Experimental results with a proof-of-concept implementation of the set cover approach to taxonomic annotation in a next release of the TANGO software show that the set cover approach further reduces ambiguity in the taxonomic annotation obtained with TANGO without distorting the relative abundance profile of the metagenomic sample.Peer ReviewedPostprint (published version

Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

Reaching social consensus family budgets: The Spanish case

The study of family budgets has been traditionally used to analyse consumers’ behaviour and estimate cost-of-living since the end of 19th century. Generally speaking, the computation of the budgets has been based on two different methodologies, the prescriptive and the descriptive method. Both present several drawbacks like the comparison among different areas, family types and over time. This paper proposes a new methodology for reaching family budgets, namely social consensus family budgets, to overcome such problems and examine the main features of the novel approach. The suggested method uses the minimization of the differences with respect to the consumer’s preferences to obtain a solution that summarizes single behaviour into a social preference. This approach is especially conceived for preferences on possibly related-expenditure groups. In addition, several algorithms are introduced to compute the social family budgets. Finally, the contribution includes the Spanish case as an example of reaching some social consensus family budgets in order to show the operational character and intuitive interpretation of the proposal approach.Este trabajo forma parte del proyecto de investigación con financiación nacional: MEC-FEDER Grant ECO2016-77900-