Manifold Modeling of the Beating Heart Motion

Modeling the heart motion has important applications for diagnosis and intervention. We present a new method for modeling the deformation of the myocardium in the cardiac cycle. Our approach is based on manifold learning to build a representation of shape variation across time. We experiment with various manifold types to identify the best manifold method, and with real patient data extracted from cine MRIs. We obtain a representation, common to all subjects, that can discriminate cardiac cycle phases and heart function types

Topology of molecular machines of the endoplasmic reticulum: a compilation of proteomics and cytological data

The endoplasmic reticulum (ER) is a key organelle of the secretion pathway involved in the synthesis of both proteins and lipids destined for multiple sites within and without the cell. The ER functions to both co- and post-translationally modify newly synthesized proteins and lipids and sort them for housekeeping within the ER and for transport to their sites of function away from the ER. In addition, the ER is involved in the metabolism and degradation of specific xenobiotics and endogenous biosynthetic products. A variety of proteomics studies have been reported on different subcompartments of the ER providing an ER protein dictionary with new data being made available on many protein complexes of relevance to the biology of the ER including the ribosome, the translocon, coatomer proteins, cytoskeletal proteins, folding proteins, the antigen-processing machinery, signaling proteins and proteins involved in membrane traffic. This review examines proteomics and cytological data in support of the presence of specific molecular machines at specific sites or subcompartments of the ER

DS-KCF: a real-time tracker for RGB-D data

© 2016 The Author(s) We propose an RGB-D single-object tracker, built upon the extremely fast RGB-only KCF tracker that is able to exploit depth information to handle scale changes, occlusions, and shape changes. Despite the computational demands of the extra functionalities, we still achieve real-time performance rates of 35–43 fps in MATLAB and 187 fps in our C++ implementation. Our proposed method includes fast depth-based target object segmentation that enables, (1) efficient scale change handling within the KCF core functionality in the Fourier domain, (2) the detection of occlusions by temporal analysis of the target’s depth distribution, and (3) the estimation of a target’s change of shape through the temporal evolution of its segmented silhouette allows. Finally, we provide an in-depth analysis of the factors affecting the throughput and precision of our proposed tracker and perform extensive comparative analysis. Both the MATLAB and C++ versions of our software are available in the public domain

Genre classification using chords and stochastic language models

Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different leve! ls of the genre hierarchy for the techniques employed are presented and discussed.This work is supported by the Spanish CICyT PROSEMUS project (TIN2006-14932-C02), the research programme Consolider Ingenio 2010 (MIPRCV, CSD2007-00018) and the Pascal Network of Excellence