Abstract
This paper proposes an ontology-based approach to support the process of visualizing urban mobility data. The approach consists of building a visualization-oriented urban mobility ontology, focused on themes such as ridership, vehicle flows and the like. Existing ontologies focus on modelling the overall structure of transportation networks, and do not address the formalization of such themes. The ontology also allows characterizing visualization techniques with human perception factors, so that they can be used to automatically infer recommended techniques for a dataset. The ultimate goal is to benefit decision makers, by providing an ontology that can assist with the process of developing semantically-rich visualizations, with increased data interoperability and knowledge extraction capabilities. We provide an example with real data of the public transportation system of the city of Porto, Portugal. The example shows the semantic characterization of a visualization technique, and how semantics can assist the task of automatically recommending visualizations. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81 +/- 0.59 and 1.98 +/- 0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic.

Abstract

Abstract
This paper aims to provide a closer look at consumers' attitude toward Facebook advertising by providing a comparison between attitude toward brand posts and ads, a topic that has been disregarded in the extant literature. It also considers the relationship with the users' ad avoidance and electronic word-of-mouth communication. An exploratory quantitative analysis was performed by means of a structured self-administered questionnaire. 385 individuals aged between 18 and 44 participated in the study. The results include evidence on respondents' more favourable attitude toward brand posts than toward Facebook ads. Moreover, ads are considered more annoying by those who spend more time on Facebook. These results help shed the light on how Facebook users handle ads and brand posts, offering some clues for a more effective social media marketing strategy. Copyright © 2017 Inderscience Enterprises Ltd.

Abstract
We present a numerical implementation of a solver for the Maxwell-Bloch equations to calculate the propagation of a light pulse in a nonlinear medium composed of an atomic gas in one, two and three dimensional systems. This implementation solves the wave equation of light using a finite difference method in the time domain scheme, while the Bloch equations for the atomic population in each point of the simulation domain are integrated using splitting methods. We present numerical simulations of atomic-gas systems and performance benchmarks.

Abstract
Performance estimation denotes a task of estimating the loss that a predictive model will incur on unseen data. These procedures are part of the pipeline in every machine learning task and are used for assessing the overall generalisation ability of models. In this paper we address the application of these methods to time series forecasting tasks. For independent and identically distributed data the most common approach is cross-validation. However, the dependency among observations in time series raises some caveats about the most appropriate way to estimate performance in these datasets and currently there is no settled way to do so. We compare different variants of cross-validation and different variants of out-of-sample approaches using two case studies: One with 53 real-world time series and another with three synthetic time series. Results show noticeable differences in the performance estimation methods in the two scenarios. In particular, empirical experiments suggest that cross-validation approaches can be applied to stationary synthetic time series. However, in real-world scenarios the most accurate estimates are produced by the out-of-sample methods, which preserve the temporal order of observations.