Abstract
While fluoroscopy is still the most widely used imaging modality to guide cardiac interventions, the fusion of pre-operative Magnetic Resonance Imaging (MRI) with real-time intra-operative ultrasound (US) is rapidly gaining clinical acceptance as a viable, radiation-free alternative. In order to improve the detection of the left ventricular (LV) surface in 4D ultrasound, we propose to take advantage of the pre-operative MRI scans to extract a realistic geometrical model representing the patients cardiac anatomy. This could serve as prior information in the interventional setting, allowing to increase the accuracy of the anatomy extraction step in US data. We have made use of a real-time 3D segmentation framework used in the recent past to solve the LV segmentation problem in MR and US data independently and we take advantage of this common link to introduce the prior information as a soft penalty term in the ultrasound segmentation algorithm. We tested the proposed algorithm in a clinical dataset of 38 patients undergoing both MR and US scans. The introduction of the personalized shape prior improves the accuracy and robustness of the LV segmentation, as supported by the error reduction when compared to core lab manual segmentation of the same US sequences.

Abstract
In this article we survey the historical background and development of information and data visualization, and an overview of the intersection of data visualization with storytelling applied to the field of data journalism, where it finds its most widespread use in narrative visualizations. We start by explaining why the mere act of visualization can be highly useful to readers, helping them discover patterns and comprehend information. Backed by historical references, we will describe how some of the first data visualizations were used to explain facts, understand certain events, and determine courses of action. We will then outline how storytelling and narrative techniques are being currently used with data visualization to leverage the power of visual expression. Our goal is to characterize storytelling with data as a vibrant and interesting field that current journalism practices employ to help readers understand and form opinions on complex facts. By presenting concepts like storytelling with data and data stories, we aim to spark interest in further research in the applications of data visualization and narrative.

Abstract
We describe GPU implementations of the matrix recommender algorithms CCD++ and ALS. We compare the processing time and predictive ability of the GPU implementations with existing multi- core versions of the same algorithms. Results on the GPU are better than the results of the multi- core versions (maximum speedup of 14.8).

Abstract
Global competition pressures have forced manufactures to adapt their productive capabilities. In order to satisfy the ever-changing market demands many organizations adopted flexible resources capable of executing several products with different performance criteria. The unrelated parallel-machines makespan minimization problem (Rm||Cmax) is known to be NP-hard or too complex to be solved exactly. In the heuristics used for this problem, the MCT (Minimum Completion Time), which is the base for several others, allocates tasks in a random like order to the minimum completion time machine. This paper proposes an ordered approach to the MCT heuristic. MOMCT (Modified Ordered Minimum Completion Time) will order tasks in accordance to the MS index, which represents the mean difference of the completion time on each machine and the one on the minimum completion time machine. The computational study demonstrates the improved performance of MOMCT over the MCT heuristic.

Abstract
The IEEE 802.11 standard has been evolving over the past decade, introducing a set of new mechanisms at the MAC layer to improve the Quality of Service (QoS) provided to the communication. Among such improvements, we highlight the evolution from earlier DCF and PCF, until more recent EDCA and HCCA MAC layer mechanisms. In this paper we perform a simulation assessment of these four MAC mechanisms, evaluating their ability to support real-time (RT) communication. More specifically, we assess their ability to handle RT traffic in open communication environments composed of RT and non-RT stations operating in the same frequency channel and coverage area. The target of this paper is to highlight and understand the limitations of each mechanism when supporting RT communication. We show that for most of the situations, including less demanding scenarios, these mechanisms are not adequate to support RT traffic.

Abstract
We present a new type system combining refinement types and the expressiveness of intersection type discipline. The use of such features makes it possible to derive more precise types than in the original refinement system. We have been able to prove several interesting properties for our system (including subject reduction) and developed an inference algorithm, which we proved to be sound.