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Publications

2016

Statistically Enhanced Analogue and Mixed-Signal Design and Test

Authors
Ramos, PL; da Silva, JM; Ferreira, DR; Santos, MB;

Publication
PROCEEDINGS OF THE 2016 IEEE 21ST INTERNATIONAL MIXED-SIGNALS TEST WORKSHOP (IMSTW)

Abstract
The design, manufacture and operational characteristics (e.g., yield, performance, and reliability) of modern electronic integrated systems exhibit extreme levels of complexity that cannot be easily modelled or predicted. Different mathematical methodologies have been explored to address this issue. Monte Carlo simulation is the most widely employed and straightforward approach to evaluate the circuits' performance statistics. However, the high number of trial cases and the long simulations times required to obtain results for complex circuits with a ppm resolution, lead to very long analysis times. The present work addresses the evaluation of alternative statistical inference methodologies which allow obtaining similar results departing from a smaller dimension data set of Monte Carlo simulations from which the overall population is estimated. These methodologies include the use of Bayesian inference, Expectation-inimization, and Kolmogorov-Smirnov tests. Results are presented which show the validity of these approaches.

2016

SimTensor: A synthetic tensor data generator

Authors
T, HF; Gama, J;

Publication
CoRR

Abstract

2016

GEDAE-LaB: A free software to calculate the energy system contributions during exercise

Authors
Bertuzzi R.; Melegati J.; Bueno S.; Ghiarone T.; Pasqua L.A.; Gáspari A.F.; Lima-Silva A.E.; Goldman A.;

Publication
Plos One

Abstract
Purpose: The aim of the current study is to describe the functionality of free software developed for energy system contributions and energy expenditure calculation during exercise, namely GEDAE-LaB. Methods: Eleven participants performed the following tests: 1) a maximal cycling incremental test to measure the ventilatory threshold and maximal oxygen uptake (VO2max); 2) a cycling workload constant test at moderate domain (90% ventilatory threshold); 3) a cycling workload constant test at severe domain (110%VO2max). Oxygen uptake and plasma lactate were measured during the tests. The contributions of the aerobic (AMET), anaerobic lactic (LAMET), and anaerobic alactic (ALMET) systems were calculated based on the oxygen uptake during exercise, the oxygen energy equivalents provided by lactate accumulation, and the fast component of excess post-exercise oxygen consumption, respectively. In order to assess the intra-investigator variation, four different investigators performed the analyses independently using GEDAE-LaB. A direct comparison with commercial software was also provided. Results: All subjects completed 10 min of exercise at moderate domain, while the time to exhaustion at severe domain was 144 ± 65 s. The AMET, LAMET, and ALMET contributions during moderate domain were about 93, 2, and 5%, respectively. The AMET, LAMET, and ALMET contributions during severe domain were about 66, 21, and 13%, respectively. No statistical differences were found between the energy system contributions and energy expenditure obtained by GEDAE-LaB and commercial software for both moderate and severe domains (P > 0.05). The ICC revealed that these estimates were highly reliable among the four investigators for both moderate and severe domains (all ICC = 0.94). Conclusion: These findings suggest that GEDAE-LaB is a free software easily comprehended by users minimally familiarized with adopted procedures for calculations of energetic profile using oxygen uptake and lactate accumulation during exercise. By providing availability of the software and its source code we hope to facilitate future related research.

2016

Learning from the News: Predicting Entity Popularity on Twitter

Authors
Saleiro, P; Soares, C;

Publication
ADVANCES IN INTELLIGENT DATA ANALYSIS XV

Abstract
In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learning approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high or low in the following hours. We run several experiments on six different entities in a dataset of over 150M tweets and 5M news and obtained F1 scores over 0.70. Error analysis indicates that news perform better on predicting entity popularity on Twitter when they are the primary information source of the event, in opposition to events such as live TV broadcasts, political debates or football matches.

2016

Preface: DLMIA 2016

Authors
Carneiro, G; Tavares, JMRS; Bradley, A; Papa, JP; Nascimento, JC; Cardoso, JS; Belagiannis, V; Lu, Z;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2016

Ontologies and procedural modelling

Authors
Adão, T; Magalhães, L; Peres, E;

Publication
SpringerBriefs in Computer Science

Abstract
This chapter consists of a literature review regarding the use of ontologies on virtual environments and the procedural modelling solutions that have been proposed with focus in two approaches: (1) the production of virtual hollow buildings, uniquely composed by outer facades; and (2) the production of virtual traversable buildings, with interior divisions included. The integration of ontologies and semantics in procedural modelling is also addressed in each one of the referred approaches. © The Author(s) 2016.

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