Abstract
We propose a new linear regression model for interval-valued variables. The model uses quantile functions to represent the intervals, thereby considering the distributions within them. In this paper we study the special case where the Uniform distribution is assumed in each observed interval, and we analyze the extension to the Symmetric Triangular distribution. The parameters of the model are obtained solving a constrained quadratic optimization problem that uses the Mallows distance between quantile functions. As in the classical case, a goodness-of-fit measure is deduced. Two applications on up-to-date fields are presented: one predicting duration of unemployment and the other allowing forecasting burned area by forest fires.

Abstract
The Douro Valley of Portugal is a well-known wine region producing Port wine since the end of the 18th century, with quality table wines becoming increasingly important over the last 20 years. Port wine production is the most important economic sector of the region and Vintage Port is the top quality Port wine type, produced only from the best vintages. The purpose of this research was to examine how the variability of annual weather influences the quality of Vintage Port. A weather and climate data set for the period 1980-2009 and a consensus ranking that combined a collection of vintage chart scores into a ranking were used to characterize both the weather and the vintage quality. In order to more precisely model the weather influences on the quality of the vintages it was necessary to partition the growing season into smaller growth intervals in which several heat and precipitation variables were evaluated. The heat-related variables were defined according to the phenology of grapevines, using a partition of the growing season based on accumulated temperature, rather than on calendar dates. Precipitation variables were calculated using broad periods corresponding to the dormant, vegetative and maturation stages of the grapevines. A logistic regression model was used as a tool to identify the weather variables that help to explain the relationships between yearly weather characteristics and vintage quality. The results show that several weather characteristics are strongly associated with better quality vintages: growing season mean temperatures above the region's average, warm winters, cool July through veraison and cool temperatures during ripening. In summary, although the weather is not solely responsible for determining a vintage quality, it plays an important role on it; therefore, its understanding can provide invaluable management insights to growers and producers.

Abstract
AbstractWe present the most comprehensive catalogue of cancer-associated gene alterations through characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes project. Using matched whole-genome sequencing data, we attributed RNA alterations to germline and somatic DNA alterations, revealing likely genetic mechanisms. We identified 444 associations of gene expression with somatic non-coding single-nucleotide variants. We found 1,872 splicing alterations associated with somatic mutation in intronic regions, including novel exonization events associated with Alu elements. Somatic copy number alterations were the major driver of total gene and allele-specific expression (ASE) variation. Additionally, 82% of gene fusions had structural variant support, including 75 of a novel class called “bridged” fusions, in which a third genomic location bridged two different genes. Globally, we observe transcriptomic alteration signatures that differ between cancer types and have associations with DNA mutational signatures. Given this unique dataset of RNA alterations, we also identified 1,012 genes significantly altered through both DNA and RNA mechanisms. Our study represents an extensive catalog of RNA alterations and reveals new insights into the heterogeneous molecular mechanisms of cancer gene alterations.

Abstract
In an information society, people expect to find answers to their questions quickly and with little effort. Sometimes, these answers are locked within textual documents, which often require a manual analysis, after being retrieved from the web using search engines. At FEUP InfoLab, we are researching graph-based models to index combined data (text and knowledge), with the goal of improving entity-oriented search effectiveness.

Abstract
Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.

Abstract
Significant wind power ramps have a remarkable influence on the integration of wind power. Their variability and uncertainty affect to the wind power forecast increasing the error and reducing the reliability in the continued operation of the power system. Ramp events are considered the main source of forecasting error and their study is imperative for an improvement of prediction tools. In this aspect, the first steps to achieve a study of the influence are identifying, grouping and temporal characterizing of the ramp events. This paper develops a methodology for wind power ramp events recognition in order to analyze the relationship between these events and the accuracy of the wind power forecast system according with two criteria: maximum forecast deviation and mean magnitude error. The methodology is validated using real data from the highly aggregated Spanish power system and short time timescale forecasting values. © 2016 IEEE.