Abstract
Alternative splicing is the mechanism by which different combinations of exons in the pre-mRNA give rise to distinct mature mRNAs. This process is mediated by splicing factors that bind the pre-mRNA and affect the recognition of its splicing signals. Saccharomyces species lack many of the regulatory factors present in metazoans. Accordingly, it is generally assumed that the amount of alternative splicing is limited. However, there is recent compelling evidence that yeast have functional alternative splicing, mainly in response to environmental conditions. We have previously shown that sequence and structure properties of the pre-mRNA could explain the selection of 3' splice sites (ss) in Saccharomyces cerevisiae. In this work, we extend our previous observations to build a computational classifier that explains most of the annotated 3'ss in the CDS and 5' UTR of this organism. Moreover, we show that the same rules can explain the selection of alternative 3'ss. Experimental validation of a number of predicted alternative 3'ss shows that their usage is low compared to annotated 3'ss. The majority of these alternative 3'ss introduce premature termination codons (PTCs), suggesting a role in expression regulation. Furthermore, a genome-wide analysis of the effect of temperature, followed by experimental validation, yields only a small number of changes, indicating that this type of regulation is not widespread. Our results are consistent with the presence of alternative 3'ss selection in yeast mediated by the pre-mRNA structure, which can be responsive to external cues, like temperature, and is possibly related to the control of gene expression.

Abstract
Graphs are a very important abstraction to model complex structures and respective interactions, with a broad range of applications including web analysis, telecommunications, chemical informatics and bioinformatics. In this work we are interested in the application of graph mining to identify abnormal behavior patterns from telecom Call Detail Records (CDRs). Such behaviors could also be used to model essential business tasks in telecom, for example churning, fraud, or marketing strategies, where the number of customers is typically quite large. Therefore, it is important to rank the most interesting patterns for further analysis. We propose a vertex relevant ranking score as a unified measure for focusing the search of abnormal patterns in weighted call graphs based on CDRs. Classical graph-vertex measures usually expose a quantitative perspective of vertices in telecom call graphs. We aggregate wellknown vertex measures for handling attribute-based information usually provided by CDRs. Experimental evaluation carried out with real data streams, from a local mobile telecom company, showed us the feasibility of the proposed strategy. © 2012 Springer-Verlag.

Abstract
In this paper the integer-valued autoregressive model of order one, contaminated with additive outliers is studied in some detail. Moreover, parameter estimation is also addressed. Supposing that the timepoints of the outliers are known but their sizes are unknown, we prove that the conditional least squares (CLS) estimators of the offspring and innovation means are strongly consistent. In contrast, however, the CLS estimators of the outliers' sizes are not strongly consistent, although they converge to a random limit with probability 1. We also prove that the joint CLS estimator of the offspring and innovation means is asymptotically normal. Conditionally on the values of the process at the timepoints neighboring to the outliers' occurrences, the joint CLS estimator of the sizes of the outliers is also asymptotically normal.

Abstract
In this paper we focus on the real-time frequency domain analysis of speech signals, and on the extraction of suitable and perceptually meaningful features that are related to the glottal source and that may pave the way for robust speaker identification and voice register classification. We take advantage of an analysis-synthesis framework derived from an audio coding algorithm in order to estimate and model the relative delays between the different harmonics reflecting the contribution of the glottal source and the group delay of the vocal tract filter. We show in this paper that this approach effectively captures the shape invariance of a periodic signal and may be suited to monitor and extract in real-time perceptually important features correlating well with specific voice registers or with a speaker unique sound signature. A first validation study is described that confirms the competitive performance of the proposed approach in the automatic classification of the breathy, normal and pressed voice phonation types.

Abstract
This paper describes an algorithm which enables harmonic and noise splitting of the glottal excitation of voiced speech. The algorithm utilizes a straightforward harmonic and noise splitter which is utilized prior to glottal inverse filtering. The results show improved estimates of the glottal excitation in comparison to a known inverse filtering method.

Abstract
In this paper we introduce new phase-related features denoting the delay between the harmonics and the fundamental frequency of a periodic signal, notably of voiced singing. These features are identified as Normalized Relative Delay (NRD) and denote the phase contribution to the shape invariance of a periodic signal. Thus, NRDs are amenable to a physical and psychophysical interpretation and are structurally independent of the overall time shift of the signal, an important property that is shared with the magnitude spectrum in the case of a locally stationary signal. We describe the NRD and report on preliminary studies testing the discrimination capability of NRDs applied to singing signals.