Number of prior episodes and the presence of depressive symptoms are associated with longer length of stay for patients with acute manic episodes

Background: Few studies have analyzed predictors of length of stay (LOS) in patients admitted due to acute bipolar manic episodes. The purpose of the present study was to estimate LOS and to determine the potential sociodemographic and clinical risk factors associated with a longer hospitalization. Such information could be useful to identify those patients at high risk for long LOS and to allocate them to special treatments, with the aim of optimizing their hospital management. Methods: This was a cross-sectional study recruiting adult patients with a diagnosis of bipolar disorder (Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision (DSM-IV-TR) criteria) who had been hospitalized due to an acute manic episode with a Young Mania Rating Scale total score greater than 20. Bivariate correlational and multiple linear regression analyses were performed to identify independent predictors of LOS. Results: A total of 235 patients from 44 centers were included in the study. The only factors that were significantly associated to LOS in the regression model were the number of previous episodes and the Montgomery-Åsberg Depression Rating Scale (MADRS) total score at admission (P < 0.05). Conclusions: Patients with a high number of previous episodes and those with depressive symptoms during mania are more likely to stay longer in hospital. Patients with severe depressive symptoms may have a more severe or treatment-resistant course of the acute bipolar manic episode.

Ontologies for representation of folk song metadata

The digital management of collections in museums, archives, libraries and galleries is an increasingly important part of cultural heritage studies. This paper describes a representation for folk song metadata, based on the Web Ontology Language (OWL) implementation of the CIDOC Conceptual Reference Model. The OWL representation facilitates encoding and reasoning over a genre ontology, while the CIDOC model enables a representation of complex spatial containment and proximity relations among geographic regions. It is shown how complex queries of folk song metadata, relying on inference and not only retrieval, can be expressed in OWL and solved using a description logic reasoner.

Oracles for audio chord estimation

This paper explores how audio chord estimation could improve if information about chord boundaries or beat onsets is revealed by an oracle. Chord estimation at the frame level is compared with three simulations, each using an oracle of increasing powers. The beat and chord segments revealed by an oracle are used to compute a chord ranking at the segment level, and to compute the cumulative probability of finding the correct chord among the top ranked chords. Oracle results on two different audio datasets demonstrate the substantial potential of segment versus frame approaches for chord audio estimation. This paper also provides a comparison of the oracle results on the Beatles dataset, the standard dataset in this area, with the new Billboard Hot 100 chord dataset.

Estudio bibliográfico de sistemas educativos en el ámbito de la inteligencia artificial

Este informe trata de reunir en un documento la revisión bibliográfica realizada como base necesaria para el desarrollo de una tesis doctoral sobre las interacciones entre profesores y alumnos en las sesiones tradicionales de aprendizaje. Nuestra investigación se enmarca en los trabajos del grupo GALAN de la Facultad de Informática de San Sebastián de la UPV-EHU, que se dedica desde sus comienzos al desarrollo de herramientas educativas flexibles dotadas de comportamiento inteligente. En este informe presentamos un estudio bibliográfico de sistemas educativos en el ámbito de la inteligencia artificial. En particular se centra en los siguientes aspectos: tutores inteligentes, sistemas educativos para la web y herramientas de ayuda al profesor. Además se incluye una revisión del modelado de usuario y un estudio de técnicas para el análisis de datos.

Using network mesures to test evolved NK-landscapes

In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances that maximize the estimation of distribution algorithm complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.

Aprendizaje basado en proyectos aplicado a la asignatura Ingeniería del software : guía del docente y guía del estudiante

Este informe recoge las guías del docente y del estudiante para la puesta en marcha, seguimiento continuo y evaluación de la asignatura Ingeniería del Software del segundo curso del Grado en Ingeniería Informática. Todo ello basado en metodologías activas, concretamente la metodología de Aprendizaje Basado en Proyectos (ABP, o PBL de Project Based Learning). El trabajo publicado en este informe es el resultado obtenido por los autores dentro del programa de formación del profesorado en metodologías activas (ERAGIN), auspiciado por el Vicerrectorado de Calidad e Innovación Docente de la Universidad del País Vasco (UPV/EHU).

Music-Theoretic Estimation of Chords and Keys from Audio

This paper proposes a new method for local key and chord estimation from audio signals. This method relies primarily on principles from music theory, and does not require any training on a corpus of labelled audio files. A harmonic content of the musical piece is first extracted by computing a set of chroma vectors. A set of chord/key pairs is selected for every frame by correlation with fixed chord and key templates. An acyclic harmonic graph is constructed with these pairs as vertices, using a musical distance to weigh its edges. Finally, the sequences of chords and keys are obtained by finding the best path in the graph using dynamic programming. The proposed method allows a mutual chord and key estimation. It is evaluated on a corpus composed of Beatles songs for both the local key estimation and chord recognition tasks, as well as a larger corpus composed of songs taken from the Billboard dataset.

Learning from human teaching strategies

Recent works in the area of adaptive education systems point out the importance of aumenting the student model to improve the personalization and adaptation to the learner by means of several aspects such as emotions, user locations or interactions. Until now the study of interactions has been mainly focused on the student-learning system flow, despite the fact that the most successful and used way of teaching are the traditional face-to-face interactions. In this project, we explore the use of interactions among teachers and students, as they occur in traditional education, to enrich the current student models, with the aim of providing them with useful information about new characteristics for improving the learning process. At a first step, in this paper we present the formal process carried out to obtain information about teachers’ expertise and necessities regarding the direct interactions with students.

Obtaining teaching expertise from F2F learning interactions

Learning environments are commonly used nowadays, but they exclude face-to-face interaction among teachers and students what is a successful basis of traditional education. On the other hand, in many cases teachers are imposed to use technology, what they do in an intuitive way. That is, teachers “learn by doing” and do not fully exploit its potential benefits. Consequently, some questions arise: How do teachers use F2F interaction to guide learning session? How can technology help teachers and students in their day by day? Moreover, are teachers and students really opened to be helped by technology? In this paper we present the formal process carried out to obtain information about teachers’ expertise and necessities regarding the direct interactions with students. We expose the possibilities to cover those necessities and the willingness that teachers show to be helped.

New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs’ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions.

Communities in complex networks

The study of complex networks has attracted the attention of the scientific community for many obvious reasons. A vast number of systems, from the brain to ecosystems, power grid, and the Internet, can be represented as large complex networks, i.e, assemblies of many interacting components with nontrivial topological properties. The link between these components can describe a global behaviour such as the Internet traffic, electricity supply service, market trend, etc. One of the most relevant topological feature of graphs representing these complex systems is community structure which aims to identify the modules and, possibly, their hierarchical organization, by only using the information encoded in the graph topology. Deciphering network community structure is not only important in order to characterize the graph topologically, but gives some information both on the formation of the network and on its functionality.