Observatorio de los informativos de televisión. Análisis de contenido en la nube. Aprendizaje colaborativo e interactivo. Memoria 2015

El Observatorio de la Calidad de la Información en Televisión, de la Facultad de Ciencias de la Información de la Universidad Complutense de Madrid, es un proyecto de innovación y mejora de la calidad docente con el que se pretende establecer vínculos entre investigación y docencia y entre teoría y práctica en el marco de la enseñanza de la asignatura Información en Televisión. A través de una plataforma online de trabajo colaborativo e interactivo creada ad hoc, los alumnos analizan los contenidos de los noticieros de las cinco cadenas españolas nacionales de televisión. En el desarrollo de este proyecto, el alumno adquiere, entre otros conocimientos y destrezas, competencias académicas y profesionales sobre la materia de información en televisión. Los alumnos participantes reconocen como muy positiva y satisfactoria esta experiencia de enseñanza y aprendizaje.

Aportaciones de las universidades españolas a la investigación sobre menores y medios de comunicación

El interés académico español por la relación de los niños y jóvenes con los medios de comunicación se ha incrementado a lo largo de estos últimos años hasta tal punto que constituye una línea de investigación destacada en muchas universidades. Asistimos a una proliferación de grupos de investigación, observatorios, laboratorios, institutos… a través de los cuales se logran dar pasos importantes. De estos trabajos de investigación se extraen frutos que aumentan exponencialmente cuando esos estudios se llevan a cabo en redes que permiten poner en contacto a centros hasta hace poco desconectados entre sí, de modo que puedan compartir información.Si partimos de este punto fuerte que supone la interacción entre los distintos grupos, este trabajo trata de ofrecer un mapa del estado actual de la investigación que se desarrolla desde las universidades españolas sobre menores y medios de comunicación. Pretendemos mostrar las principales líneas de actuación impulsadas desde los centros educativos, algunas de las conclusiones obtenidas a raíz de estos trabajos, así como las tendencias y perspectivas futuras de este interesante y, a la vez, ambiciosos campo de estudio. Por otro lado, el déficit mayor que encontramos es que nuestras investigaciones adolecen de escaso componente internacional. No tanto porque no se aborde la situación de otros países, sino más bien porque deberían ser más numerosos los investigadores que formen parte de consorcios que, además, permitan la solvencia suficiente como para establecer comparativas y hojas de ruta para los medios y normativas aplicables por instituciones supranacionales como la Unión Europea.

The PG500 series : novel heparan sulfate mimetics as potent angiogenesis and heparanase inhibitors for cancer therapy

Heparan sulfate mimetics, which we have called the PG500 series, have been developed to target the inhibition of both angiogenesis and heparanase activity. This series extends the technology underpinning PI-88, a mixture of highly sulfated oligosaccharides which reached Phase III clinical development for hepatocellular carcinoma. Advances in the chemistry of the PG500 series provide numerous advantages over PI-88. These new compounds are fully sulfated, single entity oligosaccharides attached to a lipophilic moiety, which have been optimized for drug development. The rational design of these compounds has led to vast improvements in potency compared to PI-88, based on in vitro angiogenesis assays and in vivo tumor models. Based on these and other data, PG545 has been selected as the lead clinical candidate for oncology and is currently undergoing formal preclinical development as a novel treatment for advanced cancer.

Time series analysis of a Web search engine transaction log

In this paper, we use time series analysis to evaluate predictive scenarios using search engine transactional logs. Our goal is to develop models for the analysis of searchers’ behaviors over time and investigate if time series analysis is a valid method for predicting relationships between searcher actions. Time series analysis is a method often used to understand the underlying characteristics of temporal data in order to make forecasts. In this study, we used a Web search engine transactional log and time series analysis to investigate users’ actions. We conducted our analysis in two phases. In the initial phase, we employed a basic analysis and found that 10% of searchers clicked on sponsored links. However, from 22:00 to 24:00, searchers almost exclusively clicked on the organic links, with almost no clicks on sponsored links. In the second and more extensive phase, we used a one-step prediction time series analysis method along with a transfer function method. The period rarely affects navigational and transactional queries, while rates for transactional queries vary during different periods. Our results show that the average length of a searcher session is approximately 2.9 interactions and that this average is consistent across time periods. Most importantly, our findings shows that searchers who submit the shortest queries (i.e., in number of terms) click on highest ranked results. We discuss implications, including predictive value, and future research.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.