The hidden costs of hidden debt

We report evidence that salience may have economically signi.cant e¤ects on homeowners.borrowing behavior, through a bias in favour of less salient but more costly loans. Survey evidence corroborates the existence of such a bias. We outline a simple model in which some consumers are biased and show that under plausible assumptions this affects prices in equilibrium. Market data support the predictions of the model.

Long term debt with hidden borrowing

We consider borrowers with the opportunity to raise funds from a competitive baking sector,that shares information about borrowers, and an alternative hidden lender. We highlight thatthe presence of the hidden lender restricts the contracts that can be obtained from the banking sector and that in equilibrium some borrowers obtain funds from both the banking sector and the (inefficient) hidden lender simultaneously. We further show that as the inefficiency of the hidden lender increases, total welfare decreases. By extending the model to examine a partially hidden lender, we further highlight the key role of information.

Self-similarity of complex networks and hidden metric spaces

We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.

Class of correlated random networks with hidden variables

We study a class of models of correlated random networks in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices. We find analytical expressions for the main topological properties of these models as a function of the distribution of hidden variables and the probability of connecting vertices. The expressions obtained are checked by means of numerical simulations in a particular example. The general model is extended to describe a practical algorithm to generate random networks with an a priori specified correlation structure. We also present an extension of the class, to map nonequilibrium growing networks to networks with hidden variables that represent the time at which each vertex was introduced in the system.

The development of a web page for lipid science and research. Main web sites of interest

En internet encontramos gran cantidad de información científico-técnica cuya validez no suele estar controlada por comités correctores. Para aprovechar estos recursos es necesario filtrar y facilitar el acceso del usuario a la información. En este artículo se expone la experiencia práctica en el desarrollo de una página WEB centrada en las actividades del grupo de investigación «Calidad Nutricional y Tecnología de los Lípidos». Los objetivos de esta página WEB fueron los siguientes: difusión de las actividades del grupo de investigación, aprovechar los recursos que ofrece internet y fomentar y facilitar su uso. Esta experiencia permitió presentar una metodología de trabajo eficaz para conseguir estos objetivos. Finalmente, se presentan un gran número de direcciones WEB agrupadas por apartados en el ámbito de los lípidos. Estas direcciones han sido rigurosamente seleccionadas, entre un gran número de referencias consultadas, siguiendo una serie de criterios que se discuten en este trabajo, para ofrecer aquellas que presentan un mayor interés práctico.

Analyzing hidden semantics in social bookmarking of open educational resources

Web 2.0 services such as social bookmarking allow users to manage and share the links they find interesting, adding their own tags for describingthem. This is especially interesting in the field of open educational resources, asdelicious is a simple way to bridge the institutional point of view (i.e. learningobject repositories) with the individual one (i.e. personal collections), thuspromoting the discovering and sharing of such resources by other users. In this paper we propose a methodology for analyzing such tags in order to discover hidden semantics (i.e. taxonomies and vocabularies) that can be used toimprove descriptions of learning objects and make learning object repositories more visible and discoverable. We propose the use of a simple statistical analysis tool such as principal component analysis to discover which tags createclusters that can be semantically interpreted. We will compare the obtained results with a collection of resources related to open educational resources, in order to better understand the real needs of people searching for open educational resources.

On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

In this paper, the theory of hidden Markov models (HMM) isapplied to the problem of blind (without training sequences) channel estimationand data detection. Within a HMM framework, the Baum–Welch(BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedureassumes the model (i.e., the channel response) to be static throughoutthe observation sequence. By means of introducing a parametric model fortime-varying channel responses, a version of the algorithm, which is moreappropriate for mobile channels [time-dependent Baum-Welch (TDBW)] isderived. Aiming to compare algorithm behavior, a set of computer simulationsfor a GSM scenario is provided. Results indicate that, in comparisonto other Baum–Welch (BW) versions of the algorithm, the TDBW approachattains a remarkable enhancement in performance. For that purpose, onlya moderate increase in computational complexity is needed.

Blind channel estimation and data detection using hidden Markov models theory

In this correspondence, we propose applying the hiddenMarkov models (HMM) theory to the problem of blind channel estimationand data detection. The Baum–Welch (BW) algorithm, which is able toestimate all the parameters of the model, is enriched by introducingsome linear constraints emerging from a linear FIR hypothesis on thechannel. Additionally, a version of the algorithm that is suitable for timevaryingchannels is also presented. Performance is analyzed in a GSMenvironment using standard test channels and is found to be close to thatobtained with a nonblind receiver.

El impresor Claudio Page durante la Guerra de Sucesión en Xàtiva y Alacant

Claudio Page tuvo un taller de imprenta en plena Guerra de Sucesión. Tras llegar a Xàtiva en 1702 e imprimir un impreso en 1702 y cinco en 1704, desapareció. El siguiente rastro con que contamos es su llegada a Alacant donde imprimió en 1713 y 1714 un total de cinco impresos. La actividad de este impresor local reflejó los intereses de los cabildos, eclesiástico y civil, que lograron la instalación de una imprenta (y también librería) que podía abastecer los amplios territorios controlados por el municipio. Era un impresor sobre el que apenas se conocían datos, y del que no se sabía cuál había sido su producción. En este trabajo se reconstruye su trayectoria y se analizan los impresos, lo que nos ha permitido detectar la continuidad del material tipográfico utilizado en Xàtiva y Alacant. Los impresos indican unos usos ligados a los intereses más directos de las comunidades locales: por una parte la comunicación de las actividades de la Corona, la gestión administrativa de los cabildos, tanto civil como eclesiástico, pero además contribuyó a promover las actividades de las órdenes y los particulares que contaron con un medio muy útil para promocionar sus causas judiciales.

Generalization transitions in Hidden-Layer neural networks for third-order feature discrimination

Stochastic learning processes for a specific feature detector are studied. This technique is applied to nonsmooth multilayer neural networks requested to perform a discrimination task of order 3 based on the ssT-block¿ssC-block problem. Our system proves to be capable of achieving perfect generalization, after presenting finite numbers of examples, by undergoing a phase transition. The corresponding annealed theory, which involves the Ising model under external field, shows good agreement with Monte Carlo simulations.