Practical applications of the likelihood, time transcendence and metadata in virtual models of heritage elements

Contributed to: III Bienal de Restauración Monumental: "Sobre la des-restauración" (Sevilla, Spain, Nov 23-25, 2006)

Failure detectors and communication efficiency in the crash and general omisión failure models

122 p.

Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals.

Problems when generating virtual models representing real objects: Hondarribia walls

[EN] This paper is based in the following project:

Linking Reduced Deforestation and a Global Carbon Market: Impacts on Costs, Financial Flows, and Technological Innovation

25 p.

Análisis del riesgo de contraparte a través del modelo estructural de Merton en el marco de Solvencia II

[ES] La entrada en vigor del proyecto de Solvencia II transformará por completo el sistema de determinación de las necesidades de capital del sector asegurador europeo. Recientemente se ha presentado el último estudio de impacto cuantitativo (QIS5), donde se establece la forma de cálculo del modelo estándar para la determinación de los requerimientos de capital. Este trabajo trata de analizar la adecuación de la calibración del riesgo de crédito de la contraparte mediante los modelos que se proponen en los últimos informes de impacto cuantitativo (cuarto y quinto). Para ello comparamos las necesidades de capital que se obtienen por ambas alternativas, frente a las que resultarían de aplicar un modelo de simulación basado en el enfoque estructural. Los resultados obtenidos muestran que el uso de probabilidades basadas en la metodología de Merton frente a aquellas basadas en ratings, dan lugar a requerimientos de capital sustancialmente mayores. Además, el modelo propuesto en QIS4 basado en la distribución de Vasicek no es adecuado cuando el número de contrapartes es reducido, situación habitual en el sector asegurador europeo. Por otra parte, la nueva propuesta (QIS5 o modelo de Ter Berg) es más versátil y adecuada que su antecesora pero requiere analizar con más detenimiento las hipótesis de calibración para de este modo aproximar mejor las estimaciones al riesgo realmente asumido.

Digital Quantum Rabi and Dicke Models in Superconducting Circuits

We propose the analog-digital quantum simulation of the quantum Rabi and Dicke models using circuit quantum electrodynamics (QED). We find that all physical regimes, in particular those which are impossible to realize in typical cavity QED setups, can be simulated via unitary decomposition into digital steps. Furthermore, we show the emergence of the Dirac equation dynamics from the quantum Rabi model when the mode frequency vanishes. Finally, we analyze the feasibility of this proposal under realistic superconducting circuit scenarios.

Usage of substances and their dosage regimes in pharmacological animal models of schizophrenia

Background: Contrary to what is generally thought schizophrenia is a very common mental health issue. For this, several animal models are used to assess the illness in order to develop a definitive. The most widely spread paradigm is the use of pharmacological models. Aim: The aim of this review is to display which are the most used insults for the assessment of social behaviour related negative symptoms in animal models as well as to ascertain which is the most adequate regime. Design: Literature review. Methods: PubMed database was used for this article by the search of the indexed “schizophrenia”, “animal models”, “social behaviour” and “negative symptoms” descriptors. With the exception of a single article due to it value this review is based on articles from 10 years onwards. Besides, only clinical trials and reviews written in English or Spanish and that had laboratory rodents as target population were accepted. Results: The studies assessed agree that pharmacological models (specially those regarding the NMDA receptor antagonists) are a valuable means for the experimental investigation of negative symptoms in schizophrenia with the necessity to emphasise that only some negative symptoms (anhedonia and social interaction, mainly) can be experimentally assessed. Conclusions: There is not enough evidence regarding the fours aspects of this review. PCP, Ketamine or MK-801 in sub-acute dosage regimes are currently the most indicated insults to mimic schizophrenic symptoms in rodents, although further research in needed, albeit other substances are valuable as well. (In English language exclusively)

Differential Neuroprotective Effects of 5 '-Deoxy-5 '-Methylthioadenosine

Background: 5'-deoxy-5'-methylthioadenosine (MTA) is an endogenous compound produced through the metabolism of polyamines. The therapeutic potential of MTA has been assayed mainly in liver diseases and, more recently, in animal models of multiple sclerosis. The aim of this study was to determine the neuroprotective effect of this molecule in vitro and to assess whether MTA can cross the blood brain barrier (BBB) in order to also analyze its potential neuroprotective efficacy in vivo. Methods: Neuroprotection was assessed in vitro using models of excitotoxicity in primary neurons, mixed astrocyte-neuron and primary oligodendrocyte cultures. The capacity of MTA to cross the BBB was measured in an artificial membrane assay and using an in vitro cell model. Finally, in vivo tests were performed in models of hypoxic brain damage, Parkinson's disease and epilepsy. Results: MTA displays a wide array of neuroprotective activities against different insults in vitro. While the data from the two complementary approaches adopted indicate that MTA is likely to cross the BBB, the in vivo data showed that MTA may provide therapeutic benefits in specific circumstances. Whereas MTA reduced the neuronal cell death in pilocarpine-induced status epilepticus and the size of the lesion in global but not focal ischemic brain damage, it was ineffective in preserving dopaminergic neurons of the substantia nigra in the 1-methyl-4-phenyl-1,2,3,6-tetrahydro-pyridine (MPTP)-mice model. However, in this model of Parkinson's disease the combined administration of MTA and an A(2A) adenosine receptor antagonist did produce significant neuroprotection in this brain region. Conclusion: MTA may potentially offer therapeutic neuroprotection.

Robust Sliding Control of SEIR Epidemic Models

This paper is aimed at designing a robust vaccination strategy capable of eradicating an infectious disease from a population regardless of the potential uncertainty in the parameters defining the disease. For this purpose, a control theoretic approach based on a sliding-mode control law is used. Initially, the controller is designed assuming certain knowledge of an upper-bound of the uncertainty signal. Afterwards, this condition is removed while an adaptive sliding control system is designed. The closed-loop properties are proved mathematically in the nonadaptive and adaptive cases. Furthermore, the usual sign function appearing in the sliding-mode control is substituted by the saturation function in order to prevent chattering. In addition, the properties achieved by the closed-loop system under this variation are also stated and proved analytically. The closed-loop system is able to attain the control objective regardless of the parametric uncertainties of the model and the lack of a priori knowledge on the system.

Calibrating Analytical Models for Semilocal Strings

In this work we calibrate two different analytic models of semilocal strings by constraining the values of their free parameters. In order to do so, we use data obtained from the largest and most accurate field theory simulations of semilocal strings to date, and compare several key properties with the predictions of the models. As this is still work in progress, we present some preliminary results together with descriptions of the methodology we are using in the characterisation of semilocal string networks.