Probabilistic Response of Multi-support Structures on Non-uniform Soil Conditions

Conventional seismic design criteria take values of internal forced and other response variables as those provided by an envelope to the values of those variables produced by in-phase motion of all supports. In structures extended in plan, such as long bridges, or founded on heterogeneous formations or irregular topography, such as dams, differences in ground motion among different supports may give to differences as compared with those produced by conventional analysis. In this paper ground motion is represented as stochastic process with evolutionary intensity and frequency content. A criterion for determining design responses, based on the variance of the response of the structure is proposed. Proportionality criterion depends on cross-correlations between displacements and accelerations occurring at supports. The proposed criterion is illustrated by applying it to a continuous bridge supported on piles embedded in a variable depth layer of soft clay

Exploring a syntactic notion of modal many-valued logics

We propose a general semantic notion of modal many-valued logic. Then, we explore the di culties to characterize this notion in a syntactic way and analyze the existing literature with respect to this frameworkPeer Reviewe

On completeness results for predicate lukasiewicz, product, gödel and nilpotent minimum logics expanded with truth-constants

In this paper we deal with generic expansions of first-order predicate logics of some left-continuous t-norms with a countable set of truth-constants. Besides already known results for the case of Lukasiewicz logic, we obtain new conservativeness and completeness results for some other expansions. Namely, we prove that the expansions of predicate Product, Gödel and Nilpotent Minimum logics with truth-constants are conservative, which already implies the failure of standard completeness for the case of Product logic. In contrast, the expansions of predicate Gödel and Nilpotent Minimum logics are proved to be strong standard complete but, when the semantics is restricted to the canonical algebra, they are proved to be complete only for tautologies. Moreover, when the language is restricted to evaluated formulae we prove canonical completeness for deductions from finite sets of premises.Peer Reviewe

Unsupervised learning as a complement to convolutional neural network classification in the analysis of saccadic eye movement in spino-cerebellar ataxia type 2

IWANN es un congreso internacional que se celebra bienalmente desde 1991. Su campo de estudio se centra en la fundamentación y aplicación de las distintas técnicas de Inteligencia Computacional : Redes Neuronales Artificiales, Algoritmos Genéticos, Lógica Borrosa, Aprendizaje Automático. En esta edición han participado 150 investigadores.This paper aims at assessing spino-cerebellar type 2 ataxiaby classifying electrooculography records into registers corresponding to healthy, presymptomatic and ill individuals. The primary used technique is the convolutional neural network applied to the time series of eye movements, called saccades. The problem is exceptionally hard, though, because the recorded saccadic movements for presymptomatic cases often do not substantially di er from those of healthy individuals. Precisely this distinction is of the utmost clinical importance, since early intervention on presymptomatic patients can ameliorate symptoms or at least slow their progression. Yet, each register contains a number of saccades that, although not consistent with the current label, have not been considered indicative of another class by the examining physicians. As a consequence, an unsupervised learning mechanism may be more suitable to handle this form of misclassi cation. Thus, our proposal introduces the k-means approach and the SOM method, as complementary techniques to analyse the time series. The three techniques operating in tandem lead to a well performing solution to this diagnosis problem.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Universidad de Granada, Universitat Politècnica de Catalunya, Universidad de Las Palmas de Gran Canaria, Springe

Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.Comment: 9 pages, 2 figure

Fitting in a complex chi^2 landscape using an optimized hypersurface sampling

Fitting a data set with a parametrized model can be seen geometrically as finding the global minimum of the chi^2 hypersurface, depending on a set of parameters {P_i}. This is usually done using the Levenberg-Marquardt algorithm. The main drawback of this algorithm is that despite of its fast convergence, it can get stuck if the parameters are not initialized close to the final solution. We propose a modification of the Metropolis algorithm introducing a parameter step tuning that optimizes the sampling of parameter space. The ability of the parameter tuning algorithm together with simulated annealing to find the global chi^2 hypersurface minimum, jumping across chi^2{P_i} barriers when necessary, is demonstrated with synthetic functions and with real data

Application of mathematical and machine learning techniques to analyse eye tracking data enabling better understanding of children’s visual cognitive behaviours

In this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements were explored using machine learning approaches. Two different techniques of machine-learning were used (Bayesian and K-Means) to obtain separate model sequences or average scanpaths for those children who responded either correctly or incorrectly to the graph task. Application of these machine-learning approaches indicated distinct differences in the resulting scanpaths for children who completed the graph task correctly or incorrectly: children who responded correctly accessed information that was mostly categorised as critical, whereas children responding incorrectly did not. There was also evidence that the children who were correct accessed the graph information in a different, more logical order, compared to the children who were incorrect. The visual behaviours aligned with different aspects of graph comprehension, such as initial understanding and orienting to the graph, and later interpretation and use of relevant information on the graph. The findings are discussed in terms of the implications for early mathematics teaching and learning, particularly in the development of graph comprehension, as well as the application of machine learning techniques to investigations of other visual-cognitive behaviours.Peer reviewe