Search results for "methodologie"
showing 10 items of 2141 documents
Inferring slowly-changing dynamic gene-regulatory networks
2015
Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experi…
On the metric properties of dynamic time warping
1987
Recently, some new and promising methods have been proposed to reduce the number of Dynamic Time Warping (DTW) computations in Isolated Word Recognition. For these methods to be properly applicable, the verification of the Triangle Inequality (TI) by the DTW-based Dissimilarity Measure utilized seems to be an important prerequisite.
Trading off accuracy for efficiency by randomized greedy warping
2016
Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for finding similarity between time series instances. We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for ve…
Pathway network inference from gene expression data
2014
[EN] Background: The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results: We presen…
Difract: Un nuevo laboratorio virtual para la modelización matemática de las propiedades de difracción de redes fractales
2011
[EN] This work presents a new virtual laboratory, Difract, developed with Easy Java Simulations, for using in Optics courses as a computer tool for the mathematical modelling of the diffraction properties of 1D and 2D fractal gratings. This virtual laboratory enables students to quickly and easily analyze the influence on the Fraunhofer diffraction pattern of the different construction parameters of the fractal grating. As an application example, the Cantor fractal set has been considered.
The species-specific monitoring protocols for plant species of Community interest in Italy
2017
The results of a project for the identification of species-specific monitoring protocols for the Italian plant species protected under the Habitats Directive (Annexes II/IV/V) are presented. The project led to the development of 118 monitoring factsheets, providing an operational guidance for 107 vascular taxa, 10 bryophytes and 1 lichen taxon. Each factsheet includes information on the species (distribution, biology, ecology, conservation status, threats, etc.) and the description of field methodologies for the detection of the two main reporting parameters, i.e. population size and habitat quality. Practical information to plan field activities are also given. Protocols were designed to a…
The measurement of rank mobility
2009
Abstract In this paper we investigate the problem of measuring social mobility when the social status of individuals is given by their rank. In order to sensibly represent the rank mobility of subgroups within a given society, we address the problem in terms of partial permutation matrices which include standard (“global”) matrices as a special case. We first provide a characterization of a partial ordering on partial matrices which, in the standard case of global matrices, coincides with the well-known “concordance” ordering. We then provide a characterization of an index of rank mobility based on partial matrices and show that, in the standard case of comparing global matrices, it is equi…
Human capital and income inequality revisited
2021
This paper revisits the relationship between human capital and income inequality, using an updated data set on human capital inequality and a novel database on earnings inequality. We find an inver...
E-learning in radiology: an Italian multicentre experience.
2012
Objective: The aim of this study was to design, deliver and evaluate an e-learning teaching programme for post-graduate radiodiagnostics training that would involve various post-graduate schools throughout Italy. Materials and methods: All of the Directors of Italian post-graduate schools of radiodiagnostics were sent an e-mail on 27 September 2010 informing them of our willingness to set up an e-learning project for the academic year 2010-2011 in the form of single-subject teaching seminars. The proposed subjects were the semeiotics of the various organs and apparatuses in the context of "Urgent/Emergency Pathology". After having received registrations, a calendar of lessons was planned to…
Eigenexpressions: Emotion Recognition Using Multiple Eigenspaces
2013
This paper presents an appearance-based holistic method for expression recognition. A two stage supervised learning approach is used. At the first stage, training images are used to compute one subspace per expression. At the second stage, the same images are used to train a classifier. In this step, Euclidean distances from each image to each particular subspace are used as the input to the classifier. The resulting system significantly outperforms the baseline eigenfaces method on the Cohn-Kanade data set, with performance gains in the range 10%-20%.