Search results for "dimensionality"
showing 10 items of 231 documents
Lossless coding of hyperspectral images with principal polynomial analysis
2014
The transform in image coding aims to remove redundancy among data coefficients so that they can be independently coded, and to capture most of the image information in few coefficients. While the second goal ensures that discarding coefficients will not lead to large errors, the first goal ensures that simple (point-wise) coding schemes can be applied to the retained coefficients with optimal results. Principal Component Analysis (PCA) provides the best independence and data compaction for Gaussian sources. Yet, non-linear generalizations of PCA may provide better performance for more realistic non-Gaussian sources. Principal Polynomial Analysis (PPA) generalizes PCA by removing the non-li…
Clustering-Based Protocol Classification via Dimensionality Reduction
2015
We propose a unique framework that is based upon diffusion processes and other methodologies for finding meaningful geometric descriptions in high-dimensional datasets. We will show that the eigenfunctions of the generated underlying Markov matrices can be used to construct diffusion processes that generate efficient representations of complex geometric structures for high-dimensional data analysis. This is done by non-linear transformations that identify geometric patterns in these huge datasets that find the connections among them while projecting them onto low dimensional spaces. Our methods automatically classify and recognize network protocols. The main core of the proposed methodology…
Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation
2014
This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical …
Linear and non-linear brain-heart and brain-brain interactions during sleep.
2015
In this study, the physiological networks underlying the joint modulation of the parasympathetic component of heart rate variability (HRV) and of the different electroencephalographic (EEG) rhythms during sleep were assessed using two popular measures of directed interaction in multivariate time series, namely Granger causality (GC) and transfer entropy (TE). Time series representative of cardiac and brain activities were obtained in 10 young healthy subjects as the normalized high frequency (HF) component of HRV and EEG power in the δ, θ, α, Ï, and β bands, measured during the whole duration of sleep. The magnitude and statistical significance of GC and TE were evaluated between each …
Perovskite nanoparticles : synthesis, properties, and novel applications in photovoltaics and LEDs
2018
Solar cells and light-emitting diodes (LEDs) based on metal-halide perovskites are transitioning from promising performers to direct competitors to well-established technologies, with cost-effectiveness as a strong advantage. Nanostructured perovskites have yielded record LEDs due to their higher versatility in the local management of charge carriers, which has enabled photoluminescence quantum yields (PLQYs) close to 100%. However, these perovskite nanostructures are yet to be fully exploited in other applications such as photovoltaics, where they can also present competitive advantages as they enable feasible routes to surpass the Shockley–Queisser limit by means of multiexciton generatio…
High-pressure Raman investigation of high index facets bounded alpha-Fe2O3 pseudocubic crystals
2021
[EN] High index facet bounded alpha-Fe2O3 pseudocubic crystals has gained the attention of the scientific community due to its promising electrochemical sensing response towards aqueous ammonia. The structural stability of alpha-Fe2O3 pseudocubic crystals is investigated through high-pressure Raman spectroscopy up to 22.2 GPa, and those results are compared with our ab initio theoretical calculations. The symmetry of the experimental Raman-active modes has been assigned by comparison with theoretical data. In addition to the Raman-active modes, two additional Raman features are also detected, whose intensity increases with compression. The origin of these two additional peaks addressed in t…
Least-squares temporal difference learning based on an extreme learning machine
2014
Abstract Reinforcement learning (RL) is a general class of algorithms for solving decision-making problems, which are usually modeled using the Markov decision process (MDP) framework. RL can find exact solutions only when the MDP state space is discrete and small enough. Due to the fact that many real-world problems are described by continuous variables, approximation is essential in practical applications of RL. This paper is focused on learning the value function of a fixed policy in continuous MPDs. This is an important subproblem of several RL algorithms. We propose a least-squares temporal difference (LSTD) algorithm based on the extreme learning machine. LSTD is typically combined wi…
A parsimonious model for generating arbitrage-free scenario trees
2016
Simulation models of economic, financial and business risk factors are widely used to assess risks and support decision-making. Extensive literature on scenario generation methods aims at describing some underlying stochastic processes with the least number of scenarios to overcome the ‘curse of dimensionality’. There is, however, an important requirement that is usually overlooked when one departs from the application domain of security pricing: the no-arbitrage condition. We formulate a moment matching model to generate multi-factor scenario trees for stochastic optimization satisfying no-arbitrage restrictions with a minimal number of scenarios and without any distributional assumptions.…
Generating Multi-Asset Arbitrage-Free Scenario Trees with Global Optimization
2013
Simulation models of economic, financial and business risk factors are widely used to assess risks and support decision-making. Extensive literature on scenario generation methods aims at describing some underlying stochastic processes with the least number of scenarios to overcome the "curse of dimensionality". There is, however, an important requirement that is usually overlooked when one departs from the application domain of security pricing: the no-arbitrage condition. We formulate a moment matching model to generate multi-factor scenario trees satisfying no-arbitrage restrictions with a minimal number of scenarios and without any distributional assumptions. The resulting global optimi…
Cooperation and conflict in host manipulation: interactions among macro-parasites and micro-organisms.
2014
10 pages; International audience; Several parasite species are known to manipulate the phenotype of their hosts in ways that enhance their own transmission. Co-occurrence of manipulative parasites, belonging to the same species or to more than one species, in a single host has been regularly observed. Little is known, however, on interactions between co-occurring manipulative parasites with same or different transmission routes. Several models addressing this problem have provided predictions on how cooperation and conflict between parasites could emerge from multiple infections. Here, we review the empirical evidence in favor of the existence of synergistic or antagonistic interactions bet…