Search results for "1707"
showing 10 items of 274 documents
Algorithms for the inference of causality in dynamic processes: Application to cardiovascular and cerebrovascular variability
2015
This study faces the problem of causal inference in multivariate dynamic processes, with specific regard to the detection of instantaneous and time-lagged directed interactions. We point out the limitations of the traditional Granger causality analysis, showing that it leads to false detection of causality when instantaneous and time-lagged effects coexist in the process structure. Then, we propose an improved algorithm for causal inference that combines the Granger framework with the approach proposed by Pearl for the study of causality among multiple random variables. This new approach is compared with the traditional one in theoretical and simulated examples of interacting processes, sho…
Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series
2012
The complexity of the short-term cardiovascular control prompts for the introduction of multivariate (MV) nonlinear time series analysis methods to assess directional interactions reflecting the underlying regulatory mechanisms. This study introduces a new approach for the detection of nonlinear Granger causality in MV time series, based on embedding the series by a sequential, non-uniform procedure, and on estimating the information flow from one series to another by means of the corrected conditional entropy. The approach is validated on short realizations of linear stochastic and nonlinear deterministic processes, and then evaluated on heart period, systolic arterial pressure and respira…
Estimating brain connectivity when few data points are available: Perspectives and limitations
2017
Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in …
Adaptative air traffic network: Statistical regularities in air traffic management
2016
Starting from traffic data on flights trajectories-planned and actual ones-in Europe, we build a navigation point network. We study this network which exhibits different features for different European countries. In particular, some countries uses a high number of navpoints, facilitating the planning of the flight plan by air companies at the cost of higher concentrations of traffic in few nodes. Making use of the deviations from the planned trajectories, we find that once again different countries have different control procedures with respect to traffic management. Interestingly, we find that some countries tend to make more deviations when the traffic conditions are low. Moreover, they t…
Extracellular Vesicle-Mediated Cell–Cell Communication in the Nervous System: Focus on Neurological Diseases
2019
Extracellular vesicles (EVs), including exosomes, are membranous particles released by cells into the extracellular space. They are involved in cell differentiation, tissue homeostasis, and organ remodelling in virtually all tissues, including the central nervous system (CNS). They are secreted by a range of cell types and via blood reaching other cells whose functioning they can modify because they transport and deliver active molecules, such as proteins of various types and functions, lipids, DNA, and miRNAs. Since they are relatively easy to isolate, exosomes can be characterized, and their composition elucidated and manipulated by bioengineering techniques. Consequently, exosomes appear…
Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO.
2012
The present study introduces sparse modeling for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence (PDC) function, derived from fitting a multivariate autoregressive model to the observed signals. A sparse optimization method is proposed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO). In simulations aLASSO was found superior to the commonly used least-squares (LS) estimation with respect to estimation performance. The normalized error between the true and estimated model parameters dropped from 0.200.04 for LS estimatio…
How to standardize (if you must)
2017
In many situations we are interested in appraising the value of a certain characteristic for a given individual relative to the context in which this value is observed. In recent years this problem has become prominent in the evaluation of scientific productivity and impact. A popular approach to such relative valuations consists in using percentile ranks. This is a purely ordinal method that may sometimes lead to counterintuitive appraisals, in that it discards all information about the distance between the raw values within a given context. By contrast, this information is partly preserved by using standardization, i.e., by transforming the absolute values in such a way that, within the s…
A Planning and Control System for Self-Driving Racing Vehicles
2018
Autonomous robots will soon enter our everyday life as self-driving cars. These vehicles are designed to behave according to certain sets of cooperative rules, such as traffic ones, and to respond to events that might be unpredictable in their occurrence but predictable in their nature, such as a pedestrian suddenly crossing a street, or another car losing control. As civilian autonomous cars will cross the road, racing autonomous cars are under development, which will require superior Artificial Intelligence Drivers to perform in structured but uncertain conditions. We describe some preliminary results obtained during the development of a planning and control system as key elements of an A…
Coating impact and radiation effects on optical frequency domain Reflectometry fiber-based temperature sensors
2015
International audience; Temperature response of radiation-tolerant OFDR-based sensors is here investigated, with particular attention on the impact of coating on OFS. By performing consecutive thermal treatments we developed a controlled system to evaluate the performances of our distributed temperature sensor and to estimate the radiation impact. We show an important evolution of the temperature coefficient measurements with thermal treatments for non-irradiated fiber and that the amplitude of this change decreases increasing radiation dose. As final results, we demonstrate that sensor performances are improved if we performed a pre-thermal treatment on the fiber-based system permitting to…
Steady state γ-ray radiation effects on Brillouin fiber sensors
2015
International audience; Brillouin optical time-domain analysis (BOTDA) sensors offer remarkable advantages for the surveillance of the planned French deep geological radioactive wastes repository, called Cigéo1,2. In this work we study the performances of Brillouin distributed sensors in harsh environment. We evaluate the radiation tolerance of different sensor classes and their responses evolution during γ-ray exposition with 1kGy/h dose rate (to reach ~0.2MGy) and after 1, 3, 6 and 10 MGy accumulated doses. Measurements on strained Ge-doped SMF are reported to highlight the variation on Brillouin scattering proprieties, both intrinsic frequency position of Brillouin shift and its dependen…