Unit four: family

LicenceOf all my relatives, I like my Aunt Emily the best. She's my mother's youngest sister. She has never married, and she lives alone in a small village near Bath. She's in her late fifties, but she's still quite young in spirit. She has a fair complexion, thick brown hair which she wears in a bun, and dark brown eyes. She has a kind face, and when you meet her, the first thing you notice is her lovely, warm smile. Her face is a little wrinkled now, but I think she's still rather attractive. She is the sort of person you can always go to if you have a problem. She likes reading and gardening, and she goes for long walks over the hills with her dog, Buster. She's a very active person. Either she's making something, or mending something, or doing something to help others. She does the shopping for some of the old people in the village. She's extremely generous, but not very tolerant with people who don't agree with her. I hope that I am as happy and contented as she is when I'm her age

Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

Parallel MRI is a fast imaging technique that enables the acquisition of highly resolved images in space or/and in time. The performance of parallel imaging strongly depends on the reconstruction algorithm, which can proceed either in the original k-space (GRAPPA, SMASH) or in the image domain (SENSE-like methods). To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated. In this paper, we extend this approach using 3D-wavelet representations in order to handle all slices together and address reconstruction artifacts which propagate across adjacent slices. The gain induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE: 3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal acquisition is considered. Another important extension accounts for temporal correlations that exist between successive scans in functional MRI (fMRI). In addition to the case of 2D+t acquisition schemes addressed by some other methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and 4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that all regularization parameters are estimated in the maximum likelihood sense on a reference scan. The gain induced by such extensions is illustrated on both anatomical and functional image reconstruction, and also measured in terms of statistical sensitivity for the 4D-UWR-SENSE approach during a fast event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (eg, motor or computation tasks) and using different parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353

Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach

In standard clinical within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based Joint Detection-Estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian modeling. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to approximate the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows fine automatic tuning of spatial regularisation parameters. It follows a new algorithm that exhibits interesting properties compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model mis-specification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery

PID Control for Takagi-Sugeno Fuzzy Model

In this chapter, we deal with the problem of controlling Takagi-Sugeno (TS) fuzzy model by PID controllers using the particle swarm optimization (PSO). Therefore, a new algorithm is proposed. This algorithm relies on the use of a new objective function taking into account both the performance indices and the error signal. The advantages of this approach are discussed through simulations on a numerical example

Fuel consumption assessment in delivery tours to develop eco driving behaviour

Full text available for free at http://abstracts.aetransport.org/paper/index/id/3886/confid/18International audienceA report of the European Commission in 1998 identified various areas that can be explored to achieve a sustainable logistics. Among those areas, we discuss the reduction of fuel consumption by an eco-driving strategy. Eco-driving is often cited as a good practice to reduce fuel consumption and claim a potential of - 10% to - 20% of fuel consumption and CO2 emissions. Freight transportation by truck is one of the major contributors to CO2 emissions (14% of the grand total in France). However, assessing its potential in actual operations is not an easy task and to our best knowledge has never been done before on a comprehensive scale. There were no researches that were able to prove the efficiency of eco-driving in an operational freight transport context. To complement other researches that aim to bring a theoretical analysis to the link between the consumption and its impacting factors, this research is anchored in practice. Firstly it measures consumption on real situations. About 9000 tours were followed and analyzed. Secondly the significant fuel consumption factors are analysed. Third the importance of driving behaviour as one of the most important factors for reducing consumption is assessed. In this research, done in collaboration with a logistics services provider operating its own trucks fleet, we defined a measurement protocol implemented in 29 trucks. Then we were able to retain the fuel consumption and to link it to the context of the tour. Several incentives were tested to motivate truck drivers in order to reduce fuel consumption. This raises the question of the individual measurement and the evaluation of the driving behaviour improvement. In classical eco-driving models, the estimation of the eco-driving fuel consumption depending on the tour environment was often overlooked because of the complexity of the task. However it is required to build a new sustainable incentive system. The main contribution of this paper is to identify and to propose a new system that allows logistics service provider to evaluate driving behaviours and to share the eco driving individual gain as a new driver incentive method. As a result we propose a non linear model to estimate an interval of eco-driving consumption depending on tour environment factors like truck type, road type, speed, load and weather. By reporting the eco-driving strategy implemented in 3 different operational areas during 2 years, this research has enabled us to understand the benefits of the actions to reach fuel consumption and emissions reduction up to 4,2%. It shows here that eco driving strategy can be very efficient in an operational freight transportation environment. In this contribution we developed a first assessment of driving behaviour depending on the conditions of every tour. Thus this paper opens research opportunities in two directions; the first is the experimentation of this approach in different context. The second direction is the enhancement of the model to gain in precision or in robustness

Bayesian optimization for sparse neural networks with trainable activation functions

In the literature on deep neural networks, there is considerable interest in developing activation functions that can enhance neural network performance. In recent years, there has been renewed scientific interest in proposing activation functions that can be trained throughout the learning process, as they appear to improve network performance, especially by reducing overfitting. In this paper, we propose a trainable activation function whose parameters need to be estimated. A fully Bayesian model is developed to automatically estimate from the learning data both the model weights and activation function parameters. An MCMC-based optimization scheme is developed to build the inference. The proposed method aims to solve the aforementioned problems and improve convergence time by using an efficient sampling scheme that guarantees convergence to the global maximum. The proposed scheme is tested on three datasets with three different CNNs. Promising results demonstrate the usefulness of our proposed approach in improving model accuracy due to the proposed activation function and Bayesian estimation of the parameters

First order pyramidal slip of 1/3 <1-210> screw dislocations in zirconium

Atomistic simulations, based either on an empirical interatomic potential or on ab initio calculations, are used to study the pyramidal glide of a 1/3 screw dislocation in hexagonal close-packed zirconium. Generalized stacking fault calculations reveal a metastable stacking fault in the first order pyramidal {10-11} plane, which corresponds to an elementary pyramidal twin. This fault is at the origin of a metastable configuration of the screw dislocation in zirconium, which spontaneously appears when the dislocation glides in the pyramidal plane.Comment: symposium "Multiscale perspectives on plasticity in hcp metals", TMS 2014 annual meeting, Metallurgical and Materials Transactions A (2014

Sparse signal recovery using a Bernoulli generalized Gaussian prior

International audienceBayesian sparse signal recovery has been widely investigated during the last decade due to its ability to automatically estimate regularization parameters. Prior based on mixtures of Bernoulli and continuous distributions have recently been used in a number of recent works to model the target signals , often leading to complicated posteriors. Inference is therefore usually performed using Markov chain Monte Carlo algorithms. In this paper, a Bernoulli-generalized Gaussian distribution is used in a sparse Bayesian regularization framework to promote a two-level flexible sparsity. Since the resulting conditional posterior has a non-differentiable energy function , the inference is conducted using the recently proposed non-smooth Hamiltonian Monte Carlo algorithm. Promising results obtained with synthetic data show the efficiency of the proposed regularization scheme