The new very small angle neutron scattering spectrometer at Laboratoire Leon Brillouin

The design and characteristics of the new very small angle neutron scattering spectrometer under construction at the Laboratoire Leon Brillouin is described. Its goal is to extend the range of scattering vectors magnitudes towards 2x10{-4} /A. The unique feature of this new spectrometer is a high resolution two dimensional image plate detector sensitive to neutrons. The wavelength selection is achieved by a double reflection supermirror monochromator and the collimator uses a novel multibeam design

A double supermirror monochromator for neutron instrumentation at LLB

The design and characteristics of a double supermirror monochromator for neutron instrumentation at the Laboratoire Leon Brillouin is described. The aim of this monochromator is to reduce the intense gamma-radiation produced by conventional velocity selectors and to avoid a direct view of the guide while keeping a comparable neutron transmission (higher than 70%). The monochromator offers a continuous choice of wavelength selection in the range 0.5 to 2 nm

GARDENS OF OCEANIA

International Development,

Both chronic treatments by epothilone D and fluoxetine increase the short-term memory and differentially alter the mood status of STOP/MAP6 KO mice.: epothilone and fluoxetine improve STOP KO memory

International audienceRecent evidence underlines the crucial role of neuronal cytoskeleton in the pathophysiology of psychiatric diseases. In this line, the deletion of STOP/MAP6 (Stable Tubule Only Polypeptide), a microtubule-stabilizing protein, triggers various neurotransmission and behavioral defects, suggesting that STOP knockout (KO) mice could be a relevant experimental model for schizoaffective symptoms. To establish the predictive validity of such a mouse line, in which the brain serotonergic tone is dramatically imbalanced, the effects of a chronic fluoxetine treatment on the mood status of STOP KO mice were characterized. Moreover, we determined the impact, on mood, of a chronic treatment by epothilone D, a taxol-like microtubule-stabilizing compound that has previously been shown to improve the synaptic plasticity deficits of STOP KO mice. We demonstrated that chronic fluoxetine was either antidepressive and anxiolytic, or pro-depressive and anxiogenic, depending on the paradigm used to test treated mutant mice. Furthermore, control-treated STOP KO mice exhibited paradoxical behaviors, compared with their clear-cut basal mood status. Paradoxical fluoxetine effects and control-treated STOP KO behaviors could be because of their hyper-reactivity to acute and chronic stress. Interestingly, both epothilone D and fluoxetine chronic treatments improved the short-term memory of STOP KO mice. Such treatments did not affect the serotonin and norepinephrine transporter densities in cerebral areas of mice. Altogether, these data demonstrated that STOP KO mice could represent a useful model to study the relationship between cytoskeleton, mood, and stress, and to test innovative mood treatments, such as microtubule-stabilizing compounds

Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series

We propose a quantum algorithm for sampling from a solution of stochastic differential equations (SDEs). Using differentiable quantum circuits (DQCs) with a feature map encoding of latent variables, we represent the quantile function for an underlying probability distribution and extract samples as DQC expectation values. Using quantile mechanics we propagate the system in time, thereby allowing for time-series generation. We test the method by simulating the Ornstein-Uhlenbeck process and sampling at times different from the initial point, as required in financial analysis and dataset augmentation. Additionally, we analyse continuous quantum generative adversarial networks (qGANs), and show that they represent quantile functions with a modified (reordered) shape that impedes their efficient time-propagation. Our results shed light on the connection between quantum quantile mechanics (QQM) and qGANs for SDE-based distributions, and point the importance of differential constraints for model training, analogously with the recent success of physics informed neural networks.Comment: v3, minor updat

Mathematical modelling of the atherosclerotic plaque formation

International audienceThis article is devoted to the construction of a mathematical model describing the early formation of atherosclerotic lesions. Following the work of El Khatib, Genieys and Volpert, we model atherosclerosis as an inflammatory disease. We consider that the inflammatory process starts with the penetration of Low Density Lipoproteins cholesterol in the intima. This phenomenon is related to the local blood flow dynamics. Using a system of reaction-diffusion equations, we first provide a one-dimensional model of lesion growth. Then we perform numerical simulations on a two-dimensional geometry mimicking the carotid artery. We couple the previous mathematical model with blood flow and we provide a model in which the lesion appears in the area of lower shear stress

What can we learn from quantum convolutional neural networks?

We can learn from analyzing quantum convolutional neural networks (QCNNs) that: 1) working with quantum data can be perceived as embedding physical system parameters through a hidden feature map; 2) their high performance for quantum phase recognition can be attributed to generation of a very suitable basis set during the ground state embedding, where quantum criticality of spin models leads to basis functions with rapidly changing features; 3) pooling layers of QCNNs are responsible for picking those basis functions that can contribute to forming a high-performing decision boundary, and the learning process corresponds to adapting the measurement such that few-qubit operators are mapped to full-register observables; 4) generalization of QCNN models strongly depends on the embedding type, and that rotation-based feature maps with the Fourier basis require careful feature engineering; 5) accuracy and generalization of QCNNs with readout based on a limited number of shots favor the ground state embeddings and associated physics-informed models. We demonstrate these points in simulation, where our results shed light on classification for physical processes, relevant for applications in sensing. Finally, we show that QCNNs with properly chosen ground state embeddings can be used for fluid dynamics problems, expressing shock wave solutions with good generalization and proven trainability.Comment: 13 pages, 7 figure

Extracting the hydrodynamic resistance of droplets from their behavior in microchannel networks

The overall traffic of droplets in a network of microfluidic channels is strongly influenced by the liquid properties of the moving droplets. In particular, the effective hydrodynamic resistance of individual droplets plays a key role in their global behavior. We here propose two simple and low-cost experimental methods for measuring this parameter by analyzing the dynamics of a regular sequence of droplets injected into an "asymmetric loop" network. The choice of a droplet taking either route through the loop is influenced by the presence of previous droplets which modulate the hydrodynamic resistance of the branches they are sitting in. We propose to extract the effective resistance of a droplet from easily observable time series, namely from the choices the droplets make at junctions and from the inter-droplet distances. This becomes possible when utilizing a recently proposed theoretical model, based on a number of simplifying assumptions. We here present several sets of measurements of the hydrodynamic resistance of droplets, expressed in terms of a "resistance length". The aim is twofold, (1) to reveal its dependence on a number of parameters, such as the viscosity, the volume of droplets, their velocity as well as the spacing between them. At the same time (2), by using a standard measurement technique, we compare the limitations of the proposed methods. As an important result of this comparison we obtain the range of validity of the simplifying assumptions made in the theoretical model.Comment: 11 pages, 11 figure