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We present dynamic light scattering (DLS) measurements of soft polymethyl-methacrylate (PMMA) and polyacrylamide(PA) polymer gels prepared with trapped bodies (latex spheres or maghemite nanoparticles). We show that the anomalous…
An adaptive kernel method in the Bayesian framework together with a new simulation program for Rutherford backscattering spectroscopy (RBS) have been applied to the analysis of RBS data. Even in the case of strongly overlapping RBS peaks a…
We develop theory and computational methods to investigate particle inclusions embedded within curved lipid bilayer membranes. We consider the case of spherical lipid vesicles where inclusion particles are coupled through (i) intramembrane…
We develop a Bayesian variable selection method, called SVEN, based on a hierarchical Gaussian linear model with priors placed on the regression coefficients as well as on the model space. Sparsity is achieved by using degenerate spike…
We have studied the mesoscopic shape fluctuations in aligned multilamellar stacks of DMPC bilayers using the neutron spin-echo technique. The corresponding in plane dispersion relation $\tau^{-1}$(q$_{||}$) at different temperatures in the…
Using x-ray photon correlation spectroscopy, we studied the layer fluctuations in the lamellar phase of an ionic lyotropic system. We measured the relaxation rate of in-plane (undulation) fluctuations as a function of the wave vector.…
This paper proposes a new data-driven approach to model detailed splashes for liquid simulations with neural networks. Our model learns to generate small-scale splash detail for the fluid-implicit-particle method using training data…
We describe an optical fiber based interferometer to measure velocity profiles in sheared complex fluids using Dynamic Light Scattering (DLS). After a review of the theoretical problem of DLS under shear, a detailed description of the setup…
We propose an empirical approach centered on the spectral dynamics of weights -- the behavior of singular values and vectors during optimization -- to unify and clarify several phenomena in deep learning. We identify a consistent bias in…
The incoherent approximation for the determination of the vibrational density of states of glasses from inelastic neutron or x-ray scattering data is extended to treat the coherent scattering. The method is applied to new room temperature…
Converting neutron scattering data to real-space time-dependent structures can only be achieved through suitable models, which is particularly challenging for geometrically disordered structures. We address this problem by introducing…
Dynamics of spreading of viscous non - volatile fluid droplets on surfaces is modelled using a solid - on - solid model, which is studied with Monte Carlo simulations. Tendency for dynamical layering and surface attraction are in part…
We have used convolutional neural network in a classification task to track the radius evolution of levitating evaporating microdroplets of pure diethylene glycol, diethylene glycol water polystyrene microparticles suspension, dipropylene…
Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a…
Super-resolution microscopy is crucial for imaging sub-wavelength biological structures. However, most techniques rely on nonlinear saturation or stochastic switching of emitters, limiting imaging speed and increasing phototoxicity. Here,…
The membrane curvature of cells and intracellular compartments continuously adapts to enable cells to perform vital functions, from cell division to signal trafficking. Understanding how membrane geometry affects these processes in vivo is…
We present a new procedure which enables to extract a scattering matrix $S(l)$ as a complex function of angular momentum directly from the scattering data, without any a priori model assumptions implied. The key ingredient of the procedure…
Multiscale models of materials, consisting of upscaling discrete simulations to continuum models, are unique in their capability to simulate complex materials behavior. The fundamental limitation in multiscale models is the presence of…
We have developed a multiscale structure identification algorithm for the detection of overdensities in galaxy data that identifies structures having radii within a user-defined range. Our "multiscale probability mapping" technique combines…
In assumed probability density function (pdf) methods of turbulent combustion, the shape of the scalar pdf is assumed a priori and the pdf is parametrized by its moments for which model equations are solved. In non-premixed flows the beta…