Advances in the numerical treatment of grain-boundary migration: Coupling with mass transport and mechanics

This work is based upon a coupled, lattice-based continuum formulation that was previously applied to problems involving strong coupling between mechanics and mass transport; e.g. diffusional creep and electromigration. Here we discuss an enhancement of this formulation to account for migrating grain boundaries. The level set method is used to model grain-boundary migration in an Eulerian framework where a grain boundary is represented as the zero level set of an evolving higher-dimensional function. This approach can easily be generalized to model other problems involving migrating interfaces; e.g. void evolution and free-surface morphology evolution. The level-set equation is recast in a remarkably simple form which obviates the need for spatial stabilization techniques. This simplified level-set formulation makes use of velocity extension and field re-initialization techniques. In addition, a least-squares smoothing technique is used to compute the local curvature of a grain boundary directly from the level-set field without resorting to higher-order interpolation. A notable feature is that the coupling between mass transport, mechanics and grain-boundary migration is fully accounted for. The complexities associated with this coupling are highlighted and the operator-split algorithm used to solve the coupled equations is described.Comment: 28 pages, 9 figures, LaTeX; Accepted for publication in Computer Methods in Applied Mechanics and Engineering. [Style and formatting modifications made, references added.

Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data

The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divided into multiple parties or when the dataset is too large to be handled by the data owner. In the latter case, usually some servers are hired to perform the task of clustering. The dataset is divided by the data owner among the servers who together perform the k-means and return the cluster labels to the owner. The major challenge in this method is to prevent the servers from gaining substantial information about the actual data of the owner. Several algorithms have been designed in the past that provide cryptographic solutions to perform privacy preserving k-means. We provide a new method to perform k-means over a large set using multiple servers. Our technique avoids heavy cryptographic computations and instead we use a simple randomization technique to preserve the privacy of the data. The k-means computed has exactly the same efficiency and accuracy as the k-means computed over the original dataset without any randomization. We argue that our algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems Security. Springer, Cham, 201

Colocalization of different neurotransmitter transporters on synaptic vesicles is sparse except for VGLUT1 and ZnT3

Vesicular transporters (VTs) define the type of neurotransmitter that synaptic vesicles (SVs) store and release. While certain mammalian neurons release multiple transmitters, it is not clear whether the release occurs from the same or distinct vesicle pools at the synapse. Using quantitative single-vesicle imaging, we show that a vast majority of SVs in the rodent brain contain only one type of VT, indicating specificity for a single neurotransmitter. Interestingly, SVs containing dual transporters are highly diverse (27 types) but small in proportion (2% of all SVs), excluding the largest pool that carries VGLUT1 and ZnT3 (34%). Using VGLUT1-ZnT3 SVs, we demonstrate that the transporter colocalization influences the SV content and synaptic quantal size. Thus, the presence of diverse transporters on the same vesicle is bona fide, and depending on the VT types, this may act to regulate neurotransmitter type, content, and release in space and time

Shear accommodation in dirty grain boundaries

The effect of solutes (dirt) on the mechanics of crystalline interfaces remains unexplored. Here, we perform atomic-scale simulations to study the effect of carbon segregation on the shear accommodation at select grain boundaries in the classical α-Fe/C system. For shear velocities larger than the solute diffusion rate, we observe a transition from coupled motion to sliding. Below a critical solute excess, the boundaries break away from the solute cloud and exhibit in a coupled motion. At smaller shear velocities, the extrinsic coupled motion is jerky, occurs at relatively small shear stresses, and is aided by fast convective solute diffusion along the boundary. Our studies underscore the combined effect of energetics and kinetics of solutes in modifying the bicrystallography, temperature and rate dependence of shear accommodation at grain boundaries