Shell Model Monte Carlo method in the pnpn-formalism and applications to the Zr and Mo isotopes

We report on the development of a new shell-model Monte Carlo algorithm which uses the proton-neutron formalism. Shell model Monte Carlo methods, within the isospin formulation, have been successfully used in large-scale shell-model calculations. Motivation for this work is to extend the feasibility of these methods to shell-model studies involving non-identical proton and neutron valence spaces. We show the viability of the new approach with some test results. Finally, we use a realistic nucleon-nucleon interaction in the model space described by (1p_1/2,0g_9/2) proton and (1d_5/2,2s_1/2,1d_3/2,0g_7/2,0h_11/2) neutron orbitals above the Sr-88 core to calculate ground-state energies, binding energies, B(E2) strengths, and to study pairing properties of the even-even 90-104 Zr and 92-106 Mo isotope chains

A position sensitive phoswich hard X-ray detector system

A prototype position sensitive phoswich hard X-ray detector, designed for eventual astronomical usage, was tested in the laboratory. The scintillation crystal geometry was designed on the basis of a Monte Carlo simulation of the internal optics and includes a 3mm thick NaI(T1) primary X-ray detector which is actively shielded by a 20 mm thick CsI(T1) scintillation crystal. This phoswich arrangement is viewed by a number two inch photomultipliers. Measured values of the positional and spectral resolution of incident X-ray photons are compared with calculation

Overscreening in 1D lattice Coulomb gas model of ionic liquids

Overscreening in the charge distribution of ionic liquids at electrified interfaces is shown to proceed from purely electrostatic and steric interactions in an exactly soluble one dimensional lattice Coulomb gas model. Being not a mean-field effect, our results suggest that even in higher dimensional systems the overscreening could be accounted for by a more accurate treatment of the basic lattice Coulomb gas model, that goes beyond the mean field level of approximation, without any additional interactions.Comment: 4 pages 5 .eps figure

Perturbation theory for the effective diffusion constant in a medium of random scatterer

We develop perturbation theory and physically motivated resummations of the perturbation theory for the problem of a tracer particle diffusing in a random media. The random media contains point scatterers of density $\rho$ uniformly distributed through out the material. The tracer is a Langevin particle subjected to the quenched random force generated by the scatterers. Via our perturbative analysis we determine when the random potential can be approximated by a Gaussian random potential. We also develop a self-similar renormalisation group approach based on thinning out the scatterers, this scheme is similar to that used with success for diffusion in Gaussian random potentials and agrees with known exact results. To assess the accuracy of this approximation scheme its predictions are confronted with results obtained by numerical simulation.Comment: 22 pages, 6 figures, IOP (J. Phys. A. style

Current-induced nuclear-spin activation in a two-dimensional electron gas

Electrically detected nuclear magnetic resonance was studied in detail in a two-dimensional electron gas as a function of current bias and temperature. We show that applying a relatively modest dc-current bias, I_dc ~ 0.5 microAmps, can induce a re-entrant and even enhanced nuclear spin signal compared with the signal obtained under similar thermal equilibrium conditions at zero current bias. Our observations suggest that dynamic nuclear spin polarization by small current flow is possible in a two-dimensional electron gas, allowing for easy manipulation of the nuclear spin by simple switching of a dc current.Comment: 5 pages, 3 fig

COMPLIANCE TESTING OF IOWA’S SKID-MOUNTED SIGN DEVICE

A wide variety of traffic control devices are used in work zones, some of which are nont ormally found on the roadside or in the traveled way outsideofthe work zones. These devices are used to enhance the safety of the work zones by controlling the traffic through these areas. Due to the placement of the traffic control devices, the devices themselves may be potentially hazardous to both workers and errant vehicles. The impact performance of many work zone traffic control devices is mainly unknown and to date limited crash testing has been conducted under the criteria of National Cooperative Highway Research Program (NCHRP) Report No. 350, Recommended Procedures for the Safety Performance Evaluation of Highway Features. The objective of the study was to evaluatethe safety performance of existing skid-mounted sign supports through full- scale crash testing. Two full-scale crash tests were conducted on skid-mounted sign supports to determine their safety performance according to the Test Level 3 (TL-3) criteria set forth in the NCHRP Report No. 350. The safety performancevaluations indicate that these skid-mounted sign supports did not perform satisfactorily in the full-scale crash tests. The results of the crash tests were documented, and conclusions and recommendations pertaining tothe safety performance of the existing work zone traffic control devices were made

Graphene field-effect transistors based on boron nitride gate dielectrics

Graphene field-effect transistors are fabricated utilizing single-crystal hexagonal boron nitride (h-BN), an insulating isomorph of graphene, as the gate dielectric. The devices exhibit mobility values exceeding 10,000 cm2/V-sec and current saturation down to 500 nm channel lengths with intrinsic transconductance values above 400 mS/mm. The work demonstrates the favorable properties of using h-BN as a gate dielectric for graphene FETs.Comment: 4 pages, 8 figure

Novel Distances for Dollo Data

We investigate distances on binary (presence/absence) data in the context of a Dollo process, where a trait can only arise once on a phylogenetic tree but may be lost many times. We introduce a novel distance, the Additive Dollo Distance (ADD), which is consistent for data generated under a Dollo model, and show that it has some useful theoretical properties including an intriguing link to the LogDet distance. Simulations of Dollo data are used to compare a number of binary distances including ADD, LogDet, Nei Li and some simple, but to our knowledge previously unstudied, variations on common binary distances. The simulations suggest that ADD outperforms other distances on Dollo data. Interestingly, we found that the LogDet distance performs poorly in the context of a Dollo process, which may have implications for its use in connection with conditioned genome reconstruction. We apply the ADD to two Diversity Arrays Technology (DArT) datasets, one that broadly covers Eucalyptus species and one that focuses on the Eucalyptus series Adnataria. We also reanalyse gene family presence/absence data on bacteria from the COG database and compare the results to previous phylogenies estimated using the conditioned genome reconstruction approach

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, \textit{Liveness Analysis}, \textit{Unified Tensor Pool}, and \textit{Cost-Aware Recomputation}, all together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in those memory saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has $10^4$ basic network layers on a 12GB K40c.Comment: PPoPP '2018: 23nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programmin