Controlled Heterogeneous Nucleation and Growth of Germanium Quantum Dots on Nanopatterned Silicon Dioxide and Silicon Nitride Substrates

Controlled heterogeneous nucleation and growth of Ge quantum dots (QDs) are demonstrated on SiO_2/Si_3N_4 substrates by means of a novel fabrication process of thermally oxidizing nanopatterned SiGe layers. The otherwise random self-assembly process for QDs is shown to be strongly influenced by the nanopatterning in determining both the location and size of the QDs. Ostwald ripening processes are observed under further annealing at the oxidation temperature. Both nanopattern oxidation and Ostwald ripening offer additional mechanisms for lithography for controlling the size and placement of the QDs

Stationary Light Pulses in Cold Atomic Media

Stationary light pulses (SLPs), i.e., light pulses without motion, are formed via the retrieval of stored probe pulses with two counter-propagating coupling fields. We show that there exist non-negligible hybrid Raman excitations in media of cold atoms that prohibit the SLP formation. We experimentally demonstrate a method to suppress these Raman excitations and realize SLPs in laser-cooled atoms. Our work opens the way to SLP studies in cold as well as in stationary atoms and provides a new avenue to low-light-level nonlinear optics.Comment: 4 pages, 4 figure

Effects of Vanadium doping on BaFe2As2

We report an investigation of the structural, magnetic and electronic properties of Ba(Fe(1-x)V(x))2As2 using x-ray, transport, magnetic susceptibility and neutron scattering measurements. The vanadium substitutions in Fe sites are possible up to 40\%. Hall effect measurements indicate strong hole-doping effect through V doping, while no superconductivity is observed in all samples down to 2K. The antiferromagnetic and structural transition temperature of BaFe2As2 is gradually suppressed to finite temperature then vanishes at x=0.245 with the emergence of spin glass behavior, suggesting an avoided quantum critical point (QCP). Our results demonstrate that the avoided QCP and spin glass state which were previously reported in the superconducting phase of Co/Ni-doped BaFe2As2 can also be realized in non-superconducting Ba(Fe(1-x)V(x))2As2.Comment: 5 pages, 6 figure

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training