Combination of a magnetic Feshbach resonance and an optical bound-to-bound transition

We use laser light near resonant with an optical bound-to-bound transition to shift the magnetic field at which a Feshbach resonance occurs. We operate in a regime of large detuning and large laser intensity. This reduces the light-induced atom-loss rate by one order of magnitude compared to our previous experiments [D.M. Bauer et al. Nature Phys. 5, 339 (2009)]. The experiments are performed in an optical lattice and include high-resolution spectroscopy of excited molecular states, reported here. In addition, we give a detailed account of a theoretical model that describes our experimental data

Effect of Pauli repulsion and transfer on fusion

The effect of the Pauli exclusion principle on the nucleus-nucleus bare potential is studied using a new density-constrained extension of the Frozen-Hartree-Fock (DCFHF) technique. The resulting potentials exhibit a repulsion at short distance. The charge product dependence of this Pauli repulsion is investigated. Dynamical effects are then included in the potential with the density-constrained time-dependent Hartree-Fock (DCTDHF) method. In particular, isovector contributions to this potential are used to investigate the role of transfer on fusion, resulting in a lowering of the inner part of the potential for systems with positive Q-value transfer channels.Comment: Proceedings of an invited talk given at FUSION17, Hobart, Tasmania, AU (20-24 February, 2017

Remote Entanglement between a Single Atom and a Bose-Einstein Condensate

Entanglement between stationary systems at remote locations is a key resource for quantum networks. We report on the experimental generation of remote entanglement between a single atom inside an optical cavity and a Bose-Einstein condensate (BEC). To produce this, a single photon is created in the atom-cavity system, thereby generating atom-photon entanglement. The photon is transported to the BEC and converted into a collective excitation in the BEC, thus establishing matter-matter entanglement. After a variable delay, this entanglement is converted into photon-photon entanglement. The matter-matter entanglement lifetime of 100 $\mu$s exceeds the photon duration by two orders of magnitude. The total fidelity of all concatenated operations is 95%. This hybrid system opens up promising perspectives in the field of quantum information

A Planarity Test via Construction Sequences

Optimal linear-time algorithms for testing the planarity of a graph are well-known for over 35 years. However, these algorithms are quite involved and recent publications still try to give simpler linear-time tests. We give a simple reduction from planarity testing to the problem of computing a certain construction of a 3-connected graph. The approach is different from previous planarity tests; as key concept, we maintain a planar embedding that is 3-connected at each point in time. The algorithm runs in linear time and computes a planar embedding if the input graph is planar and a Kuratowski-subdivision otherwise

A continuous non-linear shadowing model of columnar growth

We propose the first continuous model with long range screening (shadowing) that described columnar growth in one space dimension, as observed in plasma sputter deposition. It is based on a new continuous partial derivative equation with non-linear diffusion and where the shadowing effects apply on all the different processes.Comment: Fast Track Communicatio

You Are Here:Geolocation by Embedding Maps and Images

We present a novel approach to geolocalising panoramic images on a 2-D cartographic map based on learning a low dimensional embedded space, which allows a comparison between an image captured at a location and local neighbourhoods of the map. The representation is not sufficiently discriminatory to allow localisation from a single image, but when concatenated along a route, localisation converges quickly, with over 90% accuracy being achieved for routes of around 200m in length when using Google Street View and Open Street Map data. The method generalises a previous fixed semantic feature based approach and achieves significantly higher localisation accuracy and faster convergence.Comment: 18 pages, new version accepted for ECCV 2020 (poster), with new results on publicly available dataset and comparison with implementation of previously published alternative approac

Near-field and far-field analysis of an azimuthally polarized slow Bloch mode microlaser

We report on the near- and far-field investigation of the slow Bloch modes associated with the G point of the Brillouin zone, for a honeycomb lattice photonic crystal, using near-field scanning optical microscopy (NSOM) and infra-red CCD camera. The array of doughnut-shaped monopolar mode (mode M) inside each unit cell, predicted previously by numerical simulation, is experimentally observed in the near-field by means of a metal-coated NSOM tip. In far-field, we detect the azimuthal polarization of the doughnut laser beam due to destructive and constructive interference of the mode radiating from the surface (mode TEM01*). A divergence of 2° for the laser beam and a mode size of (12.8 ± 1) μm for the slow Bloch mode at the surface of the crystal are also estimated. © 2010 Optical Society of America

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

Synthesis of Single Phase Hg-1223 High Tc Superconducting Films With Multistep Electrolytic Process

We report the multistep electrolytic process for the synthesis of high Tc single phase HgBa2Ca2Cu3O8+ (Hg-1223) superconducting films. The process includes : i) deposition of BaCaCu precursor alloy, ii) oxidation of BaCaCu films, iii) electrolytic intercalation of Hg in precursor BaCaCuO films and iv) electrochemical oxidation and annealing of Hg-intercalated BaCaCuO films to convert into Hg1Ba2Ca2Cu3O8+ (Hg-1223). Films were characterized by thermo-gravimetric analysis (TGA) and differential thermal analysis (DTA), X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electrolytic intercalation of Hg in BaCaCuO precursor is proved to be a novel alternative to high temperature-high pressure mercuration process. The films are single phase Hg-1223 with Tc = 121.5 K and Jc = 4.3 x 104 A/cm2.Comment: 17 Pages, 10 Figures. Submitted to Superconductor Science and Technolog