Superfluid-Mott-Insulator Transition in a One-Dimensional Optical Lattice with Double-Well Potentials

We study the superfluid-Mott-insulator transition of ultracold bosonic atoms in a one-dimensional optical lattice with a double-well confining trap using the density-matrix renormalization group. At low density, the system behaves similarly as two separated ones inside harmonic traps. At high density, however, interesting features appear as the consequence of the quantum tunneling between the two wells and the competition between the "superfluid" and Mott regions. They are characterized by a rich step-plateau structure in the visibility and the satellite peaks in the momentum distribution function as a function of the on-site repulsion. These novel properties shed light on the understanding of the phase coherence between two coupled condensates and the off-diagonal correlations between the two wells.Comment: 5 pages, 7 figure

A new and finite family of solutions of hydrodynamics. Part I: Fits to pseudorapidity distributions

We highlight some of the interesting properties of a new and finite, exact family of solutions of 1 + 1 dimensional perfect fluid relativistic hydrodynamics. After reviewing the main properties of this family of solutions, we present the formulas that connect it to the measured rapidity and pseudo-rapidity densities and illustrate the results with fits to p+p collisions at 8 TeV and Pb+Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV.Comment: Invited talk of T. Csorgo at the WPCF 2018 conference in Cracow, Poland, May 22-26, 2018. Submitted to Acta Physica Polonica

Accurate determination of tensor network state of quantum lattice models in two dimensions

We have proposed a novel numerical method to calculate accurately the physical quantities of the ground state with the tensor-network wave function in two dimensions. We determine the tensor network wavefunction by a projection approach which applies iteratively the Trotter-Suzuki decomposition of the projection operator and the singular value decomposition of matrix. The norm of the wavefunction and the expectation value of a physical observable are evaluated by a coarse grain renormalization group approach. Our method allows a tensor-network wavefunction with a high bond degree of freedom (such as D=8) to be handled accurately and efficiently in the thermodynamic limit. For the Heisenberg model on a honeycomb lattice, our results for the ground state energy and the staggered magnetization agree well with those obtained by the quantum Monte Carlo and other approaches.Comment: 4 pages 5 figures 2 table

Research progress on coal mine laser methane sensor

This paper discusses the research progress of low-power technology of laser methane sensors for coal mine. On the basis of environment of coal mines, such as ultra-long-distance transmission and high stability, a series of studies have been carried out. The preliminary results have been achieved in the research of low power consumption, temperature and pressure compensation and reliability design. The technology is applied to various products in coal mines, and achieves high stability and high reliability in products such as laser methane sensor, laser methane detection alarm device, wireless laser methane detection alarm device, and optic fiber multichannel laser methane sensor. Experimental testing and analysis of the characteristics of laser methane sensors, combined with the actual application

Influence of Supercurrents on Low-Temperature Thermopower in Mesoscopic N/S Structures

The thermopower of mesoscopic normal metal/superconductor structures has been measured at low temperatures. Effect of supercurrent present in normal part of the structure was studied in two cases: when it was created by applied external magnetic field and when it was applied directly using extra superconducting electrodes. Temperature and magnetic field dependencies of thermopower are compared to the numerical simulations based on the quasiclassical theory of the superconducting proximity effect.Comment: 21 pages, 12 figures. To be published in the proceedings of the ULTI conference organized in Lammi, Finland (2006

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning.

Although reinforcement learning has been successfully applied in many domains in recent years, we still lack agents that can systematically generalize. While relational inductive biases that fit a task can improve generalization of RL agents, these biases are commonly hard-coded directly in the agent's neural architecture. In this work, we show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents. Based on this insight, we propose Grid-to-Graph (GTG), a mapping from grid structures to relational graphs that carry useful spatial relational inductive biases when processed through a Relational Graph Convolution Network (R-GCN). We show that, with GTG, R-GCNs generalize better both in terms of in-distribution and out-of-distribution compared to baselines based on Convolutional Neural Networks and Neural Logic Machines on challenging procedurally generated environments and MinAtar. Furthermore, we show that GTG produces agents that can jointly reason over observations and environment dynamics encoded in knowledge bases

From the SU(2)SU(2) Quantum Link Model on the Honeycomb Lattice to the Quantum Dimer Model on the Kagom\'e Lattice: Phase Transition and Fractionalized Flux Strings

We consider the $(2+1)$-d $SU(2)$ quantum link model on the honeycomb lattice and show that it is equivalent to a quantum dimer model on the Kagom\'e lattice. The model has crystalline confined phases with spontaneously broken translation invariance associated with pinwheel order, which is investigated with either a Metropolis or an efficient cluster algorithm. External half-integer non-Abelian charges (which transform non-trivially under the $\mathbb{Z}(2)$ center of the $SU(2)$ gauge group) are confined to each other by fractionalized strings with a delocalized $\mathbb{Z}(2)$ flux. The strands of the fractionalized flux strings are domain walls that separate distinct pinwheel phases. A second-order phase transition in the 3-d Ising universality class separates two confining phases; one with correlated pinwheel orientations, and the other with uncorrelated pinwheel orientations.Comment: 16 pages, 20 figures, 2 tables, two more relevant references and one short paragraph are adde

From the SU(2)SU(2) Quantum Link Model on the Honeycomb Lattice to the Quantum Dimer Model on the Kagom\'e Lattice: Phase Transition and Fractionalized Flux Strings

We consider the $(2+1)$-d $SU(2)$ quantum link model on the honeycomb lattice and show that it is equivalent to a quantum dimer model on the Kagom\'e lattice. The model has crystalline confined phases with spontaneously broken translation invariance associated with pinwheel order, which is investigated with either a Metropolis or an efficient cluster algorithm. External half-integer non-Abelian charges (which transform non-trivially under the $\mathbb{Z}(2)$ center of the $SU(2)$ gauge group) are confined to each other by fractionalized strings with a delocalized $\mathbb{Z}(2)$ flux. The strands of the fractionalized flux strings are domain walls that separate distinct pinwheel phases. A second-order phase transition in the 3-d Ising universality class separates two confining phases; one with correlated pinwheel orientations, and the other with uncorrelated pinwheel orientations.Comment: 16 pages, 20 figures, 2 tables, two more relevant references and one short paragraph are adde

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning.

Although reinforcement learning has been successfully applied in many domains in recent years, we still lack agents that can systematically generalize. While relational inductive biases that fit a task can improve generalization of RL agents, these biases are commonly hard-coded directly in the agent's neural architecture. In this work, we show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents. Based on this insight, we propose Grid-to-Graph (GTG), a mapping from grid structures to relational graphs that carry useful spatial relational inductive biases when processed through a Relational Graph Convolution Network (R-GCN). We show that, with GTG, R-GCNs generalize better both in terms of in-distribution and out-of-distribution compared to baselines based on Convolutional Neural Networks and Neural Logic Machines on challenging procedurally generated environments and MinAtar. Furthermore, we show that GTG produces agents that can jointly reason over observations and environment dynamics encoded in knowledge bases