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

Study of Balance Equations for Hot-Electron Transport in an Arbitrary Energy Band (III)

By choosing an electron gas resting instead of drifting in the laboratory coordinate system as the initial state, the first order perturbation calculation of the previous paper (Phys. Stat. Sol. (b) 198, 785(1996)) is revised and extended to include the high order field corrections in the expression for the frictional forces and the energy transfer rates. The final expressions are formally the same as those in first order in the electric field, but the distribution functions of electrons appearing in them are defined by different expressions. The problems relative to the distribution function are discussed in detail and a new closed expression for the distribution function is obtained. The nonlinear impurity-limited resistance of a strong degenerate electron gas is computed numerically. The result calculated by using the new expression for the distribution function is quite different from that using the displaced Fermi function when the electric field is sufficiently high.Comment: 15 pages with 3 PS figures, RevTeX, to be published in Physica Status Solidi (b

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

3,6-Dibromo­naphthalene-2,7-diyl bis­(trifluoro­methane­sulfonate)

The naphthalene fused ring of the title compound, C12H4Br2F6O6S2, is slightly buckled (r.m.s. deviation = 0.036 Å) along the common C—C bond and the benzene rings are twisted by 3.2 (3)°. The two trifluoro­methyl­sulfonyl groups lie on opposite sides of the fused-ring system. The crystal structure features short inter­molecular F⋯F contacts [2.715 (4) and 2.832 (4) Å]

catena-Poly[[[diaqua­(1,10-phenanthroline-κ2 N,N′)cobalt(II)]-μ-4-hy­droxy-3-sulfonato­benzoato-κ2 O 3:O 1] sesquihydrate]

The 1,10-phenanthroline-chelated CoII atom in the polymeric title compound, {[Co(C7H4O6S)(C12H8N2)(H2O)2]·1.5H2O}n, is connected to the sulfonate O atom of one 4-hy­droxy-3-sulfonato­benzoate dianion and to the carboxyl­ate O atom of another dianion. It is also coordinated by two water mol­ecules in a trans-CoN2O4 octa­hedral environment. The dianion links adjacent metal atoms into a chain running along [110]. The chains are linked by O—H⋯O hydrogen bonds into a three-dimensional network

catena-Poly[[[diaqua­(1,10-phenanthroline-κ2 N,N′)zinc]-μ-4-hy­droxy-3-sulfonato­benzoato-κ2 O 3:O 1] sesquihydrate]

The 1,10-phenanthroline-chelated Zn atom in the polymeric title compound, {[Zn(C7H4O6S)(C12H8N2)(H2O)2]·1.5H2O}n, is connected to the sulfonate O atom of one 4-hy­droxy-3-sulfonato­benzoate dianion and to the carboxyl­ate O atom of another dianion. It is also coordinated by two water mol­ecules in an overall octa­hedral environment. The dianion links adjacent metal atoms into a chain running along [110]. The chains are linked by O—H⋯O hydrogen bonds into a three-dimensional network

Large Modeling Uncertainty in Projecting Decadal Surface Ozone Changes Over City Clusters of China

Climate policies will affect future surface ozone pollution in China. Here, we simulate changes in summertime ozone across China by 2030 under four emission scenarios reflecting different levels of climate action. We also contrast results obtained with two different chemical mechanisms employed in the chemical transport model (WRF-Chem). With emission reductions in ozone precursors introduced by climate policies, both mechanisms show promising ozone mitigation for most parts of China. However, they disagree starkly in China\u27s three main city clusters, where one mechanism projects worsening ozone pollution by 2030 despite the emission reductions. We analyze possible drivers of this important discrepancy, in particular the role of varying ozone chemical regimes affecting its sensitivity to emission changes. We recommend an intercomparison project to examine this critical modeling uncertainty among other models/mechanisms, which would be invaluable for informing local and regional emission control strategies that are based on single-model results

Online Open-set Semi-supervised Object Detection via Semi-supervised Outlier Filtering

Open-set semi-supervised object detection (OSSOD) methods aim to utilize practical unlabeled datasets with out-of-distribution (OOD) instances for object detection. The main challenge in OSSOD is distinguishing and filtering the OOD instances from the in-distribution (ID) instances during pseudo-labeling. The previous method uses an offline OOD detection network trained only with labeled data for solving this problem. However, the scarcity of available data limits the potential for improvement. Meanwhile, training separately leads to low efficiency. To alleviate the above issues, this paper proposes a novel end-to-end online framework that improves performance and efficiency by mining more valuable instances from unlabeled data. Specifically, we first propose a semi-supervised OOD detection strategy to mine valuable ID and OOD instances in unlabeled datasets for training. Then, we constitute an online end-to-end trainable OSSOD framework by integrating the OOD detection head into the object detector, making it jointly trainable with the original detection task. Our experimental results show that our method works well on several benchmarks, including the partially labeled COCO dataset with open-set classes and the fully labeled COCO dataset with the additional large-scale open-set unlabeled dataset, OpenImages. Compared with previous OSSOD methods, our approach achieves the best performance on COCO with OpenImages by +0.94 mAP, reaching 44.07 mAP