Weakly Supervised Learning of Objects, Attributes and Their Associations

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10605-2_31]”

Transductive Multi-View Zero-Shot Learning

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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

Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning

Superconductivity mediated by the antiferromagnetic spin-wave in chalcogenide iron-base superconductors

The ground state of K$_{0.8+x}$Fe$_{1.6+y}$Se$_2$ and other iron-based selenide superconductors are doped antiferromagnetic semiconductors. There are well defined iron local moments whose energies are separated from those of conduction electrons by a large band gap in these materials. We propose that the low energy physics of this system is governed by a model Hamiltonian of interacting electrons with on-site ferromagnetic exchange interactions and inter-site superexchange interactions. We have derived the effective pairing potential of electrons under the linear spin-wave approximation and shown that the superconductivity can be driven by mediating coherent spin wave excitations in these materials. Our work provides a natural account for the coexistence of superconducting and antiferromagnetic long range orders observed by neutron scattering and other experiments.Comment: 4 pages, 3 figure

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

Effect of bilayer coupling on tunneling conductance of double-layer high T_c cuprates

Physical effects of bilayer coupling on the tunneling spectroscopy of high T$_{c}$ cuprates are investigated. The bilayer coupling separates the bonding and antibonding bands and leads to a splitting of the coherence peaks in the tunneling differential conductance. However, the coherence peak of the bonding band is strongly suppressed and broadened by the particle-hole asymmetry in the density of states and finite quasiparticle life-time, and is difficult to resolve by experiments. This gives a qualitative account why the bilayer splitting of the coherence peaks was not clearly observed in tunneling measurements of double-layer high-T$_c$ oxides.Comment: 4 pages, 3 figures, to be published in PR