15,581 research outputs found
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]”
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
Transductive Multi-View Zero-Shot Learning
(c) 2012. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms
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 KFeSe 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 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 oxides.Comment: 4 pages, 3 figures, to be published in PR
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