9,549 research outputs found
Continuous Cluster Expansion for Field Theories
A new version of the cluster expansion is proposed without breaking the
translation and rotation invariance. As an application of this technique, we
construct the connected Schwinger functions of the regularized theory
in a continuous way
Robust quantum repeater with atomic ensembles and single-photon sources
We present a quantum repeater protocol using atomic ensembles, linear optics
and single-photon sources. Two local 'polarization' entangled states of atomic
ensembles and are generated by absorbing a single photon emitted by an
on-demand single-photon sources, based on which high-fidelity local
entanglement between four ensembles can be established efficiently through
Bell-state measurement. Entanglement in basic links and entanglement connection
between links are carried out by the use of two-photon interference. In
addition to being robust against phase fluctuations in the quantum channels,
this scheme may speed up quantum communication with higher fidelity by about 2
orders of magnitude for 1280 km compared with the partial read (PR) protocol
(Sangouard {\it et al.}, Phys. Rev. A {\bf77}, 062301 (2008)) which may
generate entanglement most quickly among the previous schemes with the same
ingredients.Comment: 5 pages 4 figure
Transfer Learning across Networks for Collective Classification
This paper addresses the problem of transferring useful knowledge from a
source network to predict node labels in a newly formed target network. While
existing transfer learning research has primarily focused on vector-based data,
in which the instances are assumed to be independent and identically
distributed, how to effectively transfer knowledge across different information
networks has not been well studied, mainly because networks may have their
distinct node features and link relationships between nodes. In this paper, we
propose a new transfer learning algorithm that attempts to transfer common
latent structure features across the source and target networks. The proposed
algorithm discovers these latent features by constructing label propagation
matrices in the source and target networks, and mapping them into a shared
latent feature space. The latent features capture common structure patterns
shared by two networks, and serve as domain-independent features to be
transferred between networks. Together with domain-dependent node features, we
thereafter propose an iterative classification algorithm that leverages label
correlations to predict node labels in the target network. Experiments on
real-world networks demonstrate that our proposed algorithm can successfully
achieve knowledge transfer between networks to help improve the accuracy of
classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201
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