1,738 research outputs found
CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning
To accelerate software development, much research has been performed to help
people understand and reuse the huge amount of available code resources. Two
important tasks have been widely studied: code retrieval, which aims to
retrieve code snippets relevant to a given natural language query from a code
base, and code annotation, where the goal is to annotate a code snippet with a
natural language description. Despite their advancement in recent years, the
two tasks are mostly explored separately. In this work, we investigate a novel
perspective of Code annotation for Code retrieval (hence called `CoaCor'),
where a code annotation model is trained to generate a natural language
annotation that can represent the semantic meaning of a given code snippet and
can be leveraged by a code retrieval model to better distinguish relevant code
snippets from others. To this end, we propose an effective framework based on
reinforcement learning, which explicitly encourages the code annotation model
to generate annotations that can be used for the retrieval task. Through
extensive experiments, we show that code annotations generated by our framework
are much more detailed and more useful for code retrieval, and they can further
improve the performance of existing code retrieval models significantly.Comment: 10 pages, 2 figures. Accepted by The Web Conference (WWW) 201
Gapped spin liquid with -topological order for kagome Heisenberg model
We apply symmetric tensor network state (TNS) to study the nearest neighbor
spin-1/2 antiferromagnetic Heisenberg model on Kagome lattice. Our method keeps
track of the global and gauge symmetries in TNS update procedure and in tensor
renormalization group (TRG) calculation. We also introduce a very sensitive
probe for the gap of the ground state -- the modular matrices, which can also
determine the topological order if the ground state is gapped. We find that the
ground state of Heisenberg model on Kagome lattice is a gapped spin liquid with
the -topological order (or toric code type), which has a long
correlation length unit cell length. We justify that the TRG
method can handle very large systems with over thousands of spins. Such a long
explains the gapless behaviors observed in simulations on smaller systems
with less than 300 spins or shorter than 10 unit cell length. We also discuss
experimental implications of the topological excitations encoded in our
symmetric tensors.Comment: 10 pages, 7 figure
Boundary two-parameter eight-state supersymmetric fermion model and Bethe ansatz solution
The recently introduced two-parameter eight-state
supersymmetric fermion model is extended to include boundary terms. Nine
classes of boundary conditions are constructed, all of which are shown to be
integrable via the graded boundary quantum inverse scattering method. The
boundary systems are solved by using the coordinate Bethe ansatz and the Bethe
ansatz equations are given for all nine cases.Comment: 11 pages, RevTex; some typos correcte
- …