4,094 research outputs found
Lookahead Strategies for Sequential Monte Carlo
Based on the principles of importance sampling and resampling, sequential
Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with
complex stochastic dynamic systems. Many of these systems possess strong
memory, with which future information can help sharpen the inference about the
current state. By providing theoretical justification of several existing
algorithms and introducing several new ones, we study systematically how to
construct efficient SMC algorithms to take advantage of the "future"
information without creating a substantially high computational burden. The
main idea is to allow for lookahead in the Monte Carlo process so that future
information can be utilized in weighting and generating Monte Carlo samples, or
resampling from samples of the current state.Comment: Published in at http://dx.doi.org/10.1214/12-STS401 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
In this work, we develop a simple algorithm for semi-supervised regression.
The key idea is to use the top eigenfunctions of integral operator derived from
both labeled and unlabeled examples as the basis functions and learn the
prediction function by a simple linear regression. We show that under
appropriate assumptions about the integral operator, this approach is able to
achieve an improved regression error bound better than existing bounds of
supervised learning. We also verify the effectiveness of the proposed algorithm
by an empirical study.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Optical transitions between Landau levels: AA-stacked bilayer graphene
The low-frequency optical excitations of AA-stacked bilayer graphene are
investigated by the tight-binding model. Two groups of asymmetric LLs lead to
two kinds of absorption peaks resulting from only intragroup excitations. Each
absorption peak obeys a single selection rule similar to that of monolayer
graphene. The excitation channel of each peak is changed as the field strength
approaches a critical strength. This alteration of the excitation channel is
strongly related to the setting of the Fermi level. The peculiar optical
properties can be attributed to the characteristics of the LL wave functions of
the two LL groups. A detailed comparison of optical properties between
AA-stacked and AB-stacked bilayer graphenes is also offered. The compared
results demonstrate that the optical properties are strongly dominated by the
stacking symmetry. Furthermore, the presented results may be used to
discriminate AABG from MG, which can be hardly done by STM
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