5,405 research outputs found
Nonlinear Feynman-Kac formulae for SPDEs with space-time noise
We study a class of backward doubly stochastic differential equations
(BDSDEs) involving martingales with spatial parameters, and show that they
provide probabilistic interpretations (Feynman-Kac formulae) for certain
semilinear stochastic partial differential equations (SPDEs) with space-time
noise. As an application of the Feynman-Kac formulae, random periodic solutions
and stationary solutions to certain SPDEs are obtained
Cooling a charged mechanical resonator with time-dependent bias gate voltages
We show a purely electronic cooling scheme to cool a charged mechanical
resonator (MR) down to nearly the vibrational ground state by elaborately
tuning bias gate voltages on the electrodes, which couple the MR by Coulomb
interaction. The key step is the modification of time-dependent effective
eigen-frequency of the MR based on the Lewis-Riesenfeld invariant. With respect
to a relevant idea proposed previously [Li et al., Phys. Rev. A 83, 043803
(2011)], our scheme is simpler, more practical and completely within the reach
of current technology.Comment: 9 pages,7 figures, accepted by J.Phys: Cond.Matt (Fast track
communication
Fast optical cooling of a nanomechanical cantilever by a dynamical Stark-shift gate
The efficient cooling of the nanomechanical resonators is essential to
exploration of quantum properties of the macroscopic or mesoscopic systems. We
propose such a laser-cooling scheme for a nanomechanical cantilever, which
works even for the low-frequency mechanical mode and under weak cooling lasers.
The cantilever is attached by a diamond nitrogen-vacancy center under a strong
magnetic field gradient and the cooling is assisted by a dynamical Stark-shift
gate. Our scheme can effectively enhance the desired cooling efficiency by
avoiding the off-resonant and unexpected carrier transitions, and thereby cool
the cantilever down to the vicinity of the vibrational ground state in a fast
fashion.Comment: 14 pages, 7 figure
Coupling Tension and Shear for Highly Sensitive Graphene-Based Strain Sensors
We report, based on its variation in electronic transport to coupled tension
and shear deformation, a highly sensitive graphene-based strain sensor
consisting of an armchair graphene nanoribbon (AGNR) between metallic contacts.
As the nominal strain at any direction increases from 2.5 to 10%, the
conductance decreases, particularly when the system changes from the
electrically neutral region. At finite bias voltage, both the raw conductance
and the relative proportion of the conductance depends smoothly on the gate
voltage with negligible fluctuations, which is in contrast to that of pristine
graphene. Specifically, when the nominal strain is 10% and the angle varies
from 0 degree to 90 degree, the relative proportion of the conductance changes
from 60 to 90%.Comment: 4 pages, 3 figure
An efficient cooling of the quantized vibration by a four-level configuration
Cooling vibrational degrees of freedom down to ground states is essential to
observation of quantum properties of systems with mechanical vibration. We
propose two cooling schemes employing four internal levels of the systems,
which achieve the ground-state cooling in an efficient fashion by completely
deleting the carrier and first-order blue-sideband transitions. The schemes,
based on the quantum interference and Stark-shift gates, are robust to
fluctuation of laser intensity and frequency. The feasibility of the schemes is
justified using current laboratory technology. In practice, our proposal
readily applies to an nano-diamond nitrogen-vacancy center levitated in an
optic trap or attached to a cantilever.Comment: 6 page, 4 figure
Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement
The number of social images has exploded by the wide adoption of social
networks, and people like to share their comments about them. These comments
can be a description of the image, or some objects, attributes, scenes in it,
which are normally used as the user-provided tags. However, it is well-known
that user-provided tags are incomplete and imprecise to some extent. Directly
using them can damage the performance of related applications, such as the
image annotation and retrieval. In this paper, we propose to learn an image
annotation model and refine the user-provided tags simultaneously in a
weakly-supervised manner. The deep neural network is utilized as the image
feature learning and backbone annotation model, while visual consistency,
semantic dependency, and user-error sparsity are introduced as the constraints
at the batch level to alleviate the tag noise. Therefore, our model is highly
flexible and stable to handle large-scale image sets. Experimental results on
two benchmark datasets indicate that our proposed model achieves the best
performance compared to the state-of-the-art methods.Comment: AAAI-201
Practical and fast quantum random number generation based on photon arrival time relative to external reference
We present a practical high-speed quantum random number generator, where the
timing of single-photon detection relative to an external time reference is
measured as the raw data. The bias of the raw data can be substantially reduced
compared with the previous realizations. The raw random bit rate of our
generator can reach 109 Mbps. We develop a model for the generator and evaluate
the min-entropy of the raw data. Toeplitz matrix hashing is applied for
randomness extraction, after which the final random bits are able to pass the
standard randomness tests.Comment: 9 pages, 3 figures, 1 table. Accepted for publication in Applied
Physics Letter
Bidirectional and tunable single-photons multi-channel quantum router between microwave and optical light
Routing of photon play a key role in optical communication and quantum
networks. Although the quantum routing of signals has been investigated in
various systems both in theory and experiment. However, no current theory can
route quantum signals between microwave and optical light. Here, we propose an
experimentally accessible tunable multi-channel quantum routing proposal using
photon-phonon translation in a hybrid opto-electromechanical system. It is the
first demonstration that the single-photon of optical frequency can be routed
into three different output ports by adjusting microwave power. More important,
the two output signals can be selected according to microwave power. Meanwhile,
we also demonstrate the vacuum and thermal noise will be insignificant for the
optical performance of the single-photon router at temperature of the order of
20 mK. Our proposal may have paved a new avenue towards multi-channel router
and quantum network.Comment: arXiv admin note: text overlap with arXiv:1109.4361 by other author
Generating the Schrodinger cat state in a nanomechanical resonator coupled to a charge qubit
We propose a scheme for generating the Schr\"{o}dinger cat state based on
geometric operations by a nanomechanical resonator coupled to a superconducting
charge qubit. The charge qubit, driven by two strong classical fields,
interacts with a high-frequency phonon mode of the nanomechanical resonator.
During the operation, the charge qubit undergoes no real transitions, while the
phonon mode of the nanomechanical resonator is displaced along different paths
in the phase space, dependent on the states of the charge qubit, which yields
the Schr\"{o}dinger cat state. The robustness of the scheme is justified by
considering noise from environment, and the feasibility of the scheme is
discussed.Comment: 6 pages 3 figure
Testing dark energy models with data
is a diagnostic approach to distinguish dark energy models. However,
there are few articles to discuss what is the distinguishing criterion. In this
paper, firstly we smooth the latest observational data using a
model-independent method -- Gaussian processes, and then reconstruct the
and its fist order derivative . Such
reconstructions not only could be the distinguishing criteria, but also could
be used to estimate the authenticity of models. We choose some popular models
to study, such as CDM, generalized Chaplygin gas (GCG) model,
Chevallier-Polarski-Linder (CPL) parametrization and Jassal-Bagla-Padmanabhan
(JBP) parametrization. We plot the trajectories of and
with confidence level of these models, and
compare them to the reconstruction from data set. The result indicates
that the data does not favor the CPL and JBP models at
confidence level. Strangely, in high redshift range, the reconstructed
has a tendency of deviation from theoretical value, which
demonstrates these models are disagreeable with high redshift data. This
result supports the conclusions of Sahni et al. \citep{sahni2014model} and Ding
et al. \citep{ding2015there} that the CDM may not be the best
description of our universe
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