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 H(z)H(z) data

$Om(z)$ 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 $H(z)$ data using a model-independent method -- Gaussian processes, and then reconstruct the $Om(z)$ and its fist order derivative $\mathcal{L}^{(1)}_m$. 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 $\Lambda$CDM, generalized Chaplygin gas (GCG) model, Chevallier-Polarski-Linder (CPL) parametrization and Jassal-Bagla-Padmanabhan (JBP) parametrization. We plot the trajectories of $Om(z)$ and $\mathcal{L}^{(1)}_m$ with $1 \sigma$ confidence level of these models, and compare them to the reconstruction from $H(z)$ data set. The result indicates that the $H(z)$ data does not favor the CPL and JBP models at $1 \sigma$ confidence level. Strangely, in high redshift range, the reconstructed $\mathcal{L}^{(1)}_m$ has a tendency of deviation from theoretical value, which demonstrates these models are disagreeable with high redshift $H(z)$ data. This result supports the conclusions of Sahni et al. \citep{sahni2014model} and Ding et al. \citep{ding2015there} that the $\Lambda$CDM may not be the best description of our universe