6,976 research outputs found
Efficient two-step entanglement concentration for arbitrary W states
We present two two-step practical entanglement concentration protocols (ECPs)
for concentrating an arbitrary three-particle less-entangled W state into a
maximally entangled W state assisted with single photons. The first protocol
uses the linear optics and the second protocol adopts the cross-Kerr
nonlinearity to perform the protocol. In the first protocol, based on the
post-selection principle, three parties say Alice, Bob and Charlie in different
distant locations can obtain the maximally entangled W state from the arbitrary
less-entangled W state with a certain success probability. In the second
protocol, it dose not require the parties to posses the sophisticated
single-photon detectors and the concentrated photon pair can be retained after
performing this protocol successfully. Moreover, the second protocol can be
repeated to get a higher success probability. Both protocols may be useful in
practical quantum information applications.Comment: 10 pages, 4 figure
DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
A major difficulty of solving continuous POMDPs is to infer the multi-modal
distribution of the unobserved true states and to make the planning algorithm
dependent on the perceived uncertainty. We cast POMDP filtering and planning
problems as two closely related Sequential Monte Carlo (SMC) processes, one
over the real states and the other over the future optimal trajectories, and
combine the merits of these two parts in a new model named the DualSMC network.
In particular, we first introduce an adversarial particle filter that leverages
the adversarial relationship between its internal components. Based on the
filtering results, we then propose a planning algorithm that extends the
previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with
an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex
observations such as image input but also it remains highly interpretable. It
is shown to be effective in three continuous POMDP domains: the floor
positioning domain, the 3D light-dark navigation domain, and a modified Reacher
domain.Comment: IJCAI 202
Event-triggered distributed H∞ state estimation with packet dropouts through sensor networks
This study is concerned with the event-triggered distributed H∞ state estimation problem for a class of discrete-time stochastic non-linear systems with packet dropouts in a sensor network. An event-triggered communication mechanism is adopted over the sensor network with hope to reduce the communication burden and the energy consumption, where the measurements on each sensor are transmitted only when a certain triggering condition is violated. Furthermore, a novel distributed state estimator is designed where the available innovations are not only from the individual sensor, but also from its neighbouring ones according to the given topology. The purpose of the problem under consideration is to design a set of distributed state estimators such that the dynamics of estimation errors is exponentially mean-square stable and also the prespecified H∞ disturbance rejection attenuation level is guaranteed. By utilising the property of the Kronecker product and the stochastic analysis approaches, sufficient conditions are established under which the addressed state estimation problem is recast as a convex optimisation one that can be easily solved via available software packages. Finally, a simulation example is utilised to illustrate the usefulness of the proposed design scheme of event-triggered distributed state estimators.This work was supported in part by Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61203139, 61473076, 61374127 and 61422301, the Shanghai Rising-Star Program of China under Grant 13QA1400100, the ShuGuang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 13SG34, the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of Germany
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
The impressive performance of deep convolutional neural networks in
single-view 3D reconstruction suggests that these models perform non-trivial
reasoning about the 3D structure of the output space. However, recent work has
challenged this belief, showing that complex encoder-decoder architectures
perform similarly to nearest-neighbor baselines or simple linear decoder models
that exploit large amounts of per category data in standard benchmarks. On the
other hand settings where 3D shape must be inferred for new categories with few
examples are more natural and require models that generalize about shapes. In
this work we demonstrate experimentally that naive baselines do not apply when
the goal is to learn to reconstruct novel objects using very few examples, and
that in a \emph{few-shot} learning setting, the network must learn concepts
that can be applied to new categories, avoiding rote memorization. To address
deficiencies in existing approaches to this problem, we propose three
approaches that efficiently integrate a class prior into a 3D reconstruction
model, allowing to account for intra-class variability and imposing an implicit
compositional structure that the model should learn. Experiments on the popular
ShapeNet database demonstrate that our method significantly outperform existing
baselines on this task in the few-shot setting
Probing Dark Energy with the Kunlun Dark Universe Survey Telescope
Dark energy is an important science driver of many upcoming large-scale
surveys. With small, stable seeing and low thermal infrared background, Dome A,
Antarctica, offers a unique opportunity for shedding light on fundamental
questions about the universe. We show that a deep, high-resolution imaging
survey of 10,000 square degrees in \emph{ugrizyJH} bands can provide
competitive constraints on dark energy equation of state parameters using type
Ia supernovae, baryon acoustic oscillations, and weak lensing techniques. Such
a survey may be partially achieved with a coordinated effort of the Kunlun Dark
Universe Survey Telescope (KDUST) in \emph{yJH} bands over 5000--10,000 deg
and the Large Synoptic Survey Telescope in \emph{ugrizy} bands over the same
area. Moreover, the joint survey can take advantage of the high-resolution
imaging at Dome A to further tighten the constraints on dark energy and to
measure dark matter properties with strong lensing as well as galaxy--galaxy
weak lensing.Comment: 9 pages, 6 figure
Amplitude- and phase-resolved nano-spectral imaging of phonon polaritons in hexagonal boron nitride
Phonon polaritons are quasiparticles resulting from strong coupling of
photons with optical phonons. Excitation and control of these quasiparticles in
2D materials offer the opportunity to confine and transport light at the
nanoscale. Here, we image the phonon polariton (PhP) spectral response in thin
hexagonal boron nitride (hBN) crystals as a representative 2D material using
amplitude- and phase-resolved near-field interferometry with broadband mid-IR
synchrotron radiation. The large spectral bandwidth enables the simultaneous
measurement of both out-of-plane (780 cm-1) and in-plane (1370 cm-1) hBN phonon
modes. In contrast to the strong and dispersive in-plane mode, the out-of-plane
mode PhP response is weak. Measurements of the PhP wavelength reveal a
proportional dependence on sample thickness for thin hBN flakes, which can be
understood by a general model describing two-dimensional polariton excitation
in ultrathin materials
Development of Drive Control Strategy for Front-and-Rear-Motor-Drive Electric Vehicle (FRMDEV)
In order to achieve both high-efficiency drive and low-jerk mode switch in FRMDEVs, a drive control strategy is proposed, consisting of top-layer torque distribution aimed at optimal efficiency and low-layer coordination control improving mode-switch jerk. First, with the use of the off-line particle swarm optimization algorithm (PSOA), the optimal switching boundary between single-motor-drive mode (SMDM) and dual-motor drive mode (DMDM) was modelled and a real-time torque distribution model based on the radial basis function (RBF) was created to achieve the optimal torque distribution. Then, referring to the dynamic characteristics of mode switch tested on a dual-motor test bench, a torque coordination strategy by controlling the variation rate of the torque distribution coefficient during the mode-switch process was developed. Finally, based on a hardware-in-loop (HIL) test platform and an FRMDEV, the proposed drive control strategy was verified. The test results show that both drive economy and comfort were improved significantly by the use of the developed drive control strategy
Erratum: The Non-Coding RNA Journal Club: Highlights on Recent Papers-4. Non-Coding RNA 2016, 2, 9.
Please note that in the published editorial [1], affiliations 1, and 8 contained errors.[...]
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