8,492 research outputs found
Domain wall space-times with a cosmological constant
We solve vacuum Einstein's field equations with the cosmological constant in
space-times admitting 3-parameter group of isometries with 2-dimensional
space-like orbits. The general exact solutions, which are represented in the
advanced and retarded null coordinates, have two arbitrary functions due to the
freedom of choosing null coordinates. In the thin-wall approximation, the
Israel's junction conditions yield one constraint equation on these two
functions in spherical, planar, and hyperbolic domain wall space-times with
reflection symmetry. The remain freedom of choosing coordinates are completely
fixed by requiring that when surface energy density of domain walls
vanishes, the metric solutions will return to some well-known solutions. It
leads us to find a planar domain wall solution, which is conformally flat, in
the de Sitter universe.Comment: 9 pages. no figur
Spin-valley qubit in nanostructures of monolayer semiconductors: Optical control and hyperfine interaction
We investigate the optical control possibilities of spin-valley qubit carried
by single electrons localized in nanostructures of monolayer TMDs, including
small quantum dots formed by lateral heterojunction and charged impurities. The
quantum controls are discussed when the confinement induces valley
hybridization and when the valley hybridization is absent. We show that the
bulk valley and spin optical selection rules can be inherited in different
forms in the two scenarios, both of which allow the definition of spin-valley
qubit with desired optical controllability. We also investigate nuclear spin
induced decoherence and quantum control of electron-nuclear spin entanglement
via intervalley terms of the hyperfine interaction. Optically controlled
two-qubit operations in a single quantum dot are discussed.Comment: 17pages, 10 figure
Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications
This work concerns receiver design for light emitting diode (LED)
communications where the LED nonlinearity can severely degrade the performance
of communications. We propose extreme learning machine (ELM) based
non-iterative receivers and iterative receivers to effectively handle the LED
nonlinearity and memory effects. For the iterative receiver design, we also
develop a data-aided receiver, where data is used as virtual training sequence
in ELM training. It is shown that the ELM based receivers significantly
outperform conventional polynomial based receivers; iterative receivers can
achieve huge performance gain compared to non-iterative receivers; and the
data-aided receiver can reduce training overhead considerably. This work can
also be extended to radio frequency communications, e.g., to deal with the
nonlinearity of power amplifiers
Quantum-trajectory analysis for charge transfer in solid materials induced by strong laser fields
We investigate the dependence of charge transfer on the intensity of driving
laser field when SiO2 crystal is irradiated by an 800 nm laser. It is
surprising that the direction of charge transfer undergoes a sudden reversal
when the driving laser intensity exceeds critical values with different carrier
envelope phases. By applying quantum-trajectory analysis, we find that the
Bloch oscillation plays an important role in charge transfer in solid. Also, we
study the interaction of strong laser with gallium nitride (GaN) that is widely
used in optoelectronics. A pump-probe scheme is applied to control the quantum
trajectories of the electrons in the conduction band. The signal of charge
transfer is controlled successfully by means of theoretically proposed
approach
Projected Density Matrix Embedding Theory with Applications to the Two-Dimensional Hubbard Model
Density matrix embedding theory (DMET) is a quantum embedding theory for
strongly correlated systems. From a computational perspective, one bottleneck
in DMET is the optimization of the correlation potential to achieve
self-consistency, especially for heterogeneous systems of large size. We
propose a new method, called projected density matrix embedding theory
(p-DMET), which achieves self-consistency without needing to optimize a
correlation potential. We demonstrate the performance of p-DMET on the
two-dimensional Hubbard model.Comment: 25 pages, 8 figure
Geometry of the set of quantum correlations
It is well known that correlations predicted by quantum mechanics cannot be
explained by any classical (local-realistic) theory. The relative strength of
quantum and classical correlations is usually studied in the context of Bell
inequalities, but this tells us little about the geometry of the quantum set of
correlations. In other words, we do not have good intuition about what the
quantum set actually looks like. In this paper we study the geometry of the
quantum set using standard tools from convex geometry. We find explicit
examples of rather counter-intuitive features in the simplest non-trivial Bell
scenario (two parties, two inputs and two outputs) and illustrate them using
2-dimensional slice plots. We also show that even more complex features appear
in Bell scenarios with more inputs or more parties. Finally, we discuss the
limitations that the geometry of the quantum set imposes on the task of
self-testing.Comment: 11 + 8 pages, 6 figures, v2: added an argument relating self-testing
and extremality, v3: typos corrected, results unchanged, published versio
A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint
3D shape editing is widely used in a range of applications such as movie
production, computer games and computer aided design. It is also a popular
research topic in computer graphics and computer vision. In past decades,
researchers have developed a series of editing methods to make the editing
process faster, more robust, and more reliable. Traditionally, the deformed
shape is determined by the optimal transformation and weights for an energy
term. With increasing availability of 3D shapes on the Internet, data-driven
methods were proposed to improve the editing results. More recently as the deep
neural networks became popular, many deep learning based editing methods have
been developed in this field, which is naturally data-driven. We mainly survey
recent research works from the geometric viewpoint to those emerging neural
deformation techniques and categorize them into organic shape editing methods
and man-made model editing methods. Both traditional methods and recent neural
network based methods are reviewed
Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement
In this paper, we propose a 2-stage low-light image enhancement method called
Self-Reference Deep Adaptive Curve Estimation (Self-DACE). In the first stage,
we present an intuitive, lightweight, fast, and unsupervised luminance
enhancement algorithm. The algorithm is based on a novel low-light enhancement
curve that can be used to locally boost image brightness. We also propose a new
loss function with a simplified physical model designed to preserve natural
images' color, structure, and fidelity. We use a vanilla CNN to map each pixel
through deep Adaptive Adjustment Curves (AAC) while preserving the local image
structure. Secondly, we introduce the corresponding denoising scheme to remove
the latent noise in the darkness. We approximately model the noise in the dark
and deploy a Denoising-Net to estimate and remove the noise after the first
stage. Exhaustive qualitative and quantitative analysis shows that our method
outperforms existing state-of-the-art algorithms on multiple real-world
datasets
- …