66,358 research outputs found
Quantum nondemolition measurements of a flux qubit coupled to a noisy detector
We theoretically study the measurement-induced dephasing caused by back
action noise in quantum nondemolition measurements of a superconducting flux
qubit which is coupled to a superconducting quantum interference device
(SQUID). Our analytical results indicate that information on qubit flows from
qubit to detector, while quantum fluctuations which may cause dephasing of the
qubit also inject to qubit. Furthermore, the measurement probability is
frequency dependent in a short time scale and has a close relationship with the
measurement-induced dephasing. When the detuning between driven and bare
resonator equals coupling strength, we will access the state of qubit more
easily. In other words, we obtain the maximum measurement rate. Finally, we
analyzed mixed effect caused by coupling between non-diagonal term and external
variable. We found that the initial information of qubit is destroyed due to
quantum tunneling between the qubit states.Comment: 6 pages, 3 figure
Thermoelectric effect in a parallel double quantum dot structure
We discuss the thermoelectric properties assisted by the Fano effect of a
parallel double quantum dot (QD) structure. By adjusting the couplings between
the QDs and leads, we facilitate the nonresonant and resonant channels for the
Fano interference. It is found that at low temperature, Fano lineshapes appear
in the electronic and thermal conductance spectra, which can also be reversed
by an applied local magnetic flux with its phase factor . And, the
Fano effect contributes decisively to the enhancement of thermoelectric
efficiency. However, at the same temperature, the thermoelectric effect in the
case of is much more apparent, compared with the case of zero
magnetic flux. By the concept of Feynman path, we analyze the difference
between the quantum interferences in the cases of and . It
is seen that in the absence of magnetic flux the Fano interference originates
from the quantum interference among infinite-order Feynman paths, but it occurs
only between two lowest-order Feynman paths when . The increase of
temperature inevitably destroys the electron coherent transmission in each
paths. So, in the case of zero magnetic field, the thermoelectric effect
contributed by the Fano interference is easy to weaken by a little increase of
temperature.Comment: 8 pages, 4 figure
Zhu's Algebra of a C1-cofinite Vertex Algebra
For a C1-cofinite vertex algebra V, we give an efficient way to calculate
Zhu's algebra A(V) of V with respect to its C1-generators and relations. We use
two examples to explain how this method works
DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block
For the self-driving and automatic parking, perception is the basic and
critical technique, moreover, the detection of lane markings and parking slots
is an important part of visual perception. In this paper, we use the semantic
segmentation method to segment the area and classify the class of lane makings
and parking slots on panoramic surround view (PSV) dataset. We propose the
DFNet and make two main contributions, one is dynamic loss weights, and the
other is residual fusion block (RFB). Dynamic loss weights are varying from
classes, calculated according to the pixel number of each class in a batch. RFB
is composed of two convolutional layers, one pooling layer, and a fusion layer
to combine the feature maps by pixel multiplication. We evaluate our method on
PSV dataset, and achieve an advanced result.Comment: 6 pages,3 figure
Implicit temporal discretization and exact energy conservation for particle methods applied to the Poisson-Boltzmann equation
Wereportonanewmultiscalemethodapproachforthestudyofsystemswith wide
separation of short-range forces acting on short time scales and long-range
forces acting on much slower scales. We consider the case of the
Poisson-Boltzmann equation that describes the long-range forces using the
Boltzmann formula (i.e. we assume the medium to be in quasi local thermal
equilibrium). We developed a new approach where fields and particle information
(mediated by the equations for their moments) are solved self-consistently. The
new approach is implicit and numerically stable, providing exact energy
conservation. We tested different implementations all leading to exact energy
conservation. The new method requires the solution of a large set of non-linear
equations. We considered three solution strategies: Jacobian Free Newton
Krylov, an alternative, called field hiding, based on hiding part of the
residual calculation and replacing them with direct solutions and a Direct
Newton Schwarz solver that considers simplified single particle-based Jacobian.
The field hiding strategy proves to be the most efficient approach
Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies
In genome-wide association studies (GWASs) of common diseases/traits, we
often analyze multiple GWASs with the same phenotype together to discover
associated genetic variants with higher power. Since it is difficult to access
data with detailed individual measurements, summary-statistics-based
meta-analysis methods have become popular to jointly analyze data sets from
multiple GWASs. In this paper, we propose a novel summary-statistics-based
joint analysis method based on controlling the joint local false discovery rate
(Jlfdr). We prove that our method is the most powerful summary-statistics-based
joint analysis method when controlling the false discovery rate at a certain
level. In particular, the Jlfdr-based method achieves higher power than
commonly used meta-analysis methods when analyzing heterogeneous data sets from
multiple GWASs. Simulation experiments demonstrate the superior power of our
method over meta-analysis methods. Also, our method discovers more associations
than meta-analysis methods from empirical data sets of four phenotypes. The
R-package is available at: http://bioinformatics.ust.hk/Jlfdr.html
A Seq-to-Seq Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation
The prevalent approaches of Chinese word segmentation task almost rely on the
Bi-LSTM neural network. However, the methods based the Bi-LSTM have some
inherent drawbacks: hard to parallel computing, little efficient in applying
the Dropout method to inhibit the Overfitting and little efficient in capturing
the character information at the more distant site of a long sentence for the
word segmentation task. In this work, we propose a sequence-to-sequence
transformer model for Chinese word segmentation, which is premised a type of
convolutional neural network named temporal convolutional network. The model
uses the temporal convolutional network to construct an encoder, and uses one
layer of fully-connected neural network to build a decoder, and applies the
Dropout method to inhibit the Overfitting, and captures the character
information at the distant site of a sentence by adding the layers of the
encoder, and binds Conditional Random Fields model to train parameters, and
uses the Viterbi algorithm to infer the final result of the Chinese word
segmentation. The experiments on traditional Chinese corpora and simplified
Chinese corpora show that the performance of Chinese word segmentation of the
model is equivalent to the performance of the methods based the Bi-LSTM, and
the model has a tremendous growth in parallel computing than the models based
the Bi-LSTM
A finite element method of the self-consistent field theory on general curved surfaces
Block copolymers provide a wonderful platform in studying the soft condensed
matter systems. Many fascinating ordered structures have been discovered in
bulk and confined systems. Among various theories, the self-consistent field
theory (SCFT) has been proven to be a powerful tool for studying the
equilibrium ordered structures. Many numerical methods have been developed to
solve the SCFT model. However, most of these focus on the bulk systems, and
little work on the confined systems, especially on general curved surfaces. In
this work, we developed a linear surface finite element method, which has a
rigorous mathematical theory to guarantee numerical precsion, to study the
self-assembled phases of block copolymers on general curved surfaces based on
the SCFT. Furthermore, to capture the consistent surface for a given
self-assembled pattern, an adaptive approach to optimize the size of the
general curved surface has been proposed. To demonstrate the power of this
approach, we investigate the self-assembled patterns of diblock copolymers on
several distinct curved surfaces, including five closed surfaces and an
unclosed surface. Numerical results illustrate the efficiency of the proposed
method. The obtained ordered structures are consistent with the previous
results on standard surfaces, such as sphere and torus. Certainly, the proposed
numerical framework has the capability of studying the phase behaviors on
general surfaces precisely
Motion Planning for a Humanoid Mobile Manipulator System
A high redundant non-holonomic humanoid mobile dual-arm manipulator system is
presented in this paper where the motion planning to realize "human-like"
autonomous navigation and manipulation tasks is studied. Firstly, an improved
MaxiMin NSGA-II algorithm, which optimizes five objective functions to solve
the problems of singularity, redundancy, and coupling between mobile base and
manipulator simultaneously, is proposed to design the optimal pose to
manipulate the target object. Then, in order to link the initial pose and that
optimal pose, an off-line motion planning algorithm is designed. In detail, an
efficient direct-connect bidirectional RRT and gradient descent algorithm is
proposed to reduce the sampled nodes largely, and a geometric optimization
method is proposed for path pruning. Besides, head forward behaviors are
realized by calculating the reasonable orientations and assigning them to the
mobile base to improve the quality of human-robot interaction. Thirdly, the
extension to on-line planning is done by introducing real-time sensing,
collision-test and control cycles to update robotic motion in dynamic
environments. Fourthly, an EEs' via-point-based multi-objective genetic
algorithm is proposed to design the "human-like" via-poses by optimizing four
objective functions. Finally, numerous simulations are presented to validate
the effectiveness of proposed algorithms.Comment: 26 pages, 9 figure
Anisotropic Characteristic Lengths of Colloidal Monolayers Near a Water-Air Interface
Near-interface colloidal monolayers have often been used as model systems for
research on hydrodynamics in biophysics and microfluidic systems. Using optical
microscopy and multiparticle tracking techniques, the correlated diffusion of
particles is experimentally measured in colloidal monolayers near a water-air
interface. It is found that the characteristic lengths X1 and X2 of such a
colloidal monolayer are anisotropic in these two perpendicular directions. The
former (X1)is equal to the Saffman length of the monolayer and reflects the
continuous nature of the system in the longitudinal direction. The latter
(X2)is a function of both the Saffman length and the radius of the colloids and
reflects the discrete nature of the system in the transverse direction. This
discovery demonstrates that the hydrodynamics intrinsically follow different
rules in these two directions in this system
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