65,685 research outputs found

    Quantum nondemolition measurements of a flux qubit coupled to a noisy detector

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    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

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    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 ϕ=π\phi=\pi. And, the Fano effect contributes decisively to the enhancement of thermoelectric efficiency. However, at the same temperature, the thermoelectric effect in the case of ϕ=π\phi=\pi 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 ϕ=0\phi=0 and ϕ=π\phi=\pi. 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 ϕ=π\phi=\pi. 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

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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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