arXiv subjects

Qiang Zheng

Publications and source records attributed to Qiang Zheng.

At least 55 records · Page 3Linked to original sources

arXiv2019

Tuning from Frustrated Magnetism to Superconductivity in Quasi-One-Dimensional KCr$_3$As$_3$ Through Hydrogen Doping

We report the charge doping of KCr$_3$As$_3$ via H intercalation. We show that the previously reported ethanol bath deintercalation of K$_2$Cr$_3$As$_3$ to KCr$_3$As$_3$ has a secondary effect whereby H from the bath enters the quasi-one-dimensional Cr$_6$As$_6$ chains. Furthermore, we find that - contrary to previous interpretations - the difference between non-superconducting as-grown KCr$_3$As$_3$ samples and superconducting hydrothermally annealed samples is not a change in crystallinity but due to charge doping, with the latter treatment increasing the H concentration in the CrAs tubes effectively electron-doping the 133 compound. These results suggest a new stoichiometry KH$_x$Cr$_3$As$_3$, that superconductivity arises from a suppressed magnetic order via a tunable parameter and pave the way for the first charge-doped phase diagram in these materials.

cond-mat.supr-con↗

arXiv2019

Long range antiferromagnetic order in a rocksalt high entropy oxide

We report for the first time the magnetic structure of the high entropy oxide $(Mg_{0.2}Co_{0.2}Ni_{0.2}Cu_{0.2}Zn_{0.2})O$ using neutron powder diffraction. This material exhibits a sluggish magnetic transition but possesses a long-range ordered antiferromagnetic ground state, as revealed by DC and AC magnetic susceptibility, elastic and inelastic neutron scattering measurements. The magnetic propagation wavevector is k=(1/2, 1/2, 1/2) based on the cubic structure Fm-3m, and the magnetic structure consists of ferromagnetic sheets in the (111) planes with spins antiparallel between two neighboring planes. Inelastic neutron scattering reveals strong magnetic excitations at 100 K that survive up to room temperature. This work demonstrates that entropy-stabilized oxides represent a unique platform to study long range magnetic order with extreme chemical disorder.

cond-mat.mtrl-sci↗

arXiv2018

Optomechanically induced nonreciprocity in a three-mode optomechanical system

We propose to create optical nonreciprocity in a three-mode optomechanical system comprising one mechanical and two optical modes, where the mechanical mode is coupled with only one of the optical modes. The optical nonreciprocal response of the system is based on the nonlinearity induced by the optomechanical interaction. However, nonlinearity is a necessary but not a sufficient condition for observing nonreciprocity. Another necessary condition for nonreciprocal response of the system to a classical driving field is demonstrated analytically. The effects of the parameters on the nonreciprocal response of the system are discussed numerically. The three-mode optomechanical system provides a platform to realize nonreciprocity for strong optical signal fields.

quant-ph↗

arXiv2018

Frustrated structural instability in superconducting quasi-one-dimensional K$_2$Cr$_3$As$_3$

We present neutron total scattering and density functional theory studies on quasi-one-dimensional superconducting K$_2$Cr$_3$As$_3$ revealing a frustrated structural instability. Our first principles calculations find a significant phonon instability which, under energy minimization, corresponds to a frustrated orthorhombic distortion. In diffraction studies we find large and temperature independent atomic displacement parameters which pair distribution analyses confirms and shows as resulting from highly localized orthorhombic distortions of the CrAs sublattice and coupled K displacements. These results suggest a far more complex phase diagram than previously assumed for this unusual superconductor with the likelihood of subtle interplays of structure, electron-phonon and magnetic interactions.

cond-mat.supr-con↗

arXiv2018

Antisite pairs suppress the thermal conductivity of BAs

BAs was predicted to have an unusually high thermal conductivity at room temperature of 2000$\,$Wm$^{-1}$$\,$K$^{-1}$, comparable to that of diamond. However, the experimentally measured thermal conductivity of BAs single crystals is an order of magnitude lower. To identify the origin of this large inconsistency, we investigated the lattice structure and potential defects in BAs single crystals at atomic scale using aberration-corrected scanning transmission electron microscopy (STEM). Rather than finding a large concentration As vacancies ($V_\mathrm{As}$), as widely thought to dominate the thermal resistance in BAs crystals, our STEM results showed enhanced intensity of some B columns and reduced intensity of some As columns, suggesting the presence of antisite defects with As$_\mathrm{B}$ (As-atom on B site) and B$_\mathrm{As}$ (B-atom on As site) with significant concentrations. Further calculations show that the antisite pair with As$_\mathrm{B}$ next to B$_\mathrm{As}$ is preferred energetically among the different types of point defects investigated, and confirm that such defects lower the thermal conductivity for BAs. Using a concentration of 6.6$\pm$3$\times$10$^{20}$$\,$cm$^{-3}$ for the antisite pairs estimated from STEM images, thermal conductivity is estimated to be 65-100$\,$Wm$^{-1}$$\,$K$^{-1}$, in reasonable agreement with our measured value. Our study suggests that As$_\mathrm{B}$-B$_\mathrm{As}$ antisite pairs are the primary lattice defects suppressing thermal conductivity of BAs. Possible approaches are proposed for growth of high quality crystals or films with high thermal conductivity.

cond-mat.mtrl-sci↗

arXiv2017

Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation

A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance.

cs.CV↗

arXiv2017

Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data

Classification of ultrasound (US) kidney images for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT) in children is a challenging task. It is desirable to improve existing pattern classification models that are built upon conventional image features. In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children. Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is adopted for transfer learning-based feature extraction from 3-channel feature maps computed from US images, including original images, gradient features, and distanced transform features. Support vector machine classifiers are then built upon different sets of features, including the transfer learning features, conventional imaging features, and their combination. Experimental results have demonstrated that the combination of transfer learning features and conventional imaging features yielded the best classification performance for distinguishing CAKUT patients from normal controls based on their US kidney images.

cs.CV↗

arXiv2017

A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images

Purpose: To improve kidney segmentation in clinical ultrasound (US) images, we develop a new graph cuts based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. Methods: To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the GC based segmentation iteratively progresses until convergence. The proposed method has been evaluated and compared with state of the art image segmentation methods based on clinical kidney US images of 85 subjects. We randomly selected US images of 20 subjects as training data for tuning the parameters, and validated the methods based on US images of the remaining 65 subjects. The segmentation results have been quantitatively analyzed using 3 metrics, including Dice Index, Jaccard Index, and Mean Distance. Results: Experiment results demonstrated that the proposed method obtained segmentation results for bilateral kidneys of 65 subjects with average Dice index of 0.9581, Jaccard index of 0.9204, and Mean Distance of 1.7166, better than other methods under comparison (p<10-19, paired Wilcoxon rank sum tests). Conclusions: The proposed method achieved promising performance for segmenting kidneys in US images, better than segmentation methods that built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such progression chronic kidney disease.

cs.CV↗

arXiv2016

Detecting macroscopic quantum coherence with a cavity optomechanical system

The rigorous resource framework of quantum coherence has been set up recently and excited a wide variety of interests. Here we show that a quantum cavity optomechanical system, as an emerging platform, can behave with a certain value of quantum coherence at a macroscopic scale. We also find that the difference between the total optomechanical coherence and the sum of the optical and the mechanical coherence just equals their mutual information. Motivated by the detection of the optomechanical entanglement, an experimentally feasible scheme to probe the optomechanical coherence is proposed.

quant-ph↗

arXiv2016

Ternary borides Nb$_7$Fe$_3$B$_8$ and Ta$_7$Fe$_3$B$_8$ with Kagome-type iron framework

Two new ternary borides $TM$$_7$Fe$_3$B$_8$ ($TM$ = Nb, Ta) were synthesized by high-temperature thermal treatment of samples obtained by arc-melting. This new type of structure with space group $P$6/$mmm$, comprises $TM$ slabs containing isolated planar hexagonal [B$_6$] rings and iron centered $TM$ columns in a Kagome type of arrangement. Chemical bonding analysis in Nb$_7$Fe$_3$B$_8$ by means of the electron localizability approach reveals two-center interactions forming the Kagome net of Fe and embedded B, while weaker multicenter bonding present between this net and Nb atoms. Magnetic susceptibility measurements reveal antiferromagnetic order below $T_N$ = 240 K for Nb$_7$Fe$_3$B$_8$ and $T_N$ =265 K for Ta$_7$Fe$_3$B$_8$. Small remnant magnetization below 0.01 $μ_B$/f.u. is observed in the antiferromagnetic state. The bulk nature of the magnetic transitions was confirmed by the hyperfine splitting of the Mößbauer spectra, the sizable anomalies in the specific heat capacity, and the kinks in the resistivity curves. The high-field paramagnetic susceptibilities fitted by the Curie-Weiss law show effective paramagnetic moments $μ_{eff}$ about 3.1 $μ_B$/Fe in both compounds. The temperature dependence of the electrical resistivity also reveals metallic character of both compounds. Density functional calculations corroborate the metallic behaviour of both compounds and demonstrate the formation of a sizable local magnetic moment on the Fe-sites. They indicate the presence of both antiferro- and ferromagnetic interactions.

cond-mat.mtrl-sci↗

arXiv2016

Approaching quantum-limited amplification with large gain catalyzed by hybrid nonlinear media in cavity optomechanics

Amplifier is at the heart of almost all experiment carrying out the precise measurement of a weak signal. An idea amplifier should have large gain and minimum added noise simultaneously. Here, we consider the quantum measurement properties of a hybrid nonlinear cavity with the Kerr and OPA nonlinear media to amplify an input signal. We show that our hybrid-nonlinear-cavity amplifier has large gain in the single-value stable regime and achieves quantum limit unconditionally.

quant-ph↗

arXiv2016

Invertible condition of quantum Fisher information matrix for a mixed qubit

Estimating multiparamter simultaneously as precise as possible is an important goal of quantum metrolo- gy. As a first step to this end, here we give a condition determining whether two arbitrary parameters can be estimated simultaneously for a qubit in the mixed state. An application of this condition is shown.

quant-ph↗

arXiv2016

Optimal quantum parameter estimation in a pulsed quantum optomechanical system

We propose that a pulsed quantum optomechanical system can be applied for the problem of quantum parameter estimation, which targets to yield higher precision of parameter estimation utilizing quantum resource than that using classical methods. Mainly concentrating on the quantum Fisher information with respect to the mechanical frequency, we find that the corresponding precision of parameter estimation on the mechanical frequency can be enhanced by applying applicable optical resonant pulsed driving on the cavity of the optome- chanical system. Further investigation shows that the mechanical squeezing resulting from the optical pulsed driving is the quantum resource used in optimal quantum estimation on the frequency.

quant-ph↗

arXiv2015

Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges

Heterogeneous Vehicular NETworks (HetVNETs) can meet various quality-of-service (QoS) requirements for intelligent transport system (ITS) services by integrating different access networks coherently. However, the current network architecture for HetVNET cannot efficiently deal with the increasing demands of rapidly changing network landscape. Thanks to the centralization and flexibility of the cloud radio access network (Cloud-RAN), soft-defined networking (SDN) can conveniently be applied to support the dynamic nature of future HetVNET functions and various applications while reducing the operating costs. In this paper, we first propose the multi-layer Cloud RAN architecture for implementing the new network, where the multi-domain resources can be exploited as needed for vehicle users. Then, the high-level design of soft-defined HetVNET is presented in detail. Finally, we briefly discuss key challenges and solutions for this new network, corroborating its feasibility in the emerging fifth-generation (5G) era.

cs.NI↗

arXiv2015

Reliable and Efficient Autonomous Driving: the Need for Heterogeneous Vehicular Networks

Autonomous driving technology has been regarded as a promising solution to reduce road accidents and traffic congestion, as well as to optimize the usage of fuel and lane. Reliable and high efficient Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications are essential to let commercial autonomous driving vehicles be on the road before 2020. The current paper firstly presents the concept of Heterogeneous Vehicular NETworks (HetVNETs) for autonomous driving, in which an improved protocol stack is proposed to satisfy the communication requirements of not only safety but also non-safety services. We then consider and study in detail several typical scenarios for autonomous driving. In order to tackle the potential challenges raised by the autonomous driving vehicles in HetVNETs, new techniques from transmission to networking are proposed as potential solutions.

cs.NI↗

arXiv2015

An SMDP-based Resource Management Scheme for Distributed Cloud Systems

In this paper, the resource management problem in geographically distributed cloud systems is considered. The Follow Me Cloud concept which enables service migration across federated data centers (DCs) is adopted. Therefore, there are two types of service requests to the DC, i.e., new requests (NRs) initiated in the local service area and migration requests (MRs) generated when mobile users move across service areas. A novel resource management scheme is proposed to help the resource manager decide whether to accept the service requests (NRs or MRs) or not and determine how much resources should be allocated to each service (if accepted). The optimization objective is to maximize the average system reward and keep the rejection probability of service requests under a certain threshold. Numerical results indicate that the proposed scheme can significantly improve the overall system utility as well as the user experience compared with other resource management schemes.

cs.NI↗

arXiv2015

Divisible Load Scheduling in Mobile Grid based on Stackelberg Pricing Game

Nowadays, it has become feasible to use mobile nodes as contributing entities in computing systems. In this paper, we consider a computational grid in which the mobile devices can share their idle resources to realize parallel processing. The overall computing task can be arbitrarily partitioned into multiple subtasks to be distributed to mobile resource providers (RPs). In this process, the computation load scheduling problem is highlighted. Based on the optimization objective, i.e., minimizing the task makespan, a buyer-seller model in which the task sponsor can inspire the SPs to share their computing resources by paying certain profits, is proposed. The Stackelberg Pricing Game (SPG) is employed to obtain the optimal price and shared resource amount of each SP. Finally, we evaluate the performance of the proposed algorithm by system simulation and the results indicate that the SPG-based load scheduling algorithm can significantly improve the time gain in mobile grid systems.

cs.DC↗

arXiv2015

Enhancing parameter precision of optimal quantum estimation by direct quantum feedback

Various schemes have been proposed to overcome the drawback of the decoherence on quantum-enhanced parameter estimation. Here we suggest an alternative method, quantum feedback, to enhance the parameter precision of optimal quantum estimation of a dissipative qubit by investigating its dynamics of quantum Fisher information. We find that compared with the case without feedback, the quantum Fisher information of the dissipative qubit in the case of feedback has a large maximum value in time evolution and a smaller decay rate in the long time.

quant-ph↗