arXiv subjects

Ying Liu

Publications and source records attributed to Ying Liu.

At least 235 records · Page 13Linked to original sources

arXiv2020

Fast library-driven approach for implementation of the voxel spread function technique for correcting magnetic field inhomogeneity artifacts

Purpose: Previously-developed Voxel Spread Function (VSF) method (Yablonskiy, et al, MRM, 2013;70:1283) provides means to correct artifacts induced by macroscopic magnetic field inhomogeneities in the images obtained by multi-Gradient-Recalled-Echo (mGRE) techniques. The goal of this study is to develop a library-driven approach for fast VSF implementation. Methods: The VSF approach describes the contribution of the magnetic field inhomogeneity effects on the mGRE signal decay in terms of the F-function calculated from mGRE phase and magnitude images. A pre-calculated library accounting for a variety of background field gradients caused by magnetic field inhomogeneities was used herein to speed up calculation of the F-function and to generate quantitative R2* maps from the mGRE data collected from two healthy volunteers. Results: As compared with direct calculation of the F-function based on a voxel-wise approach, the new library-driven method substantially reduces computational time from several hours to few minutes, while, at the same time, providing similar accuracy of R2* mapping. Conclusion: The new procedure proposed in this study provides a fast post-processing algorithm that can be incorporated in the quantitative analysis of mGRE data to account for background field inhomogeneity artifacts, thus can facilitate the applications of mGRE-based quantitative techniques in clinical practices.

q-bio.QM↗

arXiv2020

Two-dimensional antiferromagnetic Dirac fermions in monolayer TaCoTe$_2$

Dirac point in two-dimensional (2D) materials has been a fascinating subject of research. Recently, it has been theoretically predicted that Dirac point may also be stabilized in 2D magnetic systems. However, it remains a challenge to identify concrete 2D materials which host such magnetic Dirac point. Here, based on first-principles calculations and theoretical analysis, we propose a stable 2D material, the monolayers TaCoTe$_2$, as an antiferromagnetic (AFM) 2D Dirac material. We show that it has an AFM ground state with an out-of-plane Néel vector. It hosts a pair of 2D AFM Dirac points on the Fermi level in the absence of spin-orbit coupling (SOC). When the SOC is considered, a small gap is opened at the original Dirac points. Meanwhile, another pair of Dirac points appear on the Brillouin zone boundary below the Fermi level, which are robust under SOC and have a type-II dispersion. Such a type-II AFM Dirac point has not been observed before. We further show that the location of this Dirac point as well as its dispersion type can be controlled by tuning the Néel vector orientation.

cond-mat.mtrl-sci↗

arXiv2020

Graph-Fused Multivariate Regression via Total Variation Regularization

In this paper, we propose the Graph-Fused Multivariate Regression (GFMR) via Total Variation regularization, a novel method for estimating the association between a one-dimensional or multidimensional array outcome and scalar predictors. While we were motivated by data from neuroimaging and physical activity tracking, the methodology is designed and presented in a generalizable format and is applicable to many other areas of scientific research. The estimator is the solution of a penalized regression problem where the objective is the sum of square error plus a total variation (TV) regularization on the predicted mean across all subjects. We propose an algorithm for parameter estimation, which is efficient and scalable in a distributed computing platform. Proof of the algorithm convergence is provided, and the statistical consistency of the estimator is presented via an oracle inequality. We present 1D and 2D simulation results and demonstrate that GFMR outperforms existing methods in most cases. We also demonstrate the general applicability of the method by two real data examples, including the analysis of the 1D accelerometry subsample of a large community-based study for mood disorders and the analysis of the 3D MRI data from the attention-deficient/hyperactive deficient (ADHD) 200 consortium.

stat.ME↗

arXiv2019

Super-Andreev reflection and longitudinal shift of pseudospin-one fermions

Novel fermions with a pseudospin-1 structure can be realized as emergent quasiparticles in condensed matter systems. Here, we investigate its unusual properties during the Andreev reflection at a normal-metal/superconductor (NS) interface. We show that distinct from the previously studied pseudospin-1/2 and two dimensional electron gas models, the pseudospin-1 fermions exhibit a strongly enhanced Andreev reflection probability, and remarkably, can be further tuned to approach perfect Andreev reflection with unit efficiency for all incident angles, exhibiting a previously unknown {super-Andreev reflection effect}. The super-Andreev reflection leads to perfect transparency of the NS interface that strongly promotes charge injection into the superconductor, and directly manifests as a differential conductance peak which can be readily probed in experiment. Additionally, we find that sizable longitudinal shifts exist in the normal and Andreev reflections of pseudospin-1 fermions. Distinct from the pseudospin-1/2 case, the shift is always in the forward direction in the subgap regime, regardless of whether the reflection is of retro- or specular type.

cond-mat.mes-hall↗

arXiv2019

Pressure-induced superconductivity and structural transition in ferromagnetic Cr2Si2Te6

The discovery of intrinsic magnetism in atomically thin two-dimensional transition-metal trichalcogenides has attracted intense research interest due to the exotic properties of magnetism and potential applications in devices. Pressure has proven to be an effective tool to manipulate the crystal and electronic structures of the materials. Here, we report investigations on ferromagnetic van der Waals Cr2Si2Te6 via high-pressure synchrotron x-ray diffraction, electrical resistance, Hall resistance, and magnetoresistance measurements. Under compression, Cr2Si2Te6 simultaneously undergoes a structural transition, emergence of superconductivity at 3 K, sign change of the magnetoresistance, and dramatic change of the Hall coefficient at ~8 GPa. The superconductivity persists up to the highest measured pressure of 47.1 GPa with a maximum Tc = 4.5 K at ~30 GPa. The discovery of superconductivity in the two-dimensional van der Waals ferromagnetic Cr-based Cr2Si2Te6 provides new perspectives to explore superconductivity and the interplay between superconductivity and magnetism.

cond-mat.supr-con↗

arXiv2019

Learning epidemic threshold in complex networks by Convolutional Neural Network

Deep learning has taken part in the competition since not long ago to learn and identify phase transitions in physical systems such as many body quantum systems, whose underlying lattice structures are generally regular as they're in euclidean space. Real networks have complex structural features which play a significant role in dynamics in them, and thus the structural and dynamical information of complex networks can not be directly learned by existing neural network models. Here we propose a novel and effective framework to learn the epidemic threshold in complex networks by combining the structural and dynamical information into the learning procedure. Considering the strong performance of learning in Euclidean space, Convolutional Neural Network (CNN) is used and, with the help of confusion scheme, we can identify precisely the outbreak threshold of epidemic dynamics. To represent the high dimensional network data set in Euclidean space for CNN, we reduce the dimensionality of a network by using graph representation learning algorithms and discretize the embedded space to convert it into an image-like structure. We then creatively merge the nodal dynamical states with the structural embedding by multi-channel images. In this manner, the proposed model can draw the conclusion from both structural and dynamical information. A large number of simulations show a great performance in both synthetic and empirical network data set. Our end-to-end machine learning framework is robust and universally applicable to complex networks with arbitrary size and topology.

physics.soc-ph↗

arXiv2019

Machine learning dynamical phase transitions in complex networks

In recent years, machine learning has been adopted to complex networks, but most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition point associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection, prediction, and control of complex dynamical systems in general.

physics.soc-ph↗

arXiv2019

Deep learning assessment of breast terminal duct lobular unit involution: towards automated prediction of breast cancer risk

Terminal ductal lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses' Health Study (NHS). A first set of 92 WSIs was annotated for TDLUs, acini and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73$\pm$0.09, and segmented TDLUs and adipose tissue with Dice scores of 0.86$\pm$0.11 and 0.86$\pm$0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 95% CI [0.51, 0.83], 0.81, 95% CI [0.67, 0.90], and 0.73, 95% CI [0.54, 0.85], respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of >0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80, 95% CI [0.63, 0.90] for number of TDLUs per tissue area, 0.57, 95% CI [0.19, 0.77] for median TDLU span, and 0.80, 95% CI [0.62, 0.89] for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status.

eess.IV↗

arXiv2019

Batch size selection for variance estimators in MCMC

We consider batch size selection for a general class of multivariate batch means variance estimators, which are computationally viable for high-dimensional Markov chain Monte Carlo simulations. We derive the asymptotic mean squared error for this class of estimators. Further, we propose a parametric technique for estimating optimal batch sizes and discuss practical issues regarding the estimating process. Vector auto-regressive, Bayesian logistic regression, and Bayesian dynamic space-time examples illustrate the quality of the estimation procedure where the proposed optimal batch sizes outperform current batch size selection methods.

math.ST↗

arXiv2019

Realisation of Symmetry Enforced Two-Dimensional Dirac Fermions in Nonsymmorphic $α$-Bismuthene

Two-dimensional (2D) Dirac-like electron gases have attracted tremendous research interest ever since the discovery of free-standing graphene. The linear energy dispersion and non-trivial Berry phase play the pivotal role in the remarkable electronic, optical, mechanical and chemical properties of 2D Dirac materials. The known 2D Dirac materials are gapless only within certain approximations, for example, in the absence of SOC. Here we report a route to establishing robust Dirac cones in 2D materials with nonsymmorphic crystal lattice. The nonsymmorphic symmetry enforces Dirac-like band dispersions around certain high-symmetry momenta in the presence of SOC. Through $μ$-ARPES measurements we observe Dirac-like band dispersions in $α$-bismuthene. The nonsymmorphic lattice symmetry is confirmed by $μ$-LEED and STM. Our first-principles simulations and theoretical topological analysis demonstrate the correspondence between nonsymmorphic symmetry and Dirac states. This mechanism can be straightforwardly generalized to other nonsymmorphic materials. The results open the door for the search of symmetry enforced Dirac fermions in the vast uncharted world of nonsymmorphic 2D materials.

cond-mat.mes-hall↗

arXiv2019

Theory of phonon side jump contribution in Anomalous Hall transport

The role of electron-phonon scattering in finite-temperature anomalous Hall effect is still poorly understood. In this work, we present a Boltzmann theory for the side-jump contribution from electron-phonon scattering, which is derived from the microscopic quantum mechanical theory. We show that the resulting phonon side-jump conductivity generally approaches different limiting values in the high and low temperature limits, and hence can exhibit strong temperature dependence in the intermediate temperature regime. Our theory is amenable to ab initio treatment, which makes quantitative comparison between theoretical and experimental results possible.

cond-mat.mes-hall↗

arXiv2019

Szegedy Quantum Walks with Memory on Regular Graphs

Quantum walks with memory(QWM) are a type of modified quantum walks that record the walker's latest path. The general model of coined QWM is presented in Phys. Rev. A 93, 042323 (2016). In this paper, we present general model of Szegedy QWM. Importantly, the relation of coined QWM and Szegedy QWM is revealed. By transforming coined QWM to Szegedy QWM, some amazing results about QWM are founded.

quant-ph↗

arXiv2019

Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction

With the rapid development of online advertising and recommendation systems, click-through rate prediction is expected to play an increasingly important role.Recently many DNN-based models which follow a similar Embedding&MLP paradigm have been proposed, and have achieved good result in image/voice and nlp fields. In these methods the Wide&Deep model announced by Google plays a key role.Most models first map large scale sparse input features into low-dimensional vectors which are transformed to fixed-length vectors, then concatenated together before being fed into a multilayer perceptron (MLP) to learn non-linear relations among input features. The number of trainable variables normally grow dramatically the number of feature fields and the embedding dimension grow. It is a big challenge to get state-of-the-art result through training deep neural network and embedding together, which falls into local optimal or overfitting easily. In this paper, we propose an Structured Semantic Model (SSM) to tackles this challenge by designing a orthogonal base convolution and pooling model which adaptively learn the multi-scale base semantic representation between features supervised by the click label.The output of SSM are then used in the Wide&Deep for CTR prediction.Experiments on two public datasets as well as real Weibo production dataset with over 1 billion samples have demonstrated the effectiveness of our proposed approach with superior performance comparing to state-of-the-art methods.

stat.ML↗

arXiv2019

Two-dimensional nodal-loop half metal in monolayer MnN

Two-dimensional (2D) materials with nodal-loop band crossing have been attracting great research interest. However, it remains a challenge to find 2D nodal loops that are robust against spin-orbit coupling (SOC) and realized in magnetic states. Here, based on first-principles calculations and theoretical analysis, we predict that monolayer MnN is a 2D nodal-loop half metal with fully spin polarized nodal loops. We show that monolayer MnN has a ferromagnetic ground state with out-of-plane magnetization. Its band structure shows half metallicity with three low-energy bands belonging to the same spin channel. The crossing between these bands forms two concentric nodal loops centered around the $Γ$ point near the Fermi level. Remarkably, the nodal loops and their spin polarization are robust under SOC, due to the protection of a mirror symmetry. We construct an effective model to characterize the fully polarized emergent nodal-loop fermions. We also find that a uniaxial strain can induce a loop transformation from a localized single loop circling around $Γ$ to a pair of extended loops penetrating the Brillouin zone.

cond-mat.mes-hall↗

arXiv2019

Dry transfer method for suspended graphene on lift-off-resist: simple ballistic devices with Fabry-Pérot interference

We demonstrate a fabrication scheme for clean suspended structures using chemical-vapor-deposition-grown graphene and a dry transfer method on lift-off-resist-coated substrates to facilitate suspended graphene nanoelectronic devices for technology applications. It encompasses the demands for scalable fabrication as well as for ultra-fast response due to weak coupling to environment. The fabricated devices exhibited initially a weak field-effect response with substantial positive ($p$) doping which transformed into weak negative ($n$) doping upon current annealing at the temperature of 4 Kelvin. With increased annealing current, $n$-doping gradually decreased while the Dirac peak position approached zero in gate voltage. An ultra-low residual charge density of $9\times10^8 \mathrm{ \ cm^{-2}}$ and a mobility of $1.9 \times 10^5 \mathrm{\ cm^2/Vs}$ were observed. Our samples display clear Fabry-Pérot (FP) conductance oscillation which indicates ballistic electron transport. The spacing of the FP oscillations are found to depend on the charge density in a manner that agrees with theoretical modeling based on Klein tunneling of Dirac particles. The ultra-low residual charge, the FP oscillations with density dependent period, and the high mobility prove excellent quality of our suspended graphene devices. Owing to its simplicity, scalability and robustness, this fabrication scheme enhances possibilities for production of suspended, high-quality, two-dimensional-material structures for novel electronic applications.

cond-mat.mes-hall↗

arXiv2019

Efficient quantum circuit for singular value thresholding

Singular value thresholding (SVT) operation is a fundamental core module in many mathematical models in computer vision and machine learning, particularly for many nuclear norm minimizing-based problems. We presented a quantum SVT (QSVT) algorithm which was used as a subroutine to address an image classification problem. This algorithm runs in $O\left[\log\left(pq\right)\right]$, an exponential speed improvement over the classical algorithm which runs in $O\left[poly\left(pq\right)\right]$. In this study, we investigate this algorithm and design a scalable quantum circuit for QSVT. In the circuit design, we introduce an adjustable parameter $α$ to ensure the high probability of obtaining the final result and the high fidelity of actual and ideal final states. We also show that the value of $α$ can be computed ahead of implementing the quantum circuit when the inputs of the QSVT algorithm, i.e. matrix ${\bf{A}}$ and constant $τ$, are given. In addition, we propose a small-scale quantum circuit for QSVT. We numerically simulate and demonstrate the performance of this circuit, verifying its capability to solve the intended SVT. The quantum circuit for QSVT implies a tempting possibility for experimental realization on a quantum computer.

quant-ph↗

arXiv2019

Investigating Thermal Cooling Mechanisms of Human Body Under Exposure to Electromagnetic Radiation

Thermal cooling mechanisms of human exposed to electromagnetic (EM) radiation are studied in detail. The electromagnetic and thermal co-simulation method is utilized to calculate the electromagnetic and temperature distributions. Moreover, Pennes' bioheat equation is solved to understand different thermal cooling mechanisms including blood flow, convective cooling and radiative cooling separately or jointly. Numerical results demonstrate the characteristics and functions for each cooling mechanism. Different from the traditional view that the cooling effect of blood is usually reflected by its influence on sweat secretion and evaporation, our study indicates that the blood flow itself is an important factor of thermal cooling especially for high-intensity EM radiation. This work contributes to fundamental understanding of thermal cooling mechanisms of human.

physics.med-ph↗

arXiv2019

Anomalous spatial shifts in interface electronic scattering

The anomalous spatial shifts at interface scattering, first studied in geometric optics, recently found their counterparts in the electronic context. It was shown that both longitudinal and transverse shifts, analogous to the Goos-Hanchen and Imbert-Fedorov effects in optics, can exist when electrons are scattered at a junction interface. More interestingly, the shifts are also discovered in the process of Andreev reflection at a normal/superconductor interface. Particularly, for the case with unconventional superconductors, it was discovered that the transverse shift can arise solely from the superconducting pair potential and exhibit characteristic features depending on the pairing. Here, we briefly review the recent works in this field, with an emphasis on the physical picture and theoretical understanding.

cond-mat.mes-hall↗