arXiv subjects

Ying Liu

Publications and source records attributed to Ying Liu.

At least 253 records · Page 14Linked to original sources

arXiv2018

Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks

One of the big restrictions in brain computer interface field is the very limited training samples, it is difficult to build a reliable and usable system with such limited data. Inspired by generative adversarial networks, we propose a conditional Deep Convolutional Generative Adversarial (cDCGAN) Networks method to generate more artificial EEG signal automatically for data augmentation to improve the performance of convolutional neural networks in brain computer interface field and overcome the small training dataset problems. We evaluate the proposed cDCGAN method on BCI competition dataset of motor imagery. The results show that the generated artificial EEG data from Gaussian noise can learn the features from raw EEG data and has no less than the classification accuracy of raw EEG data in the testing dataset. Also by using generated artificial data can effectively improve classification accuracy at the same model with limited training data.

cs.HC↗

arXiv2018

Multi-view Point Cloud Registration with Adaptive Convergence Threshold and its Application on 3D Model Retrieval

Multi-view point cloud registration is a hot topic in the communities of multimedia technology and artificial intelligence (AI). In this paper, we propose a framework to reconstruct the 3D models by the multi-view point cloud registration algorithm with adaptive convergence threshold, and subsequently apply it to 3D model retrieval. The iterative closest point (ICP) algorithm is implemented combining with the motion average algorithm for the registration of multi-view point clouds. After the registration process, we design applications for 3D model retrieval. The geometric saliency map is computed based on the vertex curvature. The test facial triangle is then generated based on the saliency map, which is applied to compare with the standard facial triangle. The face and non-face models are then discriminated. The experiments and comparisons prove the effectiveness of the proposed framework.

cs.CV↗

arXiv2018

Total Variation Regularized Tensor-on-scalar Regression

In this paper, we propose Total Variation Regularized Tensor-on-scalar Regression(TVTR), a novel method for estimating the association between a tensor outcome (a one dimensional or multidimensional array) and scalar predictors. While the statistical developments proposed here were motivated by the brain mapping and activity tracking, the methodology is designed and presented in generality 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 the parameter estimation, which is efficient and scalable in distributed computing platform. Proof of the algorithm convergence is provided and the statistical consistency of the estimator is presented via an oracle inequality. We presented 1D and 2D simulation results, and demonstrate that TVTR 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↗

arXiv2018

Transverse shift in crossed Andreev reflection

Crossed Andreev reflection (CAR) is an intriguing effect that occurs in a normal-superconductor-normal junction. In CAR, an incoming electron from one terminal is coherently scattered as an outgoing hole into the other terminal. Here, we reveal that there exists a transverse spatial shift in CAR, i.e., the plane of CAR for the outgoing hole may have a sizable transverse shift from the plane of incidence for the incoming electron. We explicitly demonstrate the effect in a model system based on Weyl semimetals. We further show that the effect is quite general and exists when the terminals have sizable spin-orbit coupling. In addition, we find that the corresponding shift in the elastic cotunneling process shows different behaviors, and it vanishes when the two terminals are identical. Based on these findings, we suggest possible experimental setups for detecting the effect, which may also offer an alternative method for probing CAR.

cond-mat.mes-hall↗

arXiv2018

Lithium ion intercalation in thin crystals of hexagonal TaSe2 gated by a polymer electrolyte

Ionic liquid gating has been used to modify properties of layered transition metal dichalcogenides (TMDCs), including two-dimensional (2D) crystals of TMDCs used extensively recently in the device work, which has led to observations of properties not seen in the bulk. The main effect comes from the electrostatic gating due to strong electric field at the interface. In addition, ionic liquid gating also leads to ion intercalation when the ion size of gate electrolyte is small compared to the interlayer spacing of TMDCs. However, the microscopic processes of ion intercalation have rarely been explored in layered TMDCs. Here, we employed a technique combining photolithography device fabrication and electrical transport measurements on the thin crystals of hexagonal TaSe2 using multiple channel devices gated by a polymer electrolyte LiClO4/PEO. The gate voltage and time dependent source-drain resistances of these thin crystals were used to obtain information on the intercalation process, the effect of ion intercalation, and the correlation between the ion occupation of allowed interstitial sites and the device characteristics. We found a gate voltage controlled modulation of the charge density waves and scattering rate of charge carriers. Our work suggests that ion intercalation can be a useful tool for layered materials engineering and 2D crystal device design.

cond-mat.mtrl-sci↗

arXiv2018

Angle-dependent magnetoresistance as a sensitive probe of the charge density wave in quasi-one-dimensional semimetal Ta$_2$NiSe$_7$

The behavior of charge density wave (CDW) in an external magnetic field is dictated by both orbital and Pauli (Zeeman) effects. A quasi-one-dimensional (Q1D) system features Q1D Fermi surfaces that allow these effects to be distinguished, which in turn can provide sensitive probe to the underlying electronic states. Here we studied the field dependence of an incommensurate CDW in a transition-metal chalcogenide Ta2NiSe7 with a Q1D chain structure. The angle-dependent magnetoresistance (MR) is found to be very sensitive to the relative orientation between the magnetic field and the chain direction. With an applied current fixed along the b axis (the chain direction), the angle-dependent MR shows a striking change of the symmetry below T_CDW only for a rotating magnetic field in the ac plane. In contrast, the symmetry axis remains unchanged for other configurations (H in ab and bc plane). The orbital effect conforms to the lattice symmetry, while Pauli effect in the form of μB B / v_F can be responsible for such symmetry change, provided that the Fermi velocity v_F is significantly anisotropic and the nesting vector changes in a magnetic field, which is corroborated by our first-principles calculations. Our results show that the angle-dependent MR is a sensitive transport probe of CDW and can be useful for the study of low-dimensional systems in general.

cond-mat.str-el↗

arXiv2018

A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification

Effective detection of arrhythmia is an important task in the remote monitoring of electrocardiogram (ECG). The traditional ECG recognition depends on the judgment of the clinicians' experience, but the results suffer from the probability of human error due to the fatigue. To solve this problem, an ECG signal classification method based on the images is presented to classify ECG signals into normal and abnormal beats by using two-dimensional convolutional neural networks (2D-CNNs). First, we compare the accuracy and robustness between one-dimensional ECG signal input method and two-dimensional image input method in AlexNet network. Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AlexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification. The performance evaluated on the MIT-BIH arrhythmia database demonstrates that the proposed method can achieve the accuracy of 98% and maintain high accuracy within SNR range from 20 dB to 35 dB. The experiment shows that the 2D-CNNs initialized with AlexNet weights performs better than one-dimensional signal method without a large-scale dataset.

cs.CV↗

arXiv2018

Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection

A new statistical procedure (Model-X \cite{candes2018}) has provided a way to identify important factors using any supervised learning method controlling for FDR. This line of research has shown great potential to expand the horizon of machine learning methods beyond the task of prediction, to serve the broader needs in scientific researches for interpretable findings. However, the lack of a practical and flexible method to generate knockoffs remains the major obstacle for wide application of Model-X procedure. This paper fills in the gap by proposing a model-free knockoff generator which approximates the correlation structure between features through latent variable representation. We demonstrate our proposed method can achieve FDR control and better power than two existing methods in various simulated settings and a real data example for finding mutations associated with drug resistance in HIV-1 patients.

stat.ME↗

arXiv2018

Quadratic contact point semimetal: Theory and material realization

Most electronic properties of metals are determined solely by the low-energy states around the Fermi level, and for topological metals/semimetals, these low-energy states become distinct because of their unusual energy dispersion and emergent pseudospin degree of freedom. Here, we propose a class of materials which are termed as quadratic contact point (QCP) semimetals. In these materials, the conduction and valence bands contact at isolated points in the Brillouin zone, around which the band dispersions are quadratic along all three directions. We show that in the absence/presence of spin-orbit coupling, there may exist triply/quadruply-degenerate QCPs that are protected by the crystalline symmetry. We construct effective models to characterize the low-energy fermions near these QCPs. Under strong magnetic field, unlike the usual 3D electron gas, there appear unconventional features in the Landau spectrum. The QCP semimetal phase is adjacent to a variety of topological phases. For example, by breaking symmetries via Zeeman field or lattice strain, it can be transformed into a Weyl semimetal with Weyl and double Weyl points, a Z2 topological insulator/metal, or a Dirac semimetal. Via first-principles calculations, we identify realistic materials Cu2Se and RhAs3 as candidates for QCP semimetals.

cond-mat.mtrl-sci↗

arXiv2018

Unconventional pairing induced anomalous transverse shift in Andreev reflection

Superconductors with unconventional pairings have been a fascinating subject of research, for which a central issue is to explore effects that can be used to characterize the pairing. The process of Andreev reflection--the reflection of an electron as a hole at a normal-mental-superconductor interface by transferring a Cooper pair into the superconductor--offers a basic mechanism to probe the pairing through transport. Here we predict that in Andreev reflection from unconventional superconductors, the reflected hole acquires an anomalous spatial shift normal to the plane of incidence, arising from the unconventional pairing. The transverse shift is sensitive to the superconducting gap structure, exhibiting characteristic features for each pairing type, and can be detected as voltage signals. Our work not only unveils a fundamentally new effect but also suggests a powerful new technique capable of probing the structure of unconventional pairings.

cond-mat.mes-hall↗

arXiv2018

Defects in h-BN tunnel barrier for local electrostatic probing of two dimensional materials

Defects in hexagonal boron nitride (h-BN) layer can facilitate tunneling current through thick h-BN tunneling barriers. We have investigated such current-mediating defects as local probes for materials in two dimensional heterostructure stacks. Besides $IV$ characteristics and negative differential conductance, we have characterized the electrical properties of h-BN defects in vertical graphene-h-BN-Cr/Au tunnel junctions in terms of low frequency current noise. Our results indicate a charge sensitivity of 1.5$\times$$ 10^-5$e/$\sqrt Hz$ at 10 $Hz$, which is equal to good metallic single electron transistors. The noise spectra at low frequency are governed by a few two-level fluctuators. For variations in electrochemical potential, we achieve a sensitivity of 0.8$μ$eV/$\sqrt Hz$.

cond-mat.mes-hall↗

arXiv2018

Almost ideal nodal-loop semimetal in monoclinic CuTeO$_3$ material

Nodal-loop semimetals are materials in which the conduction and valence bands cross on a one-dimensional loop in the reciprocal space. For the nodal-loop character to manifest in physical properties, it is desired that the loop is close to the Fermi level, relatively flat in energy, simple in its shape, and not coexisting with other extraneous bands. Here, based on the first-principles calculations, we show that the monoclinic CuTeO$_3$ is a realistic nodal-loop semimetal that satisfies all these requirements. The material features only a single nodal loop around the Fermi level, protected by either of the two independent symmetries: the $\mathcal{PT}$ symmetry and the glide mirror symmetry. The size of the loop can be effectively tuned by strain, and the loop can even be annihilated under stain, making a topological phase transition to a trivial insulator phase. Including the spin-orbit coupling opens a tiny gap at the loop, and the system becomes a $\mathbb{Z}_2$ topological semimetal with a nontrivial bulk $\mathbb{Z}_2$ invariant but no global bandgap. The corresponding topological surface states have been identified. We also construct a low-energy effective model to describe the nodal loop and the effect of spin-orbit coupling.

cond-mat.mtrl-sci↗

arXiv2018

Monolayer Mg$_{2}$C: Negative Poisson's ratio and unconventional 2D emergent fermions

Novel two-dimensional (2D) emergent fermions and negative Poisson's ratio in 2D materials are fascinating subjects of research. Here, based on first-principles calculations and theoretical analysis, we predict that the hexacoordinated Mg$_{2}$C monolayer hosts both exotic properties. We analyze its phonon spectrum, reveal the Raman active modes, and show that it has small in-plane stiffness constants. Particularly, under the tensile strain in the zigzag direction, the Mg$_{2}$C monolayer shows an intrinsic negative Poisson's ratio $\sim -0.023$, stemming from its unique puckered hinge structure. The material is metallic at its equilibrium state. A moderate biaxial strain can induce a metal-semimetal-semiconductor phase transition, during which several novel types of 2D fermions emerge, including the anisotropic Dirac fermions around 12 tilted Dirac points in the metallic phase, the $2$D double Weyl fermions in the semimetal phase where the conduction and valence bands touch quadratically at a single Fermi point, and the 2D pseudospin-1 fermions at the critical point of the semimetal-semiconductor phase transition where three bands cross at a single point on the Fermi level. In addition, uniaxial strains along the high-symmetry directions break the three-fold rotational symmetry and reduce the number of Dirac points. Interestingly, it also generates 2D type-II Dirac points. We construct effective models to characterize the properties of these novel fermions. Our result reveals Mg$_{2}$C monolayer as an intriguing platform for the study of novel 2D fermions, and also suggests its great potential for nanoscale device applications.

cond-mat.mes-hall↗

arXiv2018

ECD: An Edge Content Delivery and Update Framework in Mobile Edge Computing

This article proposes an edge content delivery framework (ECD) based on mobile edge computing in the era of Internet of Things (IOT), to alleviate the load of the core network and improve the quality of experience (QoE) of mobile users. Considering mobile devices become both the content consumers and providers, and majority of the contents are unnecessary to be uploaded to the cloud datacenter, at the network edge, we deploy a content server to store the raw contents generated from the mobile users, and a cache pool to store the contents that are frequently requested by mobile users in the ECD. The cache pools are ranked and high ranked cache pools will store contents with higher popularity. Furthermore, we propose edge content delivery scheme and edge content update scheme, based on content popularity and cache pool ranking. The content delivery scheme is to efficiently deliver contents to mobile users, while the edge content update scheme is to mitigate the content generated by users to appropriate cache pools based on its request frequently and cache poor ranking. The edge content delivery is completely different from the content delivery network and can further reduce the load on the core network. In addition, because the top ranking cache pools are prioritized for higher priority contents and the cache pools are prioritized for higher priority contents and the cache pools are in proximity to the mobile users, the immediately interactive response between mobile users and cache pools can be achieved. A representative case study of ECD is provided and open research issues are discussed.

cs.NI↗

arXiv2018

Weighted batch means estimators in Markov chain Monte Carlo

This paper proposes a family of weighted batch means variance estimators, which are computationally efficient and can be conveniently applied in practice. The focus is on Markov chain Monte Carlo simulations and estimation of the asymptotic covariance matrix in the Markov chain central limit theorem, where conditions ensuring strong consistency are provided. Finite sample performance is evaluated through auto-regressive, Bayesian spatial-temporal, and Bayesian logistic regression examples, where the new estimators show significant computational gains with a minor sacrifice in variance compared with existing methods.

math.ST↗

arXiv2018

Goos-Hänchen-like shifts at metal/superconductor interface

At a normal-metal/superconductor interface, an incident electron from the normal-metal (N) side can be normally reflected as an electron or Andreev reflected as a hole. We show that pronounced lateral shifts along the interface between the incident and the reflected quasiparticles can happen in both reflection processes, which are analogous to the Goos-Hänchen effect in optics. Two concrete model systems are considered. For the simplest model in which the N side is of the two-dimensional electron gas, we find that while the shift in Andreev reflection stays positive, the shift in normal reflection can be made either positive or negative, depending on the excitation energy. For the second model with the N side taken by graphene, the shift in Andreev reflection can also be made negative, and the shifts have rich behavior due to the additional sublattice pseudospin degree of freedom. We show that the shift strongly modifies the dispersion for the confined waveguide modes in an SNS structure. We also suggest a possible experimental setup for detecting the shift.

cond-mat.mes-hall↗

arXiv2018

On Maximizing Sampling Time of RF-Harvesting Sensor Nodes Over Random Channel Gains

In the future, sensor nodes or Internet of Things (IoTs) will be tasked with sampling the environment. These nodes/devices are likely to be powered by a Hybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a {\em sampling time} to collect sensory data, carry out computation, and transmit sensed data to the HAP. A key challenge, however, is random channel gains, which cause sensor nodes to receive varying amounts of Radio Frequency (RF) energy. To this end, we formulate a stochastic program to determine the charging time of the HAP and sampling time of sensor nodes. Our objective is to minimize the {\em expected} penalty incurred when sensor nodes experience an energy shortfall. We consider two cases: {\em single} and {\em multi} time slots. In the former, we determine a suitable HAP charging time and nodes sampling time on a slot-by-slot basis whilst the latter considers the best charging and sampling time for use in the next $T$ slots. We conduct experiments over channel gains drawn from the Gaussian, Rayleigh or Rician distribution. Numerical results confirm our stochastic program can be used to compute good charging and sampling times that incur the minimum penalty over the said distributions.

cs.NI↗

arXiv2018

On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems

Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is a favorable candidate for the fifth generation (5G) cellular systems. However, a key challenge is the high power consumption imposed by its numerous radio frequency (RF) chains, which may be mitigated by opting for low-resolution analog-to-digital converters (ADCs), whilst tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving the overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system {with ADC quantizers} are discussed.

cs.IT↗