arXiv subjects

Ling Li

Publications and source records attributed to Ling Li.

At least 145 records · Page 8Linked to original sources

arXiv2019

A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images

Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imaging diagnostic procedures are expertise-demanding, and thus call for a higher-integrated AI-based diagnostic algorithm. Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD. Multiple modalities, including T1- weighted MRI and 11C-CFT PET, were used in the proposed framework. The reliability of this framework was then validated on a dataset from the PET center of Huashan Hospital, as the dataset contains paired T1-MRI and CFT-PET images of 18 Normal (NL) subjects and 49 PD subjects. Results: We obtained an accuracy of 100% for the PD/NL classification task, besides, we conducted several comparative experiments to validate the diagnosis ability of our framework. Conclusion: Through experiment we illustrate that (1) automatic segmentation has the same classification effect as the manual segmentation, (2) the multi-modality images generates a better prediction than single modality images, and (3) volume feature is shown to be irrelevant to PD diagnosis.

q-bio.QM↗

arXiv2019

A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm

Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler's Elastica based approach that properly deals with random noises to improve the segmentation performance for noisy images. We solve the corresponding optimization problem via using the progressive hedging algorithm (PHA) with a step length suggested by the alternating direction method of multipliers (ADMM). Technically, all the simplified convex versions of the subproblems derived from the major framework of PHA can be obtained by using the curvature weighted approach and the convex relaxation method. Then an alternating optimization strategy is applied with the merits of using some powerful accelerating techniques including the fast Fourier transform (FFT) and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which validated some significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithm.

cs.CV↗

arXiv2019

Semi-supervised Learning on Graph with an Alternating Diffusion Process

Graph-based semi-supervised learning usually involves two separate stages, constructing an affinity graph and then propagating labels for transductive inference on the graph. It is suboptimal to solve them independently, as the correlation between the affinity graph and labels are not fully exploited. In this paper, we integrate the two stages into one unified framework by formulating the graph construction as a regularized function estimation problem similar to label propagation. We propose an alternating diffusion process to solve the two problems simultaneously, which allows us to learn the graph and unknown labels in an iterative fashion. With the proposed framework, we are able to adequately leverage both the given labels and estimated labels to construct a better graph, and effectively propagate labels on such a dynamic graph updated simultaneously with the newly obtained labels. Extensive experiments on various real-world datasets have demonstrated the superiority of the proposed method compared to other state-of-the-art methods.

cs.CV↗

arXiv2019

Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks

Convolutional Neural Networks (CNN) based image reconstruction methods have been intensely used for X-ray computed tomography (CT) reconstruction applications. Despite great success, good performance of this data-based approach critically relies on a representative big training data set and a dense convoluted deep network. The indiscriminating convolution connections over all dense layers could be prone to over-fitting, where sampling biases are wrongly integrated as features for the reconstruction. In this paper, we report a robust hierarchical synthesis reconstruction approach, where training data is pre-processed to separate the information on the domains where sampling biases are suspected. These split bands are then trained separately and combined successively through a hierarchical synthesis network. We apply the hierarchical synthesis reconstruction for two important and classical tomography reconstruction scenarios: the spares-view reconstruction and the phase reconstruction. Our simulated and experimental results show that comparable or improved performances are achieved with a dramatic reduction of network complexity and computational cost. This method can be generalized to a wide range of applications including material characterization, in-vivo monitoring and dynamic 4D imaging.

cs.CV↗

arXiv2018

Magnetic field dependence of the nonlinear magnetic response and tricritical point in the monoaxial chiral helimagnet Cr$_{1/3}$NbS$_{2}$

We present a comprehensive study of the magnetization dynamics and phase evolution in Cr$_{1/3}$NbS$_{2}$, which realizes a chiral soliton lattice (CSL). The magnetic field dependence of the ac magnetic response is analyzed for five harmonic components, $M_{nω}(H)$ $(n =1-5)$, using a phase sensitive measurement over a frequency range, $f = 11 - 10,000$ Hz. At a critical field, the modulated CSL continuously evolves from a helicity-rich to a ferromagnetic domain-rich structure, where the crossover is revealed by the onset of an anomalous nonlinear magnetic response that coincides with extremely slow dynamics. The behavior is indicative of the formation of a spatially coherent array of large ferromagnetic domains which relax on macroscopic time-scales. The frequency dependence of the ac magnetic loss displays an asymmetric distribution of relaxation times across the highly nonlinear CSL regime, which shift to shorter time-scales with increasing temperature. We experimentally resolve the tricritical point at $T_{TCP}$ in a temperature regime above the ferromagnetic Curie temperature which separates the linear and nonlinear regimes of the CSL at the phase transition. A comprehensive phase diagram is constructed which summarized the features of the field and temperature dependence of the magnetic crossovers and phase transitions in Cr$_{1/3}$NbS$_{2}$.

cond-mat.str-el↗

arXiv2018

$3D$ Crystal Image Analysis based on Fast Synchrosqueezed Transforms

We propose an efficient algorithm to analyze $3D$ atomic resolution crystal images based on a fast $3D$ synchrosqueezed wave packet transform. The proposed algorithm can automatically extract microscopic information from $3D$ atomic resolution crystal images, e.g., crystal orientation, defects, and deformation, which are important information for characterizing material properties. The effectiveness of our algorithms is illustrated by experiments of synthetic datasets and real $3$D microscopic colloidal images.

math.NA↗

arXiv2018

Mutual correlation in the shock wave geometry

We probe the shock wave geometry with the mutual correlation in a spherically symmetric Reissner Nordström AdS black hole on the basis of the gauge/gravity duality. In the static background, we find that the regions living on the boundary of the AdS black holes are correlated provided the considered regions on the boundary are large enough. We also investigate the effect of the charge on the mutual correlation and find that the bigger the value of the charge is, the smaller the value of the mutual correlation will to be. As a small perturbation is added at the AdS boundary, the horizon shifts and a dynamical shock wave geometry forms after long time enough. In this dynamic background, we find that the greater the shift of the horizon is, the smaller the mutual correlation will to be. Especially for the case that the shift is large enough, the mutual correlation vanishes, which implies that the considered regions on the boundary are uncorrelated. The effect of the charge on the mutual correlation in this dynamic background is found to be the same as that in the static background.

gr-qc↗

arXiv2018

A Modified Smoothed Particle Hydrodynamics Approach for Modelling Dynamic Contact Angle Hysteresis

Dynamic wetting plays an important role in the physics of multiphase flow, and has significant influence on many industrial and geotechnical applications. In this work, a modified smoothed particle hydrodynamics (SPH) model is employed to simulate surface tension, contact angle, and dynamic wetting effects. The wetting and dewetting phenomena are simulated in a capillary tube, where the liquid particles are raised or withdrawn by a shifting substrate. The SPH model is modified by introducing a newly-developed viscous force formulation at liquid-solid interface to reproduce the rate-dependent behaviour of moving contact line. Dynamic contact angle simulations with interfacial viscous force are conducted to verify the effectiveness and accuracy of this new formulation. In addition, the influence of interfacial viscous force with different magnitude on contact angle dynamics is examined by empirical power law correlations, and the derived constants suggest the dynamic contact angle changes monotonically with interfacial viscous force. The simulation results are consistent with the experimental observations and theoretical predictions, implying that the interfacial viscous force can be associated with slip length of flow and microscopic surface roughness. This work has demonstrated that the modified SPH model can successfully account for the rate-dependent effects of moving contact line, and can be used for realistic multiphase flow simulation under dynamic conditions.

cond-mat.soft↗

arXiv2017

Fabrication of highly dense isotropic Nd-Fe-B bonded magnets via extrusion-based additive manufacturing

Isotropic bonded magnets with a high loading fraction of 70 vol.% Nd-Fe-B are fabricated via an extrusion-based additive manufacturing, or 3D printing system that enables rapid production of large parts for the first time. The density of the printed magnet is 5.15 g/cm3. The room temperature magnetic properties are: intrinsic coercivity Hci = 8.9 kOe (708.2 kA/m), remanence Br = 5.8 kG (0.58 Tesla), and energy product (BH)max = 7.3 MGOe (58.1 kJ/m3). The as-printed magnets are then coated with two types of polymers, both of which improve the thermal stability at 127 °C as revealed by flux aging loss measurements. Tensile tests performed at 25 °C and 100 °C show that the ultimate tensile stress (UTS) increases with increasing loading fraction of the magnet powder, and decreases with increasing temperature. AC magnetic susceptibility and resistivity measurements show that the 3D printed Nd-Fe-B bonded magnets exhibit extremely low eddy current loss and high resistivity. Finally, we show that through back electromotive force measurements that motors installed with 3D printed Nd-Fe-B magnets exhibit similar performance as compared to those installed with sintered ferrites.

cond-mat.mtrl-sci↗

arXiv2017

Bayesian subset simulation

We consider the problem of estimating a probability of failure $α$, defined as the volume of the excursion set of a function $f:\mathbb{X} \subseteq \mathbb{R}^{d} \to \mathbb{R}$ above a given threshold, under a given probability measure on $\mathbb{X}$. In this article, we combine the popular subset simulation algorithm (Au and Beck, Probab. Eng. Mech. 2001) and our sequential Bayesian approach for the estimation of a probability of failure (Bect, Ginsbourger, Li, Picheny and Vazquez, Stat. Comput. 2012). This makes it possible to estimate $α$ when the number of evaluations of $f$ is very limited and $α$ is very small. The resulting algorithm is called Bayesian subset simulation (BSS). A key idea, as in the subset simulation algorithm, is to estimate the probabilities of a sequence of excursion sets of $f$ above intermediate thresholds, using a sequential Monte Carlo (SMC) approach. A Gaussian process prior on $f$ is used to define the sequence of densities targeted by the SMC algorithm, and drive the selection of evaluation points of $f$ to estimate the intermediate probabilities. Adaptive procedures are proposed to determine the intermediate thresholds and the number of evaluations to be carried out at each stage of the algorithm. Numerical experiments illustrate that BSS achieves significant savings in the number of function evaluations with respect to other Monte Carlo approaches.

stat.CO↗

arXiv2017

A review for compact model of graphene field-effect transistors

Graphene has attracted enormous interests due to its unique physical, mechanical, and electrical properties. Specially, graphene-based field-effect transistors (FETs) have evolved rapidly and are now considered as an option for conventional silicon devices. As a critical step in the design cycle of modern IC products, compact model refers to the development of models for integrated semiconductor devices for use in circuit simulations. The purpose of this review is to provide a theoretical description of current compact model of graphene field-effect transistors. Special attention is devoted to the charge sheet model, drift-diffusion model, Boltzmann equation, density of states (DOS), and surface-potential-based compact model. Finally, an outlook of this field is briefly discussed.

cond-mat.mes-hall↗

arXiv2016

Sparse Subspace Clustering via Diffusion Process

Subspace clustering refers to the problem of clustering high-dimensional data that lie in a union of low-dimensional subspaces. State-of-the-art subspace clustering methods are based on the idea of expressing each data point as a linear combination of other data points while regularizing the matrix of coefficients with L1, L2 or nuclear norms for a sparse solution. L1 regularization is guaranteed to give a subspace-preserving affinity (i.e., there are no connections between points from different subspaces) under broad theoretical conditions, but the clusters may not be fully connected. L2 and nuclear norm regularization often improve connectivity, but give a subspace-preserving affinity only for independent subspaces. Mixed L1, L2 and nuclear norm regularization could offer a balance between the subspace-preserving and connectedness properties, but this comes at the cost of increased computational complexity. This paper focuses on using L1 norm and alleviating the corresponding connectivity problem by a simple yet efficient diffusion process on subspace affinity graphs. Without adding any tuning parameter , our method can achieve state-of-the-art clustering performance on Hopkins 155 and Extended Yale B data sets.

cs.CV↗

arXiv2015

Quantum Hall Effects in a Non-Abelian Honeycomb Lattice

We study the tunable quantum Hall effects in a non-Abelian honeycomb optical lattice which is a many-Dirac-points system. We find that the quantum Hall effects present different features as change as relative strengths of several perturbations. Namely, a gauge-field-dressed next-nearest-neighbor hopping can induce the quantum spin Hall effect and a Zeeman field can induce a so-called quantum anomalous valley Hall effect which includes two copies of quantum Hall states with opposite Chern numbers and counter-propagating edge states. Our study extends the borders of the field of quantum Hall effects in honeycomb optical lattice when the internal valley degrees of freedom enlarge.

cond-mat.str-el↗

arXiv2015

Structural and magnetic phase transitions in EuTi1-xNbxO3

We investigate the structural and magnetic phase transitions in EuTi1-xNbxO3 with synchrotron powder X-ray diffraction (XRD), resonant ultrasound spectroscopy (RUS), and magnetization measurements. Upon Nb-doping, the Pm-3m to I4/mcm structural transition shifts to higher temperatures and the room temperature lattice parameter increases while the magnitude of the octahedral tilting decreases. In addition, Nb substitution for Ti destabilizes the antiferromagnetic ground state of the parent compound and long range ferromagnetic order is observed in the samples containing more than 10% Nb. The structural transition in pure and doped compounds is marked by a step-like softening of the elastic moduli in a narrow temperature interval near TS, which resembles that of SrTiO3 and can be adequately modeled using the Landau free energy model employing the same coupling between strain and octahedral tilting order parameter as previously used to model SrTiO3.

cond-mat.mtrl-sci↗

arXiv2013

Comment on "Validity of Einstein Relation in Disordered Organic Semiconductors"

Wetzelaer, Koster, and Blom [PRL 107, 066605] recently observed that the classic Einstein relation $\frac{D}μ=\frac{kT}{q}$ is still valid in disordered semiconductors in thermal(quasi)equilibrium by studying the diffusion-driven currents of single-carrier diodes. This is in contrast to the previous theoretical prediction [2] and experiment observation from Leo et.al. [3] where the Einstein relation increases with 1/T. We will discuss the reason for this discrepancy here and suggest that one-carrier diodes experiment might nor be suitable for verifying Einstein relation in organic semiconductors.

cond-mat.dis-nn↗

arXiv2013

Physical Origin of Nonlinear transport in organic semiconductor at high carrier densities

The charge transport in some organic semiconductors demonstrates nonlinear properties and further universal power-law scaling with both bias and temperature. The physical origin of this behavior is investigated here using variable range hopping theory. The results shows, this universal power-law scaling can be well explained by variable range hopping theory . Relation to the recent experimental data is also discussed.

cond-mat.mes-hall↗

arXiv2013

Charge carrier relaxation model in hopping system

The relaxation phenomena of charge carriers in hopping system have been demonstrated and investigated theoretically. An analytical model describing the charge carrier relaxation is proposed based on the hopping transport theory. The relation between the material disorder, electric field and temperature and the relaxation phenomena has been discussed in detail, respectively. The calculated results reveal that the increase of electric field and temperature can promote the relaxation effect in disordered systems, while the increase of material disorder will weaken the relaxation. The proposed model can explain well the stretched-exponential law by adopting the appropriate parameters. The calculation shows a good agreement with the experimental data for organic semiconductors.

cond-mat.mtrl-sci↗

arXiv2012

Comments on "Unusual Thermoelectric Behavior Indicating a Hopping to Bandlike Transport Transition in Pentacene"

W. Chr. Germs, K. Guo, R. A. J. Janssen, and M. Kemerink [1] recently measured the temperature and concentration dependent seebeck coefficient in organic thin film transistor and found the seebeck coefficient increases with carrier concentration (corresponding to the gate voltage) in the low temperature regime. They further concluded that this unusual behavior is due to a transition from hopping transport in static localized states to bandlike transport, occurring at low temperatures. This is obviously in contrast to the previous theoretical prediction because it is widely accepted that hopping transport is more pronounced at low temperature. We will discuss the reason for this unusual behavior here and suggest that the density of states function plays an important role in concentration dependent seebeck coefficient.

cond-mat.dis-nn↗