arXiv subjects

Yao Wang

Publications and source records attributed to Yao Wang.

At least 379 records · Page 21Linked to original sources

arXiv2017

Producing Coherent Excitations in Pumped Mott Antiferromagnetic Insulators

Nonequilibrium dynamics in correlated materials has attracted attention due to the possibility of characterizing, tuning, and creating complex ordered states. To understand the photoinduced microscopic dynamics, especially the linkage under realistic pump conditions between transient states and remnant elementary excitations, we performed nonperturbative simulations of various time-resolved spectroscopies. We used the Mott antiferromagnetic insulator as a model platform. The transient dynamics of multi-particle excitations can be attributed to the interplay between Floquet virtual states and a modification of the density of states, in which interactions induce a spectral weight transfer. Using an autocorrelation of the time-dependent spectral function, we show that resonance of the virtual states with the upper Hubbard band in the Mott insulator provides the route towards manipulating the electronic distribution and modifying charge and spin excitations. Our results link transient dynamics to the nature of many-body excitations and provide an opportunity to design nonequilibrium states of matter via tuned laser pulses.

cond-mat.str-el↗

arXiv2017

Image Decomposition Using a Robust Regression Approach

This paper considers how to separate text and/or graphics from smooth background in screen content and mixed content images and proposes an algorithm to perform this segmentation task. The proposed methods make use of the fact that the background in each block is usually smoothly varying and can be modeled well by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics create sharp discontinuity. This algorithm separates the background and foreground pixels by trying to fit pixel values in the block into a smooth function using a robust regression method. The inlier pixels that can be well represented with the smooth model will be considered as background, while remaining outlier pixels will be considered foreground. We have also created a dataset of screen content images extracted from HEVC standard test sequences for screen content coding with their ground truth segmentation result which can be used for this task. The proposed algorithm has been tested on the dataset mentioned above and is shown to have superior performance over other methods, such as the hierarchical k-means clustering algorithm, shape primitive extraction and coding, and the least absolute deviation fitting scheme for foreground segmentation.

cs.CV↗

arXiv2017

Face Recognition Using Scattering Convolutional Network

Face recognition has been an active research area in the past few decades. In general, face recognition can be very challenging due to variations in viewpoint, illumination, facial expression, etc. Therefore it is essential to extract features which are invariant to some or all of these variations. Here a new image representation, called scattering transform/network, has been used to extract features from faces. The scattering transform is a kind of convolutional network which provides a powerful multi-layer representation for signals. After extraction of scattering features, PCA is applied to reduce the dimensionality of the data and then a multi-class support vector machine is used to perform recognition. The proposed algorithm has been tested on three face datasets and achieved a very high recognition rate.

cs.CV↗

arXiv2017

Emergence of Frohlich polaron from interlayer electron-phonon coupling in van der Waals heterostructure

Van der Waals heterostructures, vertical stacks of layered materials, offer newopportunities for novel quantum phenomena which are absent in their constituent components. Here we report the emergence of polaron quasiparticles at the interface of graphene/hexagonal boron nitride (h-BN) heterostructures. Using nanospot angle-resolved photoemission spectroscopy, we observe zone-corner replicas of h-BN valence band maxima, with energy spacing coincident with the highest phonon energy of the heterostructure|an indication of Frohlich polaron formation due to forward scattering electron-phonon coupling. Parabolic fitting of the h-BN bands yields an effective mass enhancement of ~ 2.3, suggesting an intermediate coupling strength. Our theoretical simulations based on Migdal-Eliashberg theory corroborate the experimental results, allowing the extraction of microscopic physical parameters. Moreover,renormalisation of graphene $π$ band is observed due to the hybridisation with the h-BN band. Our work generalises the polaron study from transition metal oxides to Van derWaals heterostructures with higher material exibility, highlighting interlayer coupling as an extra degree of freedom to explore emergent phenomena.

cond-mat.mes-hall↗

arXiv2017

Electronic orders near the type-II van Hove singularity in BC$_3$

Using the functional renormalization group, we investigate the electron instability in the single-sheet BC$_3$ when the electron filling is near a type-II van Hove singularity. For a finite Hubbard interaction, the ferromagnetic-like spin density wave order dominates in the immediate vicinity of the singularity. Elsewhere near the singularity the p-wave superconductivity prevails. We also find that a small nearest-neighbor Coulomb repulsion can enhance the superconductivity. Our results show that BC$_3$ would be a promising candidate to realize topological $p+ip'$ superconductivity, but the transition temperature is practically sizable only if the local interaction is moderately strong.

cond-mat.supr-con↗

arXiv2017

On an extremal problem involving a pair of forbidden posets

Resolving a conjecture of Methuku and the first author we determine the size of the largest family of subsets of an $n$-element set avoiding both $Y_k$ and $Y_k'$ as induced subposets. The result follows as a consequence of the analogous result on a cyclical grid poset.

math.CO↗

arXiv2017

A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling

While diffusion MRI has been extremely promising in the study of MTBI, identifying patients with recent MTBI remains a challenge. The literature is mixed with regard to localizing injury in these patients, however, gray matter such as the thalamus and white matter including the corpus callosum and frontal deep white matter have been repeatedly implicated as areas at high risk for injury. The purpose of this study is to develop a machine learning framework to classify MTBI patients and controls using features derived from multi-shell diffusion MRI in the thalamus, frontal white matter and corpus callosum.

cs.CV↗

arXiv2017

Subspace Learning in The Presence of Sparse Structured Outliers and Noise

Subspace learning is an important problem, which has many applications in image and video processing. It can be used to find a low-dimensional representation of signals and images. But in many applications, the desired signal is heavily distorted by outliers and noise, which negatively affect the learned subspace. In this work, we present a novel algorithm for learning a subspace for signal representation, in the presence of structured outliers and noise. The proposed algorithm tries to jointly detect the outliers and learn the subspace for images. We present an alternating optimization algorithm for solving this problem, which iterates between learning the subspace and finding the outliers. This algorithm has been trained on a large number of image patches, and the learned subspace is used for image segmentation, and is shown to achieve better segmentation results than prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu, and shape primitive extraction and coding algorithm.

cs.CV↗

arXiv2017

Text Extraction From Texture Images Using Masked Signal Decomposition

Text extraction is an important problem in image processing with applications from optical character recognition to autonomous driving. Most of the traditional text segmentation algorithms consider separating text from a simple background (which usually has a different color from texts). In this work we consider separating texts from a textured background, that has similar color to texts. We look at this problem from a signal decomposition perspective, and consider a more realistic scenario where signal components are overlaid on top of each other (instead of adding together). When the signals are overlaid, to separate signal components, we need to find a binary mask which shows the support of each component. Because directly solving the binary mask is intractable, we relax this problem to the approximated continuous problem, and solve it by alternating optimization method. We show that the proposed algorithm achieves significantly better results than other recent works on several challenging images.

cs.CV↗

arXiv2017

Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and many others. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part respectively. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial-spectral total variation (SSTV) regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell_1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regulariztion has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier (ALM) method. Finally, extensive experiments on simulated and real-world noise HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.

cs.CV↗

arXiv2017

Numerically exploring the 1D-2D dimensional crossover on spin dynamics in the doped Hubbard model

Using determinant quantum Monte Carlo (DQMC) simulations, we systematically study the doping dependence of the crossover from one to two dimensions and its impact on the magnetic properties of the Hubbard model. A square lattice of chains is used, in which the dimensionality can be tuned by varying the interchain coupling $t_\perp$. The dynamical spin structure factor and static quantities, such as the static spin susceptibility and nearest-neighbor spin correlation function, are characterized in the one- and two-dimensional limits as a benchmark. When the dimensionality is tuned between these limits, the magnetic properties, while evolving smoothly from one to two dimensions, drastically change regardless of the doping level. This suggests that the spin excitations in the two-dimensional Hubbard model, even in the heavily doped case, cannot be explained using the spinon picture known from one dimension. The DQMC calculations are complemented by cluster perturbation theory studies to form a more complete picture of how the crossover occurs as a function of doping and how doped holes impact magnetic order.

cond-mat.str-el↗

arXiv2017

SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning

It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions. In this work, we present a new way to robustify AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning (SPL) into Boosting framework. Specifically, we design a new robust Boosting algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented by slightly modifying off-the-shelf Boosting packages. Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost.

cs.CV↗

arXiv2017

Unsupervised Learning of Spike Patterns for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic (μECoG) Data

For the past few years, we have developed flexible, active, multiplexed recording devices for high resolution recording over large, clinically relevant areas in the brain. While this technology has enabled a much higher-resolution view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of seizure data produced by these devices have not yet been developed. In this work we proposed an unsupervised learning framework for spike analysis, which by itself reveals spike pattern. By applying advanced video processing techniques for separating a multi-channel recording into individual spike segments, unfolding the spike segments manifold and identifying natural clusters for spike patterns, we are able to find the common spike motion patterns. And we further explored using these patterns for more interesting and practical problems as seizure prediction and spike wavefront prediction. These methods have been applied to in-vivo feline seizure recordings and yielded promising results.

q-bio.NC↗

arXiv2017

Segmentation of 3D High-frequency Ultrasound Images of Human Lymph Nodes Using Graph Cut with Energy Functional Adapted to Local Intensity Distribution

Previous studies by our group have shown that three-dimensional high-frequency quantitative ultrasound methods have the potential to differentiate metastatic lymph nodes from cancer-free lymph nodes dissected from human cancer patients. To successfully perform these methods inside the lymph node parenchyma, an automatic segmentation method is highly desired to exclude the surrounding thin layer of fat from quantitative ultrasound processing and accurately correct for ultrasound attenuation. In high-frequency ultrasound images of lymph nodes, the intensity distribution of lymph node parenchyma and fat varies spatially because of acoustic attenuation and focusing effects. Thus, the intensity contrast between two object regions (e.g., lymph node parenchyma and fat) is also spatially varying. In our previous work, nested graph cut demonstrated its ability to simultaneously segment lymph node parenchyma, fat, and the outer phosphate-buffered saline bath even when some boundaries are lost because of acoustic attenuation and focusing effects. This paper describes a novel approach called graph cut with locally adaptive energy to further deal with spatially varying distributions of lymph node parenchyma and fat caused by inhomogeneous acoustic attenuation. The proposed method achieved Dice similarity coefficients of 0.937+-0.035 when compared to expert manual segmentation on a representative dataset consisting of 115 three-dimensional lymph node images obtained from colorectal cancer patients.

cs.CV↗

arXiv2017

A General Model for Robust Tensor Factorization with Unknown Noise

Because of the limitations of matrix factorization, such as losing spatial structure information, the concept of low-rank tensor factorization (LRTF) has been applied for the recovery of a low dimensional subspace from high dimensional visual data. The low-rank tensor recovery is generally achieved by minimizing the loss function between the observed data and the factorization representation. The loss function is designed in various forms under different noise distribution assumptions, like $L_1$ norm for Laplacian distribution and $L_2$ norm for Gaussian distribution. However, they often fail to tackle the real data which are corrupted by the noise with unknown distribution. In this paper, we propose a generalized weighted low-rank tensor factorization method (GWLRTF) integrated with the idea of noise modelling. This procedure treats the target data as high-order tensor directly and models the noise by a Mixture of Gaussians, which is called MoG GWLRTF. The parameters in the model are estimated under the EM framework and through a new developed algorithm of weighted low-rank tensor factorization. We provide two versions of the algorithm with different tensor factorization operations, i.e., CP factorization and Tucker factorization. Extensive experiments indicate the respective advantages of this two versions in different applications and also demonstrate the effectiveness of MoG GWLRTF compared with other competing methods.

cs.CV↗

arXiv2017

Strain-induced Isostructural and Magnetic Phase Transitions in Monolayer MoN$_2$

The change of bonding status, typically occurring only in chemical processes, could dramatically alter the material properties. Here, we show that a tunable breaking and forming of a diatomic bond can be achieved through physical means, i.e., by a moderate biaxial strain, in the newly discovered MoN$_2$ two-dimensional (2D) material. Based on first-principles calculations, we predict that as the lattice parameter is increased under strain, there exists an isostructural phase transition at which the N-N distance has a sudden drop, corresponding to the transition from a N-N nonbonding state to a N-N single bond state. Remarkably, the bonding change also induces a magnetic phase transition, during which the magnetic moments transfer from the N(2p) sublattice to the Mo(4d) sublattice, meanwhile the type of magnetic coupling is changed from ferromagnetic to anti-ferromagnetic. We provide a physical picture for understanding these striking effects. The discovery is not only of great scientific interest in exploring unusual phase transitions in low-dimensional systems, but it also reveals the great potential of the 2D MoN$_2$ material in the nanoscale mechanical, electronic, and spintronic applications.

cond-mat.mtrl-sci↗

arXiv2017

The Bernardi process and torsor structures on spanning trees

Let G be a ribbon graph, i.e., a connected finite graph G together with a cyclic ordering of the edges around each vertex. By adapting a construction due to O. Bernardi, we associate to any pair (v,e) consisting of a vertex v and an edge e adjacent to v a bijection between spanning trees of G and elements of the set Pic^g(G) of degree g divisor classes on G, where g is the genus of G. Using the natural action of the Picard group Pic^0(G) on Pic^g(G), we show that the Bernardi bijection gives rise to a simply transitive action β_v of Pic^0(G) on the set of spanning trees which does not depend on the choice of e. A plane graph has a natural ribbon structure (coming from the counterclockwise orientation of the plane), and in this case we show that β_v is independent of v as well. Thus for plane graphs, the set of spanning trees is naturally a torsor for the Picard group. Conversely, we show that if β_v is independent of v then G together with its ribbon structure is planar. We also show that the natural action of Pic^0(G) on spanning trees of a plane graph is compatible with planar duality. These findings are formally quite similar to results of Holroyd et al. and Chan-Church-Grochow, who used rotor-routing to construct an action r_v of Pic^0(G) on the spanning trees of a ribbon graph G, which they show is independent of v if and only if G is planar. It is therefore natural to ask how the two constructions are related. We prove that β_v = r_v for all vertices v of G when G is a planar ribbon graph, i.e. the two torsor structures (Bernardi and rotor-routing) on the set of spanning trees coincide. In particular, it follows that the rotor-routing torsor is compatible with planar duality. We conjecture that for every non-planar ribbon graph G, there exists a vertex v with β_v \neq r_v.

math.CO↗

arXiv2017

An Experimental Study of Deep Convolutional Features For Iris Recognition

Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4\%, which outperforms the previous best result.

cs.CV↗