arXiv subjects

Qian Zhao

Publications and source records attributed to Qian Zhao.

156 records · Page 9Linked to original sources

arXiv2017

Denoising Hyperspectral Image with Non-i.i.d. Noise Structure

Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoG) noise assumption, which is finely in accordance with the noise characteristics possessed by a natural HSI and thus is capable of adapting various noise shapes encountered in real applications. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose a NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is designed to infer the posterior of the proposed model. All involved parameters can be recursively updated in closed-form. Compared with the current techniques, the proposed method performs more robust beyond the state-of-the-arts, as substantiated by our experiments implemented on synthetic and real noisy HSIs.

cs.CV↗

arXiv2016

What Objective Does Self-paced Learning Indeed Optimize?

Self-paced learning (SPL) is a recently raised methodology designed through simulating the learning principle of humans/animals. A variety of SPL realization schemes have been designed for different computer vision and pattern recognition tasks, and empirically substantiated to be effective in these applications. However, the investigation on its theoretical insight is still a blank. To this issue, this study attempts to provide some new theoretical understanding under the SPL scheme. Specifically, we prove that the solving strategy on SPL accords with a majorization minimization algorithm implemented on a latent objective function. Furthermore, we find that the loss function contained in this latent objective has a similar configuration with non-convex regularized penalty (NSPR) known in statistics and machine learning. Such connection inspires us discovering more intrinsic relationship between SPL regimes and NSPR forms, like SCAD, LOG and EXP. The robustness insight under SPL can then be finely explained. We also analyze the capability of SPL on its easy loss prior embedding property, and provide an insightful interpretation to the effectiveness mechanism under previous SPL variations. Besides, we design a group-partial-order loss prior, which is especially useful to weakly labeled large-scale data processing tasks. Through applying SPL with this loss prior to the FCVID dataset, which is currently one of the biggest manually annotated video dataset, our method achieves state-of-the-art performance beyond previous methods, which further helps supports the proposed theoretical arguments.

cs.LG↗

arXiv2016

Controllable light capsules employing modified Bessel-Gauss beams

We report, in theory and experiment, on a novel class of controlled light capsules with nearly perfect darkness, directly employing intrinsic properties of modified Bessel-Gauss beams. These beams are able to naturally create three-dimensional bottle-shaped region during propagation as long as the parameters are properly chosen. Remarkably, the optical bottle can be controlled to demonstrate various geometries through tuning the beam parameters, thereby leading to an adjustable light capsule. We provide a detailed insight into the theoretical origin and characteristics of the light capsule derived from modified Bessel-Gauss beams. Moreover, a binary digital micromirror device (DMD) based scheme is first employed to shape the bottle beams by precise amplitude and phase manipulation. Further, we demonstrate their ability for optical trapping of core-shell magnetic microparticles, which play a particular role in biomedical research, with holographic optical tweezers. Therefore, our observations provide a new route for generating and controlling bottle beams and will widen the potentials for micromanipulation of absorbing particles, aerosols or even individual atoms.

physics.optics↗

arXiv2016

Low-rank Matrix Factorization under General Mixture Noise Distributions

Many computer vision problems can be posed as learning a low-dimensional subspace from high dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problems using L1-norm and L2-norm losses, which mainly deal with Laplacian and Gaussian noises, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as Mixture of Exponential Power (MoEP) distributions and proposes a penalized MoEP (PMoEP) model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture is adapted from a series of preliminary super- or sub-Gaussian candidates. Moreover, by facilitating the local continuity of noise components, we embed Markov random field into the PMoEP model and further propose the advanced PMoEP-MRF model. An Expectation Maximization (EM) algorithm and a variational EM (VEM) algorithm are also designed to infer the parameters involved in the proposed PMoEP and the PMoEP-MRF model, respectively. The superseniority of our methods is demonstrated by extensive experiments on synthetic data, face modeling, hyperspectral image restoration and background subtraction.

cs.CV↗

arXiv2015

Tunable mid-infrared coherent perfect absorption in a graphene meta-surface

We exploited graphene nanoribbons based meta-surface to realize coherent perfect absorption (CPA) in the mid-infrared regime. It was shown that quasi-CPA frequencies, at which CPA can be demonstrated with proper phase modulations, exist for the graphene meta-surface with strong resonant behaviors. The CPA can be tuned substantially by merging the geometric design of the meta-surface and the electrical tunability of graphene. Furthermore, we found that the graphene nanoribbon meta-surface based CPA is realizable with experimental graphene data. The findings of CPA with graphene meta-surface can be generalized for potential applications in optical detections and signal processing with two-dimensional optoelectronic materials.

physics.optics↗

arXiv2014

Isospin coupling-channel decomposition of nuclear symmetry energy in covariant density functional theory

The isospin coupling-channel decomposition of the potential energy density functional is carried out within the covariant density functional theory, and their isospin and density dependence in particular the influence on the symmetry energy is studied. It is found that both isospin-singlet and isospin-triplet components of the potential energy play the dominant role in deciding the symmetry energy, especially when the Fock diagram is introduced. The results illustrate a quite different mechanism to the origin of the symmetry energy from the microscopic Brueckner-Hartree-Fock theory, and demonstrate the importance of the Fork diagram in the CDF theory, especially from the isoscalar mesons, in the isospin properties of the in-medium nuclear force at high density.

nucl-th↗

arXiv2014

Tunable terahertz coherent perfect absorption in a monolayer graphene

Coherent perfect absorber (CPA) was proposed as the time-reversed counterpart to laser: a resonator containing lossy medium instead of gain medium can absorb the coherent optical fields completely. Here, we exploit a monolayer graphene to realize the CPA in a non-resonant manner. It is found that quasi-CPA point exists in the terahertz regime for suspending monolayer graphene, and the CPA can be implemented with the assistant of proper phase modulation among two incident beams at the quasi-CPA frequencies. The graphene based CPA is found of broadband angular selectivity: CPA point splits into two frequency bands for the orthogonal $s$ and $p$ polarizations at oblique incidence, and the two bands cover a wide frequency range starting from zero frequency. Furthermore, the coherent absorption can be tuned substantially by varying the gate-controlled Fermi energy. The findings of CPA with non-resonant graphene sheet can be generalized for potential applications in terahertz/infrared detections and signal processing with two-dimensional optoelectronic materials.

physics.optics↗

arXiv2013

On the Dividend Strategies with Non-Exponential Discounting

In this paper, we study the dividend strategies for a shareholder with non-constant discount rate in a diffusion risk model. We assume that the dividends can only be paid at a bounded rate and restrict ourselves to the Markov strategies. This is a time inconsistent control problem. The extended HJB equation is given and the verification theorem is proved for a general discount function. Considering the pseudo-exponential discount functions (Type I and Type II), we get the equilibrium dividend strategies and the equilibrium value functions by solving the extended HJB equations.

q-fin.PM↗

arXiv2013

Dictionary learning under global sparsity constraint

A new method is proposed in this paper to learn overcomplete dictionary from training data samples. Differing from the current methods that enforce similar sparsity constraint on each of the input samples, the proposed method attempts to impose global sparsity constraint on the entire data set. This enables the proposed method to fittingly assign the atoms of the dictionary to represent various samples and optimally adapt to the complicated structures underlying the entire data set. By virtue of the sparse coding and sparse PCA techniques, a simple algorithm is designed for the implementation of the method. The efficiency and the convergence of the proposed algorithm are also theoretically analyzed. Based on the experimental results implemented on a series of signal and image data sets, it is apparent that our method performs better than the current dictionary learning methods in original dictionary recovering, input data reconstructing, and salient data structure revealing.

cs.DS↗

arXiv2013

Mean-Variance Asset-Liability Management with State-Dependent Risk Aversion

In this paper, we consider the asset-liability management under the mean-variance criterion. The financial market consists of a risk-free bond and a stock whose price process is modeled by a geometric Brownian motion. The liability of the investor is uncontrollable and is modeled by another geometric Brownian motion. We consider a specific state-dependent risk aversion which depends on a power function of the liability. By solving a flow of FBSDEs with bivariate state process, we obtain the equilibrium strategy among all the open-loop controls for this time-inconsistent control problem. It shows that the equilibrium strategy is a feedback control of the liability.

q-fin.RM↗

arXiv2012

A recursive divide-and-conquer approach for sparse principal component analysis

In this paper, a new method is proposed for sparse PCA based on the recursive divide-and-conquer methodology. The main idea is to separate the original sparse PCA problem into a series of much simpler sub-problems, each having a closed-form solution. By recursively solving these sub-problems in an analytical way, an efficient algorithm is constructed to solve the sparse PCA problem. The algorithm only involves simple computations and is thus easy to implement. The proposed method can also be very easily extended to other sparse PCA problems with certain constraints, such as the nonnegative sparse PCA problem. Furthermore, we have shown that the proposed algorithm converges to a stationary point of the problem, and its computational complexity is approximately linear in both data size and dimensionality. The effectiveness of the proposed method is substantiated by extensive experiments implemented on a series of synthetic and real data in both reconstruction-error-minimization and data-variance-maximization viewpoints.

cs.CV↗

arXiv2011

The mechanism of the polarization dependence of the optical transmission in subwavelength metal hole arrays

We investigate the mechanism of extraordinary optical transmission in subwave-length metal hole arrays. Experimental results for the arrays consisting of square or rectangle holes are well explained about the dependence of transmission strength on the polarization direction of the incident light. This polarization dependence occurs in each single-hole. For a hole array, there is in addition an interplay between the adjacent holes which is caused by the transverse magnetic field of surface plasmon polariton on the metal film surfaces. Based on the detailed study of a single-hole and two-hole structures, a simple method to calculate the total tranmissivity of hole arrays is proposed.

physics.optics↗