arXiv subjects

Jian Zhang

Publications and source records attributed to Jian Zhang.

At least 739 records · Page 41Linked to original sources

arXiv2013

Electronic and spin transport properties of zigzag graphene nanoribbon mediated by metal adatom: A study by QUAMBO-NEGF approach

Structural and electronic properties, including deformation, magnetic moment, Mulliken population, bond order as well as electronic transport properties, of zigzag graphene nanoribbon (ZGNR) with Co adatom on hollow site are investigated by quasi-atomic minimal basis orbits (QUAMBOs), a first-principles tight binding (TB) scheme based on density functional theory (DFT), combined with non-equilibrium Green's function. As for electronic transport, below Fermi level the transmission is strongly suppressed and spin-dependent as a result of magnetism by Co adatom adsorption, while above Fermi level the transmission is slightly distorted and spin-independent. Due to local environment dependence of QUAMBOs-TB parameters, we construct QUAMBOs-TB parameters of ZGNR lead and ZGNR with Co adatom on the hollow center site by divide-and-conquer approach, and well reproduce the electronic transmission behavior. Our QUAMBO-NEGF method is a new and promising way for electronic transport in large-scale system.

cond-mat.mes-hall↗

arXiv2012

Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization

Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edges. However, the use of TV is not able to recover the fine details and textures, and often suffers from undesirable staircase artifact. To reduce these effects, this letter presents an improved TV based image CS recovery algorithm by introducing a new nonlocal regularization constraint into CS optimization problem. The nonlocal regularization is built on the well known nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. Furthermore, an efficient augmented Lagrangian based algorithm is developed to solve the above combined TV and nonlocal regularization constrained problem. Experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art TV based algorithm in both PSNR and visual perception.

cs.CV↗

arXiv2012

High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization

In this paper, we propose a novel image interpolation algorithm, which is formulated via combining both the local autoregressive (AR) model and the nonlocal adaptive 3-D sparse model as regularized constraints under the regularization framework. Estimating the high-resolution image by the local AR regularization is different from these conventional AR models, which weighted calculates the interpolation coefficients without considering the rough structural similarity between the low-resolution (LR) and high-resolution (HR) images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize the interpolated HR image, which provides a way to modify these pixels with the problem of numerical stability caused by AR model. In addition, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem iteratively. Experiment results demonstrate that the proposed algorithm achieves significant performance improvements over the traditional algorithms in terms of both objective quality and visual perception

cs.MM↗

arXiv2012

Approximating dynamics of a singularly perturbed stochastic wave equation with a random dynamical boundary condition

This work is concerned with a singularly perturbed stochastic nonlinear wave equation with a random dynamical boundary condition. A splitting skill is used to derive the approximating equation of the system in the sense of probability distribution, when the singular perturbation parameter is sufficiently small. The approximating equation is a stochastic parabolic equation when the power exponent of singular perturbation parameter is in $[1/2, 1)$, but a deterministic hyperbolic (wave) equation when the power exponent is in $(1, +\infty)$.

math.AP↗

arXiv2012

Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.

cs.MM↗

arXiv2012

Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation

Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from the prior model trained by a set of high- and low-resolution image patches. In this paper, HF to be estimated is considered as a combination of two components: main high-frequency (MHF) and residual high-frequency (RHF), and we propose a novel image super-resolution method via dual-dictionary learning and sparse representation, which consists of the main dictionary learning and the residual dictionary learning, to recover MHF and RHF respectively. Extensive experimental results on test images validate that by employing the proposed two-layer progressive scheme, more image details can be recovered and much better results can be achieved than the state-of-the-art algorithms in terms of both PSNR and visual perception.

cs.CV↗

arXiv2012

Quantum discord and its asymptotic behaviors in a time-dependent XY spin chain

We study the dynamics and the asymptotic behaviors of quantum discord in a one-dimensional XY model coupled through time-dependent nearest-neighbor interactions and in the presence of a time-dependent magnetic field. We find that the time evolution of the nearest-neighbor quantum discord in this system shows non-ergodic behaviors but is asymptotic to its steady value at the long-time limit. The zero-temperature asymptotic behaviors of quantum discord is only determined by the ratio between the coupling parameter and magnetic field, whereas the finite-temperature asymptotic behaviors determined by both of them. These asymptotic behaviors are sensitive not only to the initial values of the coupling parameter and magnetic field, but also to the final values. It is interesting to note that quantum discords are more robust than entanglement against the effect of temperature. We also find particular parameter regimes, where the nearest-neighbor quantum discord is enhanced significantly.

quant-ph↗

arXiv2012

Statistical Translation, Heat Kernels and Expected Distances

High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from high variance and performs poorly in general. We explore novel connections between statistical translation, heat kernels on manifolds and graphs, and expected distances. These connections provide a new framework for unsupervised metric learning for text documents. Experiments indicate that the resulting distances are generally superior to their more standard counterparts.

cs.LG↗

arXiv2012

Effective dynamics of stochastic wave equation with a random dynamical boundary condition

This work is devoted to the effective macroscopic dynamics of a weakly damped stochastic nonlinear wave equation with a random dynamical boundary condition. The white noises are taken into account not only in the model equation defined on a domain perforated with small holes, but also in the dynamical boundary condition on the boundaries of the small holes. An effective homogenized, macroscopic model is derived in the sense of probability distribution, which is a new stochastic wave equation on a unified domain, without small holes, with a usual static boundary condition.

math.AP↗

arXiv2012

The Entire Quantile Path of a Risk-Agnostic SVM Classifier

A quantile binary classifier uses the rule: Classify x as +1 if P(Y = 1|X = x) >= t, and as -1 otherwise, for a fixed quantile parameter t {[0, 1]. It has been shown that Support Vector Machines (SVMs) in the limit are quantile classifiers with t = 1/2 . In this paper, we show that by using asymmetric cost of misclassification SVMs can be appropriately extended to recover, in the limit, the quantile binary classifier for any t. We then present a principled algorithm to solve the extended SVM classifier for all values of t simultaneously. This has two implications: First, one can recover the entire conditional distribution P(Y = 1|X = x) = t for t {[0, 1]. Second, we can build a risk-agnostic SVM classifier where the cost of misclassification need not be known apriori. Preliminary numerical experiments show the effectiveness of the proposed algorithm.

cs.LG↗

arXiv2011

Developments of a 2D Position Sensitive Neutron Detector

Chinese Spallation Neutron Source (CSNS), one project of the 12th five-year-plan scheme of China, is under construction in Guangdong province. Three neutron spectrometers will be installed at the first phase of the project, where two-dimensional position sensitive thermal neutron detectors are required. Before the construction of the neutron detector, a prototype of two-dimensional 200 mmx200 mm Multi-wire Proportional Chamber (MWPC) with the flowing gas of Ar/CO2 (90/10) has been constructed and tested with the 55Fe X-Ray using part of the electronics in 2009, which showed a good performance. Following the test in 2009, the neutron detector has been constructed with the complete electronics and filled with the 6atm.3He + 2.5atm.C3H8 gas mixture in 2010. The neutron detector has been primarily tested with an Am/Be source. In this paper, some new developments of the neutron detector including the design of the high pressure chamber, the optimization of the gas purifying system and the gas filling process will be reported. The results and discussion are also presented in this paper.

physics.ins-det↗

arXiv2011

Study of measuring methods on spatial resolution of a GEM imaging detector

In this paper, limitations of the common method measuring intrinsic spatial resolution of the GEM imaging detector are presented. Through theoretical analysis and experimental verification, we have improved the common method to avoid these limitations. Using these improved methods, more precise measurement of intrinsic spatial resolution are obtained.

physics.ins-det↗

arXiv2011

The Use of Scientific Data: A Content Analysis

Nowadays, science has been coming into a new paradigm, called data-intensive science. While current studies of the new phenomenon focused on building up infrastructure for this new paradigm, yet a few studies concern users of scientific data, particularly their usage practices in the newly emerging paradigm, even though the importance of understanding users' work flow and practices has been summoned. This study endeavors to improve our understanding of users' data usage behavior through a content analysis of publications in a frequently cited new paradigm-related project, Sloan Digital Sky Survey (SDSS). We found that (1) nearly half studies used one data source only. A few studies exploited three or more data sources; (2) the number of objects that were analyzed in SDSS publications is in all scales from one digit to millions; (3) different paper types may affect the data usage patterns; (4) Users are not only consumers of scientific data. They are producers too; (5) studies that can use multiple large scale data sources are relative rare. Issues of data provenance, trust, and usability may prevent researchers from doing this kind of research.

astro-ph.IM↗

arXiv2010

Degeneracy of Many-body Quantum States in an Optical Lattice with a Uniform Magnetic Field

We prove a theorem that shows the degeneracy of many-body states depends on total particle number and flux filling ratio, for particles in a periodic lattice and under a uniform magnetic field. Non-interacting fermions and weakly interacting bosons are given as two examples. For the later case, this phenomena can also be understood in terms of destructive quantum interferences of multiple symmetry related tunneling paths between classical energy minima, which is reminiscent of the spin-parity effect discovered in magnetic molecular cluster. We also show that the quantum ground state of a mesoscopic number of bosons in this system is not a simple mean-field state but a fragmented state even for very weak interactions.

cond-mat.quant-gas↗

arXiv2010

Face Detection with Effective Feature Extraction

There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.

cs.CV↗

arXiv2010

Geometric shape of invariant manifolds for a class of stochastic partial differential equations

Invariant manifolds play an important role in the study of the qualitative dynamical behaviors for nonlinear stochastic partial differential equations. However, the geometric shape of these manifolds is largely unclear. The purpose of the present paper is to try to describe the geometric shape of invariant manifolds for a class of stochastic partial differential equations with multiplicative white noises. The local geometric shape of invariant manifolds is approximated, which holds with significant likelihood. Furthermore, the result is compared with that for the corresponding deterministic partial differential equations.

math.AP↗

arXiv2010

Incremental Training of a Detector Using Online Sparse Eigen-decomposition

The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently, offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In addition, once learned, an offline detector can not make use of newly arriving data. To alleviate these drawbacks, online learning has been adopted with the following objectives: (1) the technique should be computationally and storage efficient; (2) the updated classifier must maintain its high classification accuracy. In this paper, we propose an effective and efficient framework for learning an adaptive online greedy sparse linear discriminant analysis (GSLDA) model. Unlike many existing online boosting detectors, which usually apply exponential or logistic loss, our online algorithm makes use of LDA's learning criterion that not only aims to maximize the class-separation criterion but also incorporates the asymmetrical property of training data distributions. We provide a better alternative for online boosting algorithms in the context of training a visual object detector. We demonstrate the robustness and efficiency of our methods on handwriting digit and face data sets. Our results confirm that object detection tasks benefit significantly when trained in an online manner.

cs.CV↗

arXiv2010

Making Sense of the Evolution of a Scientific Domain: A Visual Analytic Study of the Sloan Digital Sky Survey Research

We introduce a new visual analytic approach to the study of scientific discoveries and knowledge diffusion. Our approach enhances contemporary co-citation network analysis by enabling analysts to identify co-citation clusters of cited references intuitively, synthesize thematic contexts in which these clusters are cited, and trace how research focus evolves over time. The new approach integrates and streamlines a few previously isolated techniques such as spectral clustering and feature selection algorithms. The integrative procedure is expected to empower and strengthen analytical and sense making capabilities of scientists, learners, and researchers to understand the dynamics of the evolution of scientific domains in a wide range of scientific fields, science studies, and science policy evaluation and planning. We demonstrate the potential of our approach through a visual analysis of the evolution of astronomical research associated with the Sloan Digital Sky Survey (SDSS) using bibliographic data between 1994 and 2008. In addition, we also demonstrate that the approach can be consistently applied to a set of heterogeneous data sources such as e-prints on arXiv, publications on ADS, and NSF awards related to the same topic of SDSS.

cs.GL↗