arXiv subjects

Yan Yang

Publications and source records attributed to Yan Yang.

173 records · Page 10Linked to original sources

arXiv2017

A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects

Value-added models have been widely used to assess the contributions of individual teachers and schools to students' academic growth based on longitudinal student achievement outcomes. There is concern, however, that ignoring the presence of missing values, which are common in longitudinal studies, can bias teachers' value-added scores. In this article, a flexible correlated random effects model is developed that jointly models the student responses and the student missing data indicators. Both the student responses and the missing data mechanism depend on latent teacher effects as well as latent student effects, and the correlation between the sets of random effects adjusts teachers' value-added scores for informative missing data. The methods are illustrated with data from calculus classes at a large public university and with data from an elementary school district.

stat.ME↗

arXiv2017

Qubit compatible superconducting interconnects

We present a fabrication process for fully superconducting interconnects compatible with superconducting qubit technology. These interconnects allow for the 3D integration of quantum circuits without introducing lossy amorphous dielectrics. They are composed of indium bumps several microns tall separated from an aluminum base layer by titanium nitride which serves as a diffusion barrier. We measure the whole structure to be superconducting (transition temperature of 1.1$\,$K), limited by the aluminum. These interconnects have an average critical current of 26.8$\,$mA, and mechanical shear and thermal cycle testing indicate that these devices are mechanically robust. Our process provides a method that reliably yields superconducting interconnects suitable for use with superconducting qubits.

quant-ph↗

arXiv2017

A note on the 4-girth-thickness of K_{n,n,n}

The $4$-girth-thickness $θ(4,G)$ of a graph $G$ is the minimum number of planar subgraphs of girth at least four whose union is $G$. In this paper, we obtain that the 4-girth-thickness of complete tripartite graph $K_{n,n,n}$ is $\big\lceil\frac{n+1}{2}\big\rceil$ except for $θ(4,K_{1,1,1})=2$. And we also show that the $4$-girth-thickness of the complete graph $K_{10}$ is three which disprove the conjecture $θ(4,K_{10})=4$ posed by Rubio-Montiel (Ars Math Contemp 14(2) (2018) 319).

math.CO↗

arXiv2017

Exploring the Statistics of Magnetic Reconnection X-points in Kinetic Particle-in-Cell (PIC) Turbulence

Magnetic reconnection is a ubiquitous phenomenon in turbulent plasmas. It is an important part of the turbulent dynamics and heating of space and astrophysical plasmas. We examine the statistics of magnetic reconnection using a quantitative local analysis of the magnetic vector potential, previously used in magnetohydrodynamics simulations, and now generalized to fully kinetic PIC simulations. Different ways of reducing the particle noise for analysis purposes including multiple smoothing techniques are explored. We find that a Fourier filter applied at the Debye scale is an optimal choice for analyzing PIC data. Finlay, we find a broader distribution of normalized reconnection rates compared to the MHD limit with rates as large as 0.5 but with an average of approximately 0.1.

physics.space-ph↗

arXiv2017

ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI

Compressive sensing (CS) is an effective approach for fast Magnetic Resonance Imaging (MRI). It aims at reconstructing MR images from a small number of under-sampled data in k-space, and accelerating the data acquisition in MRI. To improve the current MRI system in reconstruction accuracy and speed, in this paper, we propose two novel deep architectures, dubbed ADMM-Nets in basic and generalized versions. ADMM-Nets are defined over data flow graphs, which are derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model. They take the sampled k-space data as inputs and output reconstructed MR images. Moreover, we extend our network to cope with complex-valued MR images. In the training phase, all parameters of the nets, e.g., transforms, shrinkage functions, etc., are discriminatively trained end-to-end. In the testing phase, they have computational overhead similar to ADMM algorithm but use optimized parameters learned from the data for CS-based reconstruction task. We investigate different configurations in network structures and conduct extensive experiments on MR image reconstruction under different sampling rates. Due to the combination of the advantages in model-based approach and deep learning approach, the ADMM-Nets achieve state-of-the-art reconstruction accuracies with fast computational speed.

cs.CV↗

arXiv2016

Edge Detection Methods Based on Differential Phase Congruency of Monogenic Image

Edge Detection Methods Based on Differential Phase Congruency of Monogenic Image Abstract: Edge detection has been widely used in medical image processing and automatic diagnosis. Some novel edge detection algorithms,based on the monogenic scale-space, are proposed by detecting points of local extrema in local amplitude, the local attenuation and modified differential phase congruency methods. The monogenic scale-space is obtained from a known image by Poisson and conjugate Poisson filtering. In mathematics, it is the Hardy space in the upper half-space. The boundary value of the monogenic scale-space representation is a monogenic image. In the monogenic scale-space, the definitions involving scale, such as local amplitude,local attenuation, local phase angle, local phase vector and local frequency (phase derivatives) are proposed. Using Clifford analysis, the relations between the local attenuation and the local phase vector are obtained. These study will be improved the understanding of image analysis in higher dimensional spaces. Experimental results are shown by using some typical images.

math.CA↗

arXiv2016

Uncertainty Principle for Measurable Sets and Signal Recovery in Quaternion Domains

The classical uncertainty principle of harmonic analysis states that a nontrivial function and its Fourier transform cannot both be sharply localized. It plays an important role in signal processing and physics. This paper generalizes the uncertainty principle for measurable sets from complex domain to hypercomplex domain using quaternion algebras, associated with the Quaternion Fourier transform. The performance is then evaluated in signal recovery problems where there is an interplay of missing and time-limiting data.

cs.IT↗

arXiv2015

Sharp inequalities of homogeneous expansions for quasi-convex mappings of type B and almost starlike mappings of order alpha

In this paper, we first obtain several sharp inequalities of homogeneous expansion for both the subclass of all normalized biholomorphic quasi-convex mappings of type B and order alpha and the subclass of all normalized biholomorphic almost starlike mappings of order alpha defined on the unit ball $B$ of a complex Banach space X. Then, with these sharp inequalities, we derive the sharp estimates of the third and fourth homogeneous expansions for the above mappings defined on the unit polydisk $D^n$ in C(n).

math.CV↗

arXiv2014

Multi-Shot Person Re-Identification via Relational Stein Divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

cs.CV↗

arXiv2012

The thickness of cartesian product $K_n \Box P_m$

The thickness $θ(G)$ of a graph $G$ is the minimum number of planar spanning subgraphs into which the graph $G$ can be decomposed. It is a topological invariant of a graph, which was defined by W.T. Tutte in 1963 and also has important applications to VLSI design. But comparing with other topological invariants, e.g. genus and crossing number, results about thickness of graphs are few. The only types of graphs whose thicknesses have been obtained are complete graphs, complete bipartite graphs and hypercubes. In this paper, by operations on graphs, the thickness of the cartesian product $K_n \Box P_m$, $n,m \geq 2$ are obtained.

math.CO↗

arXiv2012

The thickness of amalgamations of graphs

The thickness $θ(G)$ of a graph $G$ is the minimum number of planar spanning subgraphs into which the graph $G$ can be decomposed. As a topological invariant of a graph, it is a measurement of the closeness to planarity of a graph, and it also has important applications to VLSI design. In this paper, the thickness of graphs that are obtained by vertex-amalgamation and bar-amalgamation of any two graphs whose thicknesses are known are obtained, respectively. And the lower and upper bounds for the thickness of graphs that are obtained by edge-amalgamation and 2-vertex-amalgamation of any two graphs whose thicknesses are known are also derived, respectively.

math.CO↗