arXiv subjects

Rong Chen

Publications and source records attributed to Rong Chen.

125 records · Page 7Linked to original sources

arXiv2017

Factor Models for Matrix-Valued High-Dimensional Time Series

In finance, economics and many other fields, observations in a matrix form are often observed over time. For example, many economic indicators are obtained in different countries over time. Various financial characteristics of many companies are reported over time. Although it is natural to turn a matrix observation into a long vector then use standard vector time series models or factor analysis, it is often the case that the columns and rows of a matrix represent different sets of information that are closely interrelated in a very structural way. We propose a novel factor model that maintains and utilizes the matrix structure to achieve greater dimensional reduction as well as finding clearer and more interpretable factor structures. Estimation procedure and its theoretical properties are investigated and demonstrated with simulated and real examples.

stat.ME↗

arXiv2016

Scalable Algorithms for Generating and Analyzing Structural Brain Networks with a Varying Number of Nodes

Diffusion Magnetic Resonance Imaging (MRI) exploits the anisotropic diffusion of water molecules in the brain to enable the estimation of the brain's anatomical fiber tracts at a relatively high resolution. In particular, tractographic methods can be used to generate whole-brain anatomical connectivity matrix where each element provides an estimate of the connectivity strength between the corresponding voxels. Structural brain networks are built using the connectivity information and a predefined brain parcellation, where the nodes of the network represent the brain regions and the edge weights capture the connectivity strengths between the corresponding brain regions. This paper introduces a number of novel scalable methods to generate and analyze structural brain networks with a varying number of nodes. In particular, we introduce a new parallel algorithm to quickly generate large scale connectivity-based parcellations for which voxels in a region possess highly similar connectivity patterns to the rest of the regions. We show that the corresponding regional structural consistency is always superior to randomly generated parcellations over a wide range of parcellation sizes. Corresponding brain networks with a varying number of nodes are analyzed using standard graph-theorectic measures, as well as, new measures derived from spectral graph theory. Our results indicate increasingly more statistical power of brain networks with larger numbers of nodes and the relatively unique shape of the spectral profile of large brain networks relative to other well-known networks.

cs.CE↗

arXiv2016

The excluded minors for the class of matroids that are graphic or bicircular lift

Bicircular lift matroids are a class of matroids defined on the edge set of a graph. For a given graph $G$, the circuits of its bicircular lift matroid $L(G)$ are the edge sets of those subgraphs of $G$ that contain at least two cycles, and are minimal with respect to this property. For each cycle $C$ of $G$, since $L(G)/C$ is graphic and most graphic matroids are not bicircular lift, the class of bicircular lift matroids is not minor-closed. In this paper, we prove that the class of matroids that are graphic or bicircular lift has a finite list of excluded minors.

math.CO↗

arXiv2016

Representations of bicircular lift matroids

Bicircular lift matroids are a class of matroids defined on the edge set of a graph. For a given graph $G$, the circuits of its bicircular lift matroid are the edge sets of those subgraphs of $G$ that contain at least two cycles, and are minimal with respect to this property. The main result of this paper is a characterization of when two graphs give rise to the same bicircular lift matoid, which answers a question proposed by Irene Pivotto. In particular, aside from some appropriately defined "small" graphs, two graphs have the same bicircular lift matroid if and only if they are $2$-isomorphic in the sense of Whitney.

math.CO↗

arXiv2016

Well-posedness and persistence properties for two-component higher order Camassa-Holm systems with fractional inertia operator

In this paper, we study the Cauchy problem for a two-component higher order Camassa-Holm systems with fractional inertia operator $A=(1-\partial_x^2)^r,r\geq1$, which was proposed by Escher and Lyons. By the transport equation theory and Littlewood-Paley decomposition, we obtain that the local well-posedness of solutions for the system in nonhomogeneous Besov spaces $B^s_{p,q}\times B^{s-2r+1}_{p,q}$ with $1\leq p,q \leq +\infty$ and the Besov index $s>\max\left\{2r +\frac{1}{p},2r+1-\frac{1}{p}\right\}$. Moreover, we construct the local well-posedness in the critical Besov space $B^{2r+\frac{1}{2}}_{2,1}\times B^{\frac{3}{2}}_{2,1}$. On the other hand, the propagation behaviour of compactly supported solutions is examined, namely whether solutions which are initially compactly supported will retain this property throughout their time of evolution. Moreover, we also establish the persistence properties of the solutions to the two-component Camassa-Holm equation with $r=1$ in weighted $L_ϕ^p:=L^p(\mathbb{R},ϕ^p(x)dx)$ spaces for a large class of moderate weights.

math.AP↗

arXiv2016

On recognising frame and lifted-graphic matroids

We prove that there is no polynomial $p(\cdot)$ with the property that a matroid $M$ can be determined to be either a lifted-graphic or frame matroid using at most $p(|M|)$ rank evaluations. This resolves two conjectures of Geelen, Gerards and Whittle (Quasi-graphic matroids, arXiv:1512.03005v1).

math.CO↗

arXiv2014

Graphical representations of graphic frame matroids

A frame matroid M is graphic if there is a graph G with cycle matroid isomorphic to M. In general, if there is one such graph, there will be many. Zaslavsky has shown that frame matroids are precisely those having a representation as a biased graph; this class includes graphic matroids, bicircular matroids, and Dowling geometries. Whitney characterized which graphs have isomorphic cycle matroids, and Matthews characterised which graphs have isomorphic graphic bicircular matroids. In this paper, we give a characterization of which biased graphs give rise to isomorphic graphic frame matroids.

math.CO↗

arXiv2014

Intertwining connectivity in matroids

Let $M$ be a matroid and let $Q$, $R$, $S$ and $T$ be subsets of the ground set such that the smallest separation that separates $Q$ from $R$ has order $k$ and the smallest separation that separates $S$ from $T$ has order $l$. We prove that if $E(M)-(Q\cup R\cup S\cup T)$ is sufficiently large, then there is an element $e$ of $M$ such that, in one of $M\backslash e$ or $M/e$, both connectivities are preserved.

math.CO↗

arXiv2013

Extracting the nuclear symmetry potential and energy from neutron-nucleus scattering data

According to the Hugenholtz-Van Hove theorem, the symmetry energy and its density slope can be decomposed uniquely in terms of the single-nucleon potential in asymmetric nuclear matter which, at normal density, can be constrained by the nucleon optical model potential extracted from analyzing the nucleon-nucleus scattering data. To more accurately extract information on the symmetry energy and its density slope at normal density from neutron-nucleus scattering data, going beyond the well-known Lane potential, we include consistently the second order terms in isospin asymmetry in both the optical model potential and the symmetry energy decomposition. We find that the strength of the second-order symmetry potential $U_{sym,2}$ in asymmetric nuclear matter is significant compared to the first-order one U_{sym,1}$, especially at high nucleon momentum. While the $U_{sym,1}$ at normal density decreases with nucleon momentum, the $U_{sym,2}$ is found to have the opposite behavior. Moreover, we discuss effects of the $U_{sym,1}$ and $U_{sym,2}$ on determining the density dependence of the symmetry energy, and we find that the available neutron-nucleus scattering data favor a softer density dependence of the symmetry energy at normal density with a negligible contribution from the $U_{sym,2}$.

nucl-th↗

arXiv2013

The Induced Electric Field Distribution in Solar Atmosphere

A method of calculating induced electric field is presented in this paper. Induced electric field in solar atmosphere is derived by the time variation of magnetic field when the charged particle accumulation is neglected. In order to get the spatial distribution of magnetic field, several extrapolation methods are introduced. With observational data from Helioseismic and Magnetic Imager (HMI) aboard the NASA's Solar Dynamics Observatory (SDO) on May 20th, 2010, we extrapolate the magnetic field to the upper atmosphere from the photosphere. By calculating the time variation of magnetic field, we can get the induced electric field. The derived induced electric field can reach a value of 100 V/cm and the average electric field has a maximum point at the layer of 360 km above the photosphere. The Monte Carlo statistics method is used to compute the triple integration of induced electric field.

astro-ph.SR↗

arXiv2013

Lookahead Strategies for Sequential Monte Carlo

Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state. By providing theoretical justification of several existing algorithms and introducing several new ones, we study systematically how to construct efficient SMC algorithms to take advantage of the "future" information without creating a substantially high computational burden. The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.

stat.ME↗

arXiv2012

Single-nucleon potential decomposition of the nuclear symmetry energy

The nuclear symmetry energy $E_{sym}(ρ)$ and its density slope $L(ρ)$ can be decomposed analytically in terms of the single-nucleon potential in isospin asymmetric nuclear matter. Using three popular nuclear effective interaction models which have been extensively used in nuclear structure and reaction studies, namely, the isospin and momentum dependent MDI interaction model, the Skyrme Hartree-Fock approach and the Gogny Hartree-Fock approach, we analyze the contribution of different terms in the single-nucleon potential to the $E_{sym}(ρ)$ and $L(ρ)$. Our results show that the observed different density behaviors of $E_{sym}(ρ)$ for different interactions are essentially due to the variation of the symmetry potential $U_{sym,1}(ρ,k)$. Furthermore, we find that the contribution of the second-order symmetry potential $U_{sym,2}(ρ,k)$ to the $L(ρ)$ generally cannot be neglected. Moreover, our results demonstrate that the magnitude of the $U_{sym,2}(ρ,k)$ is generally comparable with that of $U_{sym,1}(ρ,k)$, indicating the second-order symmetry potential $U_{sym,2}(ρ,k)$ may have significant corrections to the widely used Lane approximation to the single-nucleon potential in extremely neutron(proton)-rich nuclear matter.

nucl-th↗

arXiv2006

Empirical Potential Function for Simplified Protein Models: Combining Contact and Local Sequence-Structure Descriptors

An effective potential function is critical for protein structure prediction and folding simulation. Simplified protein models such as those requiring only $C_α$ or backbone atoms are attractive because they enable efficient search of the conformational space. We show residue specific reduced discrete state models can represent the backbone conformations of proteins with small RMSD values. However, no potential functions exist that are designed for such simplified protein models. In this study, we develop optimal potential functions by combining contact interaction descriptors and local sequence-structure descriptors. The form of the potential function is a weighted linear sum of all descriptors, and the optimal weight coefficients are obtained through optimization using both native and decoy structures. The performance of the potential function in test of discriminating native protein structures from decoys is evaluated using several benchmark decoy sets. Our potential function requiring only backbone atoms or $C_α$ atoms have comparable or better performance than several residue-based potential functions that require additional coordinates of side chain centers or coordinates of all side chain atoms. By reducing the residue alphabets down to size 5 for local structure-sequence relationship, the performance of the potential function can be further improved. Our results also suggest that local sequence-structure correlation may play important role in reducing the entropic cost of protein folding.

q-bio.BM↗

arXiv2004

Potential function of simplified protein models for discriminating native proteins from decoys: Combining contact interaction and local sequence-dependent geometry

An effective potential function is critical for protein structure prediction and folding simulation. For simplified models of proteins where coordinates of only $C_α$ atoms need to be specified, an accurate potential function is important. Such a simplified model is essential for efficient search of conformational space. In this work, we present a formulation of potential function for simplified representations of protein structures. It is based on the combination of descriptors derived from residue-residue contact and sequence-dependent local geometry. The optimal weight coefficients for contact and local geometry is obtained through optimization by maximizing margins among native and decoy structures. The latter are generated by chain growth and by gapless threading. The performance of the potential function in blind test of discriminating native protein structures from decoys is evaluated using several benchmark decoy sets. This potential function have comparable or better performance than several residue-based potential functions that require in addition coordinates of side chain centers or coordinates of all side chain atoms.

q-bio.BM↗

arXiv2004

Importance of chirality and reduced flexibility of protein side chains: A study with square and tetrahedral lattice models

In simple models side chains are often represented implicitly (e.g., by spin-states) or simplified as one atom. We study side chain effects using square lattice and tetrahedral lattice models, with explicitly side chains of two atoms. We distinguish effects due to chirality and effects due to side chain flexibilities, since residues in proteins are L-residues, and their side chains adopt different rotameric states. Short chains are enumerated exhaustively. For long chains, we sample effectively rare events (eg, compact conformations) and obtain complete pictures of ensemble properties of these models at all compactness region. We find that both chirality and reduced side chain flexibility lower the folding entropy significantly for globally compact conformations, suggesting that they are important properties of residues to ensure fast folding and stable native structure. This corresponds well with our finding that natural amino acid residues have reduced effective flexibility, as evidenced by analysis of rotamer libraries and side chain rotatable bonds. We further develop a method calculating the exact side-chain entropy for a given back bone structure. We show that simple rotamer counting often underestimates side chain entropy significantly, and side chain entropy does not always correlate well with main chain packing. Among compact backbones with maximum side chain entropy, helical structures emerges as the dominating configurations. Our results suggest that side chain entropy may be an important factor contributing to the formation of alpha helices for compact conformations.

q-bio.BM↗

arXiv2003

Origin of Scaling Behavior of Protein Packing Density: A Sequential Monte Carlo Study of Compact Long Chain Polymers

Single domain proteins are thought to be tightly packed. The introduction of voids by mutations is often regarded as destabilizing. In this study we show that packing density for single domain proteins decreases with chain length. We find that the radius of gyration provides poor description of protein packing but the alpha contact number we introduce here characterize proteins well. We further demonstrate that protein-like scaling relationship between packing density and chain length is observed in off-lattice self-avoiding walks. A key problem in studying compact chain polymer is the attrition problem: It is difficult to generate independent samples of compact long self-avoiding walks. We develop an algorithm based on the framework of sequential Monte Carlo and succeed in generating populations of compact long chain off-lattice polymers up to length $N=2,000$. Results based on analysis of these chain polymers suggest that maintaining high packing density is only characteristic of short chain proteins. We found that the scaling behavior of packing density with chain length of proteins is a generic feature of random polymers satisfying loose constraint in compactness. We conclude that proteins are not optimized by evolution to eliminate packing voids.

cond-mat.soft↗

arXiv2002

Statistical Geometry of Packing Defects of Lattice Chain Polymer from Enumeration and Sequential Monte Carlo Method

Voids exist in proteins as packing defects and are often associated with protein functions. We study the statistical geometry of voids in two-dimensional lattice chain polymers. We define voids as topological features and develop a simple algorithm for their detection. For short chains, void geometry is examined by enumerating all conformations. For long chains, the space of void geometry is explored using sequential Monte Carlo importance sampling and resampling techniques. We characterize the relationship of geometric properties of voids with chain length, including probability of void formation, expected number of voids, void size, and wall size of voids. We formalize the concept of packing density for lattice polymers, and further study the relationship between packing density and compactness, two parameters frequently used to describe protein packing. We find that both fully extended and maximally compact polymers have the highest packing density, but polymers with intermediate compactness have low packing density. To study the conformational entropic effects of void formation, we characterize the conformation reduction factor of void formation and found that there are strong end-effect. Voids are more likely to form at the chain end. The critical exponent of end-effect is twice as large as that of self-contacting loop formation when existence of voids is not required. We also briefly discuss the sequential Monte Carlo sampling and resampling techniques used in this study.

physics.bio-ph↗